A ship thrust distribution method based on improved fish eagle algorithm

Abstract

This invention discloses a ship thrust allocation method based on an improved heron algorithm, comprising the following steps: establishing a three-degree-of-freedom mathematical model of the ship and an interference model of the mathematical model; obtaining the power, torque, and thrust models of the ship's propeller based on the three-degree-of-freedom mathematical model and the interference model, combined with the hydrodynamic characteristics analysis of the propeller; establishing a target thrust allocation function for the ship based on the propeller's energy consumption, wear, and command error; optimizing the propeller parameters using a global artificial fish swarm algorithm to obtain the relatively optimal parameter solution domain that minimizes the target thrust allocation function; and further optimizing the relatively optimal parameter solution domain using the heron algorithm to obtain the final relatively optimal solution for the propeller parameters. This method solves the problem that the selection of existing thrust allocation methods significantly affects the ship's control accuracy and cannot effectively reduce propeller wear and energy consumption while ensuring control accuracy.

Description

Technical Field

[0001] This invention relates to the field of ship thrust distribution, and more particularly to a ship thrust distribution method based on an improved fish-heron algorithm. Background Technology

[0002] my country possesses nearly three million square kilometers of sea area, making it a major coastal nation. Therefore, it places great emphasis on the development and utilization of the ocean, including offshore oil and gas development, mineral extraction, and fishing. However, developing marine resources is significantly more challenging than developing land-based resources, which necessitates the use of positioning systems. Currently, positioning systems are mainly divided into two types: dynamic positioning systems and moored positioning systems. Traditional moored positioning systems are simple in structure, reliable, and do not require propellers to resist external environmental interference, thus reducing energy consumption and offering good economic efficiency. However, when seabed space is insufficient to accommodate more anchors, and when operating depths continuously increase, dynamic positioning systems become the only option. Furthermore, dynamic positioning systems offer very high positioning accuracy, effectively ensuring the safety of the entire operation and personnel.

[0003] A dynamic positioning system is a system that uses the ship's own propulsion system and the action of a controller to automatically maintain its position and heading. Three equipment levels are defined: one where positioning failure may occur under a single fault condition; another where positioning failure will not occur with a single fault in an active component; and a third where positioning failure will not occur with a single fault in any component or system (including failures caused by fires or floods in the isolation compartment).

[0004] The most crucial component of a dynamic positioning system is the thrust distribution system, which is also a vital part of the dynamic positioning control system. Thrust distribution is responsible for rationally distributing the three-degree-of-freedom control forces output by the controller to the ship's actuators using a specific distribution strategy. However, the choice of existing thrust distribution methods significantly impacts the ship's control accuracy, failing to effectively reduce propeller wear and energy consumption while ensuring control precision. Summary of the Invention

[0005] This invention provides a ship thrust allocation method based on an improved fish-heron algorithm to overcome the problem that the selection of existing thrust allocation methods greatly affects the control accuracy of ships and cannot effectively reduce the wear and energy consumption of propellers while ensuring control accuracy.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] A ship thrust allocation method based on an improved fish-heron algorithm includes the following steps:

[0008] Step S1: Establish a three-degree-of-freedom mathematical model of the ship and an interference model of the mathematical model;

[0009] The three-degree-of-freedom mathematical model includes ship kinematics and ship dynamics models;

[0010] The interference models include sea breeze models, ocean wave models, and ocean current models;

[0011] Step S2: Based on the three-degree-of-freedom mathematical model and the interference model of the mathematical model, and combined with the hydrodynamic characteristics analysis of the propeller, obtain the power, torque and thrust model of the ship propeller;

[0012] Step S3: Obtain the energy consumption of the propeller based on the power, torque, and thrust model of the ship's propeller;

[0013] A target thrust allocation function for the ship is established based on the energy consumption of the propeller, the wear of the propeller, and the command error.

[0014] Step S4: Optimize the thruster parameters based on the global artificial fish swarm algorithm to obtain several relatively optimal parameter solution domains that minimize the ship thrust target allocation function;

[0015] The thruster parameters include the thruster's azimuth angle and thruster's thrust;

[0016] Step S5: The relative optimal parameter solution domain is optimized again using the Egret algorithm to obtain the final relative optimal solution of the thruster parameters;

[0017] The final relative optimal solution is used as the final azimuth angle and thrust of the thruster.

[0018] Furthermore, the three-degree-of-freedom mathematical model and the interference model of the mathematical model in step S1 are specifically as follows:

[0019] The ship kinematics model is

[0020]

[0021] The attitude and position function of the ship in the northeast coordinate system; Θ = [φ θ ψ] T ∈S 3 S represents the ship's attitude angle in the northeast coordinate system; 3 Represents a three-dimensional Euclidean torus; J(η) represents a function of the ship's attitude and position; v represents the ship's velocity; The rotation matrix represents the ship's attitude angles; 0 3X3 A zero matrix with three rows and three columns; T Θ (Θ) Angular velocity rotation matrix;

[0022] The ship dynamics model is

[0023]

[0024] In the formula: M represents the inertia matrix of the hydrodynamic system, which is related to the linear and angular acceleration of the ship; C(v) represents the hydrodynamic Coriolis centripetal force matrix, which is related to the ship's added mass; D(υ) represents the hydrodynamic damping coefficient matrix, which includes linear damping term D and nonlinear damping term Dv. n (υ); g(η) is the restoring force caused by the ship's buoyancy; g0 is the restoring force provided by the ship's ballast; ω0 is the environmental disturbance; τ is the theoretical thrust of the propeller.

[0025] Furthermore, in step S2, the power, torque, and thrust efficiency model of the ship's propulsion system are obtained, specifically as follows:

[0026] Step S2.1: The thrust and torque of the propeller can be defined as the following functional form;

[0027]

[0028] In the formula: T represents the thruster thrust; Q represents the thruster torque; D p Indicates the propeller diameter; n represents the rotational speed; V A ρ represents the velocity; θ represents the density of seawater; p Indicates a fixed parameter; x p Indicates time-varying parameters;

[0029] Step S2.2: Convert the functional form into specific expressions for the thruster thrust and torque as follows:

[0030]

[0031] In the formula: K T With K Q These represent the thrust coefficient and torque coefficient, respectively.

[0032] Step S2.3: Based on the torque expression of the thruster, the formula for calculating the power consumption of the thruster can be obtained as follows:

[0033]

[0034] Step S2.4: From the calculation formulas in steps S2.2 and S2.3, the relationship between the thruster's power consumption and thrust can be obtained as follows:

[0035]

[0036] Furthermore, in step S4, a target thrust allocation function is established based on the propeller's energy consumption, propeller wear, and command error. The calculation formula is as follows:

[0037]

[0038]

[0039] s=τ-B(α)T

[0040] In the formula: MinJ(α,T) represents the energy function of the ship's thrust target allocation function; T represents the thruster thrust; α represents the azimuth angle of each thruster in the current allocation cycle; W represents the weighting coefficient; p i C represents the power consumption of the i-th thruster; i T represents the correction factor; i P represents the thrust of the i-th thruster; total The sum of power consumption of all thrusters represents the total power consumption; s is the slack variable matrix; s T The transpose of the slack variable matrix; Q0 and Ω represent positive definite diagonal matrices; α0 represents the azimuth angle of each thruster in the previous cycle; (α-α0) T B(α) represents the transpose of the difference between the azimuth angles of each thruster in the current allocation cycle and the azimuth angles of each thruster in the previous cycle; B(α) represents the thruster configuration matrix; τ is the theoretical thrust of the thruster. Indicates the power variation limit; This represents the actual rate of change of thruster power; Let be the desired rate of change of thruster power; K represents the weighting coefficient matrix.

[0041] Furthermore, in step S4, several relatively optimal parameter solution domains that minimize the target allocation function of ship thrust are obtained, specifically:

[0042] Step S4.1: Based on the global artificial fish swarm algorithm, the fish swarm is randomly initialized in the relatively optimal parameter solution domain to form several initial artificial fish swarms. Each artificial fish in the initial artificial fish swarm represents a set of solutions of the ship thrust target allocation function with respect to the thrust of the propeller and the azimuth angle of the propeller.

[0043] The initialization parameters of the global artificial fish swarm algorithm include the number of artificial fish N, the initial state X of each artificial fish, the number of iterations IT, the number of attempts try_number, the crowding factor δ, the preset values ​​for jumping behavior ε and β, and the threshold value for swallowing behavior.

[0044] Step S4.2: Calculate the corresponding ship thrust target allocation function value based on the current state of each artificial fish, initialize the artificial fish state corresponding to the minimum value of the ship thrust target allocation function as the optimal artificial fish state, record the maximum movement step and visual value of each artificial fish, and output the current optimal solution X. bestWrite it to the bulletin board;

[0045] Step S4.3: Determine if the preset iteration count IT is met. If so, output the current optimal solution X on the bulletin board. best If not, proceed to steps S4.4 to S4.6;

[0046] Step S4.4: Initialize the maximum step size Step and visual value of each artificial fish in the artificial fish swarm;

[0047] Step S4.5: Evaluate the behavior of each artificial fish based on foraging behavior, grouping behavior, tail-chasing behavior, and random behavior, and select the optimal behavior to execute and update its state.

[0048] Step S4.6: For the current optimal solution X best Perform annealing operation, check if the temperature is less than the temperature threshold T_value. If it is, output the solution. Otherwise, lower the temperature and continue annealing until the temperature is less than the temperature threshold T_value.

[0049] Step S4.7: Obtain the current optimal solution X from each artificial fish swarm. best Let the solution domain be a relatively optimal parameter.

[0050] Furthermore, the foraging behavior described in step S4.5 specifically involves setting the current state of the i-th artificial fish to X. i The i-th artificial fish randomly selects a new state X within its field of vision (Visual). j And X j =X i +Visual·Rand(); if J j <J i That is, state X j Superior to X i The current state X of the artificial fish i To state X j Move one step;

[0051]

[0052] If J j ≥J i Then reselect state X. j If the fish cannot move after a preset number of attempts (try_number), it will randomly move forward one step. The formula for this random behavior is:

[0053]

[0054] In the formula: J j Represents state X jThe thruster power; J i Represents state X i The thruster power; This represents the state of the artificial fish at time t; This represents the state of the artificial fish at time t+1; Step represents the step size; Rand() represents a random number in the interval [0,1].

[0055] The clustering behavior specifically refers to: in the current state X of the i-th artificial fish i Within the field of view, search for the number n individuals of a group of fish. f X, the center of the fish school c ;

[0056] In the formula: X s "s" refers to the general state of an artificial fish; "s" represents multiple states of an artificial fish.

[0057] If condition J is satisfied c n f / J i If ≤δ, then the current state X of the artificial fish is... i X towards the center of the fish gathering c Move one step: Otherwise, foraging behavior will be performed;

[0058] In the formula: δ represents the crowding factor; J c Indicates the power consumption of the thruster at the center position; This represents the state of the artificial fish at time t; This represents the state of the artificial fish at time t+1; Step represents the step size; Rand() represents a random number in the interval [0,1].

[0059] The tail-chasing behavior specifically refers to: in the state X of the i-th artificial fish i The domain search for the optimal artificial fish partner X b J b This represents the minimum power consumption of the thruster;

[0060] If condition J is satisfied b n f / J i If ≤δ, then the current state X of the artificial fish is... i Will towards X b Move one step;

[0061] In the formula: This represents the state of the artificial fish at time t; Represents the state of the artificial fish at time t+1; Step represents the step size; Rand() represents a random number in the interval [0,1]; Jj Represents state X j The thruster power; δ represents the congestion factor; n f This indicates the number of individuals in the group; otherwise, foraging behavior is performed.

[0062] Furthermore, in step S5, the relatively optimal parameter solution domain is further optimized using the Egret algorithm, specifically as follows:

[0063] Step S5.1: Use the obtained relatively optimal parameters in the solution domain to determine the thruster thrust and thruster azimuth angle as the thrust allocation decision variables of the mathematical model of the ship thrust target allocation function;

[0064] The decision variables for thrust allocation are encoded as individuals in a flock of egrets, resulting in an encoded mathematical model; and the flock of egrets includes several egret squads composed of individuals from the flock.

[0065] Step S5.2: Construct an egret flock optimization algorithm focusing on egret flock waiting strategies, aggressive strategies, and the number of iterations;

[0066] The calculation formula for the waiting strategy is as follows:

[0067] X a,i =X i +exp(-t / (0.1·tmax))·0.1·hop·g i

[0068] In the formula: X a,i Indicates the position of the individual egret after iteration; X i represents the position of the egret individual after the last iteration; exp represents the exponential function; t represents the iteration number; tmax represents the maximum value of the iteration number; hop represents the feasible region of the independent variable; g i Represented as gradient;

[0069] The aggressive strategy includes random walks and encirclement mechanisms, and the formula for updating the position of egret individuals during random walks is as follows:

[0070] X b,i =X i +tan(r b,i )·hop / (1+t)

[0071] In the formula: X b,i Indicates the position of the individual egret after iteration; X i This indicates the position of the egret individual after the last iteration; r b,i represents a random number; t represents the number of iterations; hop represents the feasible region of the independent variable;

[0072] The formula for updating the individual egret position in the encirclement mechanism is as follows:

[0073] D h =X ibest -X i

[0074] D g =X gbest -X i

[0075] X c,i =(1-r i -r g )·X i +r h ·D h +r g ·D g

[0076] In the formula: D h D represents the difference between the optimal value of the egret squad and the current position of the individual egret; g This represents the difference between the optimal value of the egret flock and the current position of an individual egret; r i r g r h X represents a random number between [0,1]; ibest X represents the optimal value for the egret team; gbest This represents the optimal value for the egret flock;

[0077] Step S5.3: Update the optimal solutions for the waiting strategy and the aggressive strategy, wherein the optimal solution is the position of the egret individual that minimizes the value of the encoded mathematical model;

[0078] Step S5.4: Compare the fitness of the updated egret individual position with the fitness of the egret individual position in the previous iteration;

[0079] If the fitness of the updated egret individual position is better than the fitness of the egret individual position in the previous iteration, then the updated egret individual position is adopted; otherwise, the update is abandoned; until the maximum number of iterations is reached.

[0080] Step S5.5: Decode the egret individuals' positions obtained after the maximum number of iterations to obtain the thrust allocation decision variables of the mathematical model of the ship thrust target allocation function;

[0081] The ship's thrust is allocated by acquiring the thrust allocation decision variables.

[0082] Beneficial Effects: This invention provides a ship thrust allocation method based on an improved fish-heron algorithm. It constructs a three-degree-of-freedom ship dynamics and kinematics model, simplifying the computation process. A power constraint term is added to the thrust allocation objective function, resulting in more accurate calculations. A hybrid algorithm, combining simulated annealing artificial fish swarm and egret swarm algorithms, is employed to address the issues of the artificial fish swarm algorithm easily getting trapped in local optima and exhibiting slow convergence. The improved artificial fish swarm and egret swarm optimization algorithm is used to allocate ship thrust, optimizing the distribution of thrust and azimuth output from the ship's propellers using a specific strategy. This results in a reasonable allocation to the ship's actuators, effectively reducing propeller wear and energy consumption while maintaining control accuracy. Attached Figure Description

[0083] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0084] Figure 1 This is a flowchart of the ship thrust allocation method based on the improved fish-heron algorithm of the present invention. Detailed Implementation

[0085] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0086] This embodiment provides a ship thrust allocation method based on an improved fish-heron algorithm, such as... Figure 1 As shown, it includes the following steps:

[0087] Step S1: Establish a three-degree-of-freedom mathematical model of the ship and an interference model of the mathematical model; the three-degree-of-freedom mathematical model includes ship kinematics and ship dynamics models; the interference models include sea wind model, sea wave model and ocean current model;

[0088] Step S2: Based on the three-degree-of-freedom mathematical model and the interference model of the mathematical model, and combined with the hydrodynamic characteristics analysis of the propeller, obtain the power, torque and thrust model of the ship propeller;

[0089] Step S3: Obtain the energy consumption of the propeller based on the power, torque, and thrust model of the ship's propeller;

[0090] A target thrust allocation function for the ship is established based on the energy consumption of the propeller, the wear of the propeller, and the command error.

[0091] Step S4: Optimize the thruster parameters based on the global artificial fish swarm algorithm to obtain several relatively optimal parameter solution domains that minimize the target thrust allocation function of the ship; the thruster parameters include the azimuth angle of the thruster and the thrust of the thruster;

[0092] Step S5: The relative optimal parameter solution domain is optimized again using the Egret algorithm to obtain the final relative optimal solution of the thruster parameters; the final relative optimal solution is used as the final azimuth angle and thrust of the thruster.

[0093] By constructing a three-degree-of-freedom ship dynamics and kinematics model, the process calculations are simplified. A power constraint term is added to the thrust allocation objective function to improve the accuracy of the calculation results. A hybrid algorithm, combining simulated annealing artificial fish swarm and egret flock algorithms, is employed. First, the global convergence of the artificial fish swarm algorithm is used to quickly find a satisfactory solution domain. Then, the egret flock algorithm is used for rapid local search. This hybrid algorithm not only has a fast local search speed but also ensures global convergence performance. It also avoids the problems of the artificial fish swarm algorithm alone, which is prone to getting stuck in local optima and has a slow convergence speed. The improved artificial fish swarm and egret flock algorithm is used to achieve the ship's thrust allocation. The thrust and azimuth angle output by the ship's propellers are optimized and allocated to the ship's actuators in a reasonable manner, thereby effectively reducing propeller wear and energy consumption while ensuring control accuracy.

[0094] In a specific embodiment, the three-degree-of-freedom mathematical model and the interference model of the mathematical model in step S1 are specifically as follows:

[0095] The ship kinematics model is

[0096]

[0097] η represents the attitude and position function of the ship in the northeast coordinate system; Let θ be the first derivative of η; Θ = [φ θ ψ] T ∈S 3 S represents the ship's attitude angle in the northeast coordinate system; 3 Represents a three-dimensional Euclidean torus; J(η) represents a function of the ship's attitude and position; v represents the ship's velocity; The rotation matrix represents the ship's attitude angles; 0 3X3 A zero matrix with three rows and three columns; TΘ (Θ) Angular velocity rotation matrix;

[0098] Step S1.1: In the northeast coordinate system, during actual operation, the changes in the heel angle φ and pitch angle θ of a dynamically positioned vessel are very small. Therefore, we can assume that φ≈θ≈0. Based on this assumption, substituting φ=θ=0 into the ship's kinematic model yields:

[0099]

[0100]

[0101] In the formula: R(ψ)=R z,ψ P(ψ) represents the matrix with respect to the bow roll angle; I 3X3 R represents a 3x3 identity matrix; R(ψ) represents the matrix about the turning angle; R z,ψ A matrix representing the pitch and bow angle;

[0102] Step S1.2: Transform the ship's attitude and position functions in the NE coordinate system into the variable η in the hull coordinate system. p ,but:

[0103] η p =P T (ψ)η

[0104] In the formula: P T (ψ) represents the transformation matrix with respect to the bow roll angle;

[0105] Step S1.3: Combining the equations from step S1.2 and step S1.1, we can obtain:

[0106]

[0107] Among them, for In this part, it is about The function, This represents the bow angle vector; however, for dynamically positioned ships, most operations occur at low speeds. Therefore, this term is approximately 0; and since P T (ψ)P(ψ)=I 6×6 Therefore, P T (ψ)P(ψ)υ=υ, then η p ψ represents the position vector; ψ represents the turning angle. P represents T The first derivative of (ψ); I 6X6 Represents a six-row, six-column identity matrix;

[0108] Step S1.4: Linearize the nonlinear Coriolis centripetal force, damping force, restoring force, buoyancy, and moment at υ≈0, φ=θ=0, so that the ship dynamics model can be expressed as...

[0109]

[0110] In the formula: M represents the inertia matrix of the hydrodynamic system, which is related to the linear and angular acceleration of the ship, and M A It is part of M; C represents the hydrodynamic Coriolis centripetal force matrix and is related to the ship's added mass; C A It is part of C; D(υ) represents the hydrodynamic damping coefficient matrix containing linear damping term D and nonlinear damping term D. n (υ); g(η) is the restoring force caused by the ship's buoyancy; g0 is the restoring force provided by the ship's ballast; ω0 is the environmental disturbance; τ is the theoretical thrust of the propeller;

[0111] The nonlinear six-degree-of-freedom kinematic and dynamic models of a ship can then be transformed into the following forms:

[0112]

[0113]

[0114] The above can be further summarized into a state-space description as follows:

[0115]

[0116] y = Cx

[0117] In the formula: And T represents transpose; u = τ; y = η p M -1 The inverse matrix of the hydrodynamic system's inertia matrix; C = [I 6×6 0 6×6 ]; denoted by , x represents the position and velocity matrix; y represents the position and velocity matrix; A, B, E, u, and C all represent matrix symbols; G represents the system inertia matrix.

[0118] Finally, η is expressed by the matrix P(ψ) with respect to the bow angle. p Transform η into the northeast coordinate system;

[0119] In reality, we only focus on the three degrees of freedom motion of a dynamically positioned ship in the horizontal plane, including pitching, swaying, and bow turning, and we only consider the low-speed case, then υ = [uvr] T η=[ne ψ]T P(ψ)=R(ψ)=R z,ψ u represents the longitudinal velocity of the hull; v represents the transverse velocity of the hull; r represents the angular velocity of the hull; n represents the transverse position of the hull; e represents the longitudinal position of the hull; ψ represents the bow angle. For convenience, we assume that the mass distribution of the ship is uniform and symmetrical about the xz plane in the northeast coordinate system, and that the origin of the hull coordinate system is chosen on the centerline. Under these conditions, the formulas we obtain have higher accuracy, and the center of the added hull mass coincides with the center of gravity. Therefore, we can simplify the inertial matrix M and the hydrodynamic damping matrix D of the hydrodynamic system:

[0120] m represents the ship's mass; x G Indicates the position of the center of gravity in the x-axis coordinate system of the ship; I zz Represents the moment of inertia about the z-axis; Represents the hydrodynamic derivative; X u Y v ,、N r Y r N v Y represents the viscous force or torque exerted by the fluid per unit velocity; r and N v These represent hydrodynamic parameters, and their absolute values ​​are usually very small.

[0121] Step S1.5: Establish a sea breeze model

[0122] Besides causing waves, sea breezes also directly affect the hull and ship's structures, causing the ship to list and yaw. In fact, wind speed at sea level is not constant; it can be calculated by adding the average wind speed and the instantaneous wind speed, using the following formula:

[0123]

[0124] In the formula: U(t) represents the wind speed over the sea surface; This represents the average wind speed over the sea surface; Indicates the instantaneous wind speed over the sea surface;

[0125] Step S1.6: The formula for calculating the average wind speed is as follows:

[0126]

[0127] In the formula: U0 is the average wind speed measured by the meteorological station at a height of z0 above sea level; This represents the average wind speed measured by a meteorological station at a height z above sea level; z0 is generally 10 meters; k is a constant coefficient, usually taken as 0.4;

[0128] Step S1.7: Combining the two calculation formulas from steps S1.5 and S1.6, the average crosswind speed can be obtained. for

[0129]

[0130]

[0131]

[0132]

[0133] In the formula: ω represents the average wind speed over the sea surface; ω represents the angular frequency; ω p Indicates a fixed value, and The instantaneous wind speed represents the crosswind; α represents the angle between the wind direction and the horizontal plane; m represents the metric unit. The lateral wind power density is represented by z; the altitude is represented by t; and ε or ε0 represents time. i Represent a random variable between [0, 2π].

[0134] Furthermore, transforming the above formula into discrete form, we get:

[0135]

[0136] In the formula: N represents an infinitely large integer; ω i Δω represents the angular frequency when N is i. i t represents the change in angular frequency; i This represents the time when N is i;

[0137] Therefore, the absolute wind speed U T The expression for (t) is:

[0138]

[0139] Then, based on existing known technology, the wind force and torque on a ship can be expressed as follows:

[0140]

[0141] In the formula: X wind Y represents the force and moment exerted by crosswinds on a ship; wind N represents the force and moment exerted by vertical wind on a ship. wind This represents the force and moment exerted by longitudinal wind on the ship; C X Indicates the lateral wind force and the wind moment coefficient; ψ r Indicates the turning angle; ρ a U represents air density;r Indicates relative wind speed; A T C represents the projected area above the waterline of the ship; Y Indicates the vertical wind force and wind moment coefficient; A L C represents the side projection area above the waterline of the ship. N Indicates longitudinal wind force and wind moment coefficient;

[0142] Step S1.8: Establish a wave model

[0143] Ocean wave interference is the most complex environmental interference, which has a significant impact on dynamically positioned ships. It can interfere with the ship's course and track. At the same time, its frequency is low and close to the ship's own frequency, which can easily cause resonance. Therefore, the modeling is mainly based on second-order wave forces.

[0144] For regular waves, the formulas for calculating second-order wave forces and moments can be derived from the following conclusions:

[0145]

[0146] In the formula: X wave Y represents the force and moment exerted by transverse waves on the ship; wave N represents the force and moment exerted by vertical waves on the ship. wave Represents the force and moment exerted by longitudinal waves on the ship; ρ represents the density of seawater; L represents the overall length of the ship; ζ D λ represents the average wave amplitude; χ represents the wavelength; C represents the encounter angle between the ship and the wave; λ represents the average wave amplitude; χ represents the wavelength; χ represents the encounter angle between the ship and the wave; χ represents the average wave amplitude. XD (λ), C YD (λ), C ND (λ) represents the wave force and moment coefficients in the transverse, vertical, and longitudinal directions, respectively; where...

[0147] The wave density ζ(t) is:

[0148]

[0149] In the formula: S ζζ (ω i ) represents the ITTC single-parameter spectrum; Δω represents the change in angular frequency; m represents an integer variable;

[0150] Step S1.9: The formulas for calculating the second-order wave force and moment can then be obtained as follows:

[0151]

[0152] Step S1.10: Establish an ocean current model

[0153] There are two main ways to calculate the effect of ocean currents on dynamically positioned vessels: the first is to convert the vessel's motion parameters in still water into relative parameters between the vessel and the ocean current; the second is to use the force and moment coefficient of the ocean current to calculate the force and moment exerted by the ocean current on the vessel, as shown in the following expressions:

[0154]

[0155] In the formula: X current Y represents the force and moment exerted by the transverse ocean current on the ship's hull. current N represents the force and moment exerted by the vertical ocean current on the ship's hull. current This represents the force and moment exerted by the longitudinal ocean current on the ship's hull; A fw V represents the projected area of ​​a vessel below the waterline; c This indicates the wind force at altitude c; C x (β) represents the force and moment coefficient of the ocean current along the lateral direction; C y (β) represents the longitudinal force and moment coefficient of the ocean current; A sw C represents the side projected area of ​​the vessel below the waterline; n (β) represents the force and moment coefficient of the ocean current along the heading.

[0156] In a specific embodiment, step S2 obtains the power, torque, and thrust efficiency model of the ship propeller, specifically as follows:

[0157] Step S2.1: The thrust and torque of the propeller can be defined as the following functional form;

[0158]

[0159] In the formula: T represents the thruster thrust; Q represents the thruster torque; D p Indicates the propeller diameter; n represents the rotational speed; V A ρ represents the velocity; θ represents the density of seawater; p Indicates a fixed parameter; x p Indicates time-varying parameters;

[0160] Step S2.2: Convert the functional form into specific expressions for the thruster thrust and torque as follows:

[0161]

[0162] In the formula: K T With K Q These represent the thrust coefficient and torque coefficient, respectively.

[0163] Step S2.3: Based on the torque expression of the thruster, the formula for calculating the power consumption of the thruster can be obtained as follows:

[0164]

[0165] Step S2.4: From the calculation formulas in steps S2.2 and S2.3, the relationship between the thruster's power consumption and thrust can be obtained as follows:

[0166]

[0167] Furthermore, in step S4, a target thrust allocation function for the ship is established based on the energy consumption of the propeller, the wear of the propeller, and the command error. Specifically,

[0168] Step S4.1: Based on the three-degree-of-freedom mathematical model and its interference model, and combined with the hydrodynamic characteristics of the propeller, the thrust distribution and energy function calculation formulas of the propeller are obtained as follows:

[0169] Min J(α,T)=WP total +s T Qs+(α-α0) T Ω(α-α0)

[0170] The general form of the thrust distribution objective function usually includes three parts: the total energy consumption of the thruster, the distribution command error, and the wear of the thruster.

[0171] The formula for calculating the total energy consumption of the thruster is as follows:

[0172]

[0173] Where: P total P represents the total energy consumption of all thrusters. i The energy consumed by the i-th thruster is approximately proportional to the third and second power of the thruster's thrust, i.e.

[0174] P i =C i T i 3 / 2

[0175] Therefore, we can express the first term as:

[0176]

[0177] Where: W is a weighting coefficient, reflecting the weight of the power consumption term in the entire optimization objective; in the actual optimization process, the proportion of the energy consumption term in the entire optimization objective can be adjusted by changing the size of the weight;

[0178] Step S4.2: The relationship between the theoretical thrust of the thruster and T, representing the actual thrust of the thruster, is as follows:

[0179] s=τ-B(α)T

[0180] The above formula is the allocation error penalty term, which is the second term in the total energy consumption term. Its purpose is to penalize the error between the three-degree-of-freedom control command and the actual thrust and torque of the thruster. The existence of this error can increase the feasible region of the solution, making the thrust allocation solvable. However, it is necessary to ensure that the error is as small as possible to achieve the expected control effect. Here, the slack variable s is used to represent this error. The weight matrix Q is a positive definite diagonal matrix, representing the proportion of the second term in the objective function. Its value should be as large as possible to ensure that s≈0.

[0181] Step S4.3: The third part of the thrust distribution and energy function calculation formula for the propeller is the penalty term for the change in the propeller azimuth angle; where α represents the azimuth angle of each propeller in this distribution cycle; α0 represents the azimuth angle of each propeller in the previous cycle; Ω is also a diagonal positive definite matrix, representing the weight of this term; on the one hand, due to its own physical limitations, the change in the azimuth angle of a real ship's propeller cannot be very large in a short period of time. If the change in the azimuth angle command between two distribution cycles is too large, it will cause the propeller to fail to meet the requirements of the control command, thereby reducing the positioning accuracy; on the other hand, if the propeller maintains maximum rotation, it will aggravate the wear of the propeller; therefore, whether from the perspective of improving positioning accuracy or reducing propeller wear, the change in the azimuth angle should be limited.

[0182] However, to maintain power system stability, the general thrust allocation model was improved by incorporating a power variation constraint term into the objective function, resulting in a thrust allocation objective function based on power management. The calculation formula is as follows:

[0183]

[0184] In the formula: MinJ(α,T) represents the energy function of the ship's thrust target allocation function; T represents the actual thrust of the propeller; α represents the azimuth angle of each propeller in the current allocation cycle; W represents the weighting coefficient; p i C represents the power consumption of the i-th thruster; i T represents the correction factor; i P represents the thrust of the i-th thruster; total The sum of power consumption of all thrusters represents the total power consumption; s is the slack variable matrix; s T The transpose of the slack variable matrix; Q0 and Ω represent positive definite diagonal matrices; α0 represents the azimuth angle of each thruster in the previous cycle; (α-α0) T B(α) represents the transpose of the difference between the azimuth angles of each thruster in the current allocation cycle and the azimuth angles of each thruster in the previous cycle; B(α) represents the thruster configuration matrix; τ is the theoretical thrust of the thruster. Indicates the power variation limit; This represents the actual rate of change of thruster power; Let be the desired rate of change of thruster power; K represents the weighting coefficient matrix.

[0185] In a specific embodiment, step S4 involves obtaining several relatively optimal parameter solution domains that minimize the target allocation function of ship thrust, specifically as follows:

[0186] Step S4.1: Based on the global artificial fish swarm algorithm, the fish swarm is randomly initialized in the relatively optimal parameter solution domain to form several initial artificial fish swarms. Each artificial fish in the initial artificial fish swarm represents a set of solutions of the ship thrust target allocation function with respect to the thrust of the propeller and the azimuth angle of the propeller.

[0187] The initialization parameters of the global artificial fish swarm algorithm include the number of artificial fish N, the initial state X of each artificial fish, the number of iterations IT, the number of attempts try_number, the crowding factor δ, the preset values ​​for jumping behavior ε and β, and the threshold value for swallowing behavior.

[0188] Step S4.2: Calculate the corresponding ship thrust target allocation function value based on the current state of each artificial fish, initialize the artificial fish state corresponding to the minimum value of the ship thrust target allocation function as the optimal artificial fish state, record the maximum movement step and visual value of each artificial fish, and output the current optimal solution X. best Write it to the bulletin board;

[0189] Step S4.3: Determine if the preset iteration count IT is met. If so, output the current optimal solution X on the bulletin board. best If not, proceed to steps S4.4 to S4.6;

[0190] Step S4.4: Initialize the maximum step size Step and visual value of each artificial fish in the artificial fish swarm;

[0191] Step S4.5: Evaluate the behavior of each artificial fish based on foraging behavior, grouping behavior, tail-chasing behavior, and random behavior, and select the optimal behavior to execute and update its state.

[0192] Step S4.6: For the current optimal solution X best Perform annealing operation, check if the temperature is less than the temperature threshold T_value. If it is, output the solution. Otherwise, lower the temperature and continue annealing until the temperature is less than the temperature threshold T_value.

[0193] Step S4.7: Obtain the current optimal solution X from each artificial fish swarm. best Let the solution domain be a relatively optimal parameter.

[0194] Furthermore, the foraging behavior described in step S4.5 specifically involves setting the current state of the i-th artificial fish to X. i The i-th artificial fish randomly selects a new state X within its field of vision (Visual). j And X j =X i +Visual·Rand(); if J j <J i That is, state X j Superior to X i The current state X of the artificial fish i To state X j Move one step;

[0195]

[0196] If J j ≥J i Then reselect state X. j If the fish cannot move after a preset number of attempts (try_number), it will randomly move forward one step. The formula for this random behavior is:

[0197]

[0198] In the formula: J j Represents state X j The thruster power; J i Represents state X i The thruster power; This represents the state of the artificial fish at time t; This represents the state of the artificial fish at time t+1; Step represents the step size; Rand() represents a random number in the interval [0,1].

[0199] The clustering behavior specifically refers to: in the current state X of the i-th artificial fish i Within the field of view, search for the number n individuals of a group of fish. f X, the center of the fish school c ;

[0200] In the formula: X s "s" refers to the general state of an artificial fish; "s" represents multiple states of an artificial fish.

[0201] If condition J is satisfied c n f / J i If ≤δ, then the current state X of the artificial fish is... i X towards the center of the fish gathering c Move one step: Otherwise, foraging behavior will be performed;

[0202] In the formula: δ represents the crowding factor; J c Indicates the power consumption of the thruster at the center position; This represents the state of the artificial fish at time t; This represents the state of the artificial fish at time t+1; Step represents the step size; Rand() represents a random number in the interval [0,1].

[0203] The tail-chasing behavior specifically refers to: in the state X of the i-th artificial fish i The domain search for the optimal artificial fish partner X b J b This represents the minimum power consumption of the thruster;

[0204] If condition J is satisfied b n f / J i If ≤δ, then the current state X of the artificial fish is... i Will towards X b Move one step;

[0205] In the formula: This represents the state of the artificial fish at time t; Represents the state of the artificial fish at time t+1; Step represents the step size; Rand() represents a random number in the interval [0,1]; J j Represents state X j The thruster power; δ represents the congestion factor; n f This indicates the number of individuals in the group; otherwise, foraging behavior is performed.

[0206] Furthermore, in step S5, the relatively optimal parameter solution domain is further optimized using the Egret algorithm, specifically as follows:

[0207] Step S5.1: Use the obtained relatively optimal parameters in the solution domain to determine the thruster thrust and thruster azimuth angle as the thrust allocation decision variables of the mathematical model of the ship thrust target allocation function;

[0208] The decision variables for thrust allocation are encoded as individuals in a flock of egrets, resulting in an encoded mathematical model; and the flock of egrets includes several egret squads composed of individuals from the flock.

[0209] Step S5.2: Construct an egret flock optimization algorithm focusing on egret flock waiting strategies, aggressive strategies, and the number of iterations;

[0210] The calculation formula for the waiting strategy is as follows:

[0211] X a,i =X i+exp(-t / (0.1·tmax))·0.1·hop·g i

[0212] In the formula: X a,i Indicates the position of the individual egret after iteration; X i represents the position of the egret individual after the last iteration; exp represents the exponential function; t represents the iteration number; tmax represents the maximum value of the iteration number; hop represents the feasible region of the independent variable; g i Represented as gradient;

[0213] The aggressive strategy includes random walks and encirclement mechanisms, and the formula for updating the position of egret individuals during random walks is as follows:

[0214] X b,i =X i +tan(r b,i )·hop / (1+t)

[0215] In the formula: X b,i Indicates the position of the individual egret after iteration; X i This indicates the position of the egret individual after the last iteration; r b,i represents a random number; t represents the number of iterations; hop represents the feasible region of the independent variable;

[0216] The formula for updating the individual egret position in the encirclement mechanism is as follows:

[0217] D h =X ibest -X i

[0218] D g =X gbest -X i

[0219] X c,i =(1-r i -r g )·X i +r h ·D h +r g ·D g

[0220] In the formula: D h D represents the difference between the optimal value of the egret squad and the current position of the individual egret; g This represents the difference between the optimal value of the egret flock and the current position of an individual egret; r i r g r h X represents a random number between [0,1]; ibest X represents the optimal value for the egret team; gbestThis represents the optimal value for the egret flock;

[0221] Step S5.3: Update the optimal solutions for the waiting strategy and the aggressive strategy, wherein the optimal solution is the position of the egret individual that minimizes the value of the encoded mathematical model;

[0222] Step S5.4: Compare the fitness of the updated egret individual position with the fitness of the egret individual position in the previous iteration;

[0223] If the fitness of the updated egret individual position is better than the fitness of the egret individual position in the previous iteration, then the updated egret individual position is adopted; otherwise, the update is abandoned; until the maximum number of iterations is reached.

[0224] Step S5.5: Decode the egret individuals' positions obtained after the maximum number of iterations to obtain the thrust allocation decision variables of the mathematical model of the ship thrust target allocation function;

[0225] The ship's thrust is allocated by acquiring the thrust allocation decision variables.

[0226] When a ship performs thrust allocation, this invention utilizes an improved artificial fish swarm algorithm, specifically an artificial fish swarm algorithm combined with simulated annealing. This effectively addresses the shortcomings of the standalone artificial fish swarm algorithm, which suffers from low computational accuracy and a tendency to get trapped in local optima. Furthermore, this solution sequentially incorporates an updated optimization algorithm, the egret flocking optimization algorithm, to better optimize the ship thrust allocation problem. By effectively combining the simulated annealing artificial fish swarm algorithm with the egret flocking algorithm, and adhering to the principle of "compensating for each other's weaknesses," the advantages of both algorithms are retained: first, the global convergence of the artificial fish swarm algorithm is used to quickly find a satisfactory solution domain, and then the egret flocking algorithm is used for rapid local search. This results in a hybrid algorithm that not only has a fast local search speed but also guarantees global convergence performance, effectively solving the ship thrust allocation problem and making it widely applicable in engineering practice.

[0227] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A ship thrust allocation method based on an improved fish-heron algorithm, characterized in that, Includes the following steps: Step S1: Establish a three-degree-of-freedom mathematical model of the ship and an interference model of the mathematical model; The three-degree-of-freedom mathematical model includes ship kinematics and ship dynamics models; The interference models include sea breeze models, ocean wave models, and ocean current models; Step S2: Based on the three-degree-of-freedom mathematical model and the interference model of the mathematical model, and combined with the hydrodynamic characteristics analysis of the propeller, obtain the power, torque and thrust model of the ship propeller; Step S3: Obtain the energy consumption of the propeller based on the power, torque, and thrust model of the ship's propeller; A target thrust allocation function for the ship is established based on the energy consumption of the propeller, the wear of the propeller, and the command error. Step S4: Optimize the thruster parameters based on the global artificial fish swarm algorithm to obtain several relatively optimal parameter solution domains that minimize the ship thrust target allocation function; The thruster parameters include the thruster's azimuth angle and thruster's thrust; Step S5: The relative optimal parameter solution domain is optimized again using the Egret algorithm to obtain the final relative optimal solution of the thruster parameters; The final relative optimal solution is used as the final azimuth angle and thrust of the thruster.

2. The ship thrust allocation method based on the improved heron algorithm according to claim 1, characterized in that, The three-degree-of-freedom mathematical model and its interference model in step S1 are specifically as follows: The ship kinematics model is The attitude and position functions of the ship in the northeast coordinate system; The attitude angle of the ship in the northeast coordinate system; Represents a three-dimensional Euclidean torus; Functions representing the ship's attitude and position; Indicates the speed of the ship; The rotation matrix representing the ship's attitude angles; Represents a zero matrix with 3 rows and 3 columns; Represents the angular velocity rotation matrix; The ship dynamics model is In the formula: This represents the inertia matrix of the hydrodynamic system, which is related to the linear and angular accelerations of the ship. This represents the hydrodynamic Coriolis centripetal force matrix, which is related to the ship's added mass. This indicates that the hydrodynamic damping coefficient matrix contains linear damping terms. With nonlinear damping term ; The restoring force caused by the buoyancy of the ship; The restoring force provided for ship ballast; For environmental interference; This is the theoretical thrust of the propulsion unit.

3. The ship thrust allocation method based on the improved heron algorithm according to claim 1, characterized in that, Step S2 obtains the power, torque, and thrust efficiency model of the ship's propulsion system, specifically as follows: Step S2.1: The thrust and torque of the propeller are defined as the following functional forms; In the formula: Indicates the thrust of the propulsion unit; Indicates the thruster torque; Indicates rotational speed; Indicates a fixed parameter; Indicates time-varying parameters; Step S2.2: Convert the functional form into specific expressions for the thruster thrust and torque as follows: In the formula: and These represent the thrust coefficient and torque coefficient, respectively. Indicates the density of seawater; Indicates the propeller diameter; Step S2.3: Based on the torque expression of the thruster, the formula for calculating the power consumption of the thruster can be obtained as follows: Step S2.4: From the calculation formulas in steps S2.2 and S2.3, the relationship between the thruster's power consumption and thrust is obtained as follows: 。 4. The ship thrust allocation method based on the improved heron algorithm according to claim 1, characterized in that, In step S3, a target thrust allocation function is established based on the propeller's energy consumption, propeller wear, and command error. The calculation formula is as follows: In the formula: The energy function representing the target allocation function of ship thrust; Indicates the thrust of the propulsion unit; Represents the azimuth angle of each thruster in the current allocation cycle; Indicates the weighting coefficients; Indicates the first Power consumption of the thruster; Indicates the correction factor; Indicates the first The thrust of the stage propeller; This represents the total power consumption of all thrusters; The slack variable matrix; Transpose of the slack variable matrix; and Represents a positive definite diagonal matrix; Indicates the azimuth angle of each thruster in the previous cycle; This represents the transpose of the difference between the azimuth angles of each thruster in the current allocation cycle and the azimuth angles of each thruster in the previous cycle. This represents the thruster configuration matrix; This is the theoretical thrust of the propulsion unit; Indicates the power variation limit; This represents the actual rate of change of thruster power; The desired rate of change of thruster power; This represents the weight coefficient matrix.

5. A ship thrust allocation method based on an improved herring algorithm according to claim 1, characterized in that, In step S4, the solution domain of several relatively optimal parameters that minimize the target allocation function of ship thrust is obtained, specifically: Step S4.1: Based on the global artificial fish swarm algorithm, the fish swarm is randomly initialized in the relatively optimal parameter solution domain to form several initial artificial fish swarms. Each artificial fish in the initial artificial fish swarm represents a set of solutions of the ship thrust target allocation function with respect to the thrust of the propeller and the azimuth angle of the propeller. The initialization parameters for the global artificial fish swarm algorithm include the number of individual artificial fish. The initial state of each artificial fish Number of iterations Number of attempts Crowding factor Preset values ​​for jump behavior , and the threshold for swallowing behavior ; Step S4.2: Calculate the corresponding ship thrust target allocation function value based on the current state of each artificial fish, and initialize the artificial fish state corresponding to the minimum value of the ship thrust target allocation function as the optimal artificial fish state. Record the maximum movement step size of each artificial fish. With vision Output the current optimal solution. Write it to the bulletin board; Step S4.3: Determine if the preset number of iterations is met. If so, output the current optimal solution on the bulletin board. If not, proceed to steps S4.4 to S4.6; Step S4.4: Initialize the maximum movement step size of each artificial fish in the artificial fish swarm. With vision ; Step S4.5: Evaluate the behavior of each artificial fish based on foraging behavior, grouping behavior, tail-chasing behavior, and random behavior, and select the optimal behavior to execute and update its own state. Step S4.6: For the current optimal solution Perform the annealing operation and determine if the temperature is below the temperature threshold. If the solution is correct, output the solution; otherwise, decrease the temperature and continue annealing until the temperature is below the temperature threshold. ; Step S4.7: Obtain the current optimal solution for each artificial fish swarm. Let the solution domain be a relatively optimal parameter.

6. A ship thrust allocation method based on an improved herring algorithm according to claim 5, characterized in that, The foraging behavior described in step S4.5 specifically involves: setting the first... The current state of the artificial fish is , No. An artificial fish in its field of vision A new state is randomly selected within. ,and ;like state Superior The current state of the artificial fish To state Move one step; like Then select a new state. To make an attempt; to achieve the desired outcome. If the artificial fish still cannot move after a certain number of attempts, it will randomly move forward one step. The formula for this random behavior is: In the formula: Representing state The thruster power; Representing state The thruster power; express The state of the artificial fish at any given moment; express The state of the artificial fish at any given moment; Indicates the step size; Represents a random number in the interval [0,1]. The clustering behavior specifically refers to: in the first... Current status of the artificial fish field of vision Inside, search for the number of individuals that gather in a school of fish. The center of the fish gathering ; In the formula: This refers generally to the state of artificially created fish; This indicates multiple states of an artificial fish; If the conditions are met The current state of the artificial fish is as follows: Towards the center of the fish gathering Move one step: ; Otherwise, foraging behavior will be performed; In the formula: Indicates the crowding factor; Indicates the power consumption of the thruster at the center position; express The state of the artificial fish at any given moment; express The state of the artificial fish at any given moment; Indicates the step size; Represents a random number in the interval [0,1]. The rear-end collision specifically refers to: in the first... The state of an artificial fish Search for the optimal artificial fish partner in the field , This represents the minimum power consumption of the thruster; If the conditions are met The current state of the artificial fish is as follows: Will towards Move one step; In the formula: express The state of the artificial fish at any given moment; express The state of the artificial fish at any given moment; Indicates the step size; Represents a random number in the interval [0,1]. Representing state The thruster power; Indicates the crowding factor; Indicates the number of individuals in a school of fish; Otherwise, it will engage in foraging behavior.

7. A ship thrust allocation method based on an improved herring algorithm according to claim 1, characterized in that, Step S5 involves further optimizing the solution domain of the relatively optimal parameters using the Egret algorithm, specifically as follows: Step S5.1: Use the obtained relatively optimal parameters in the solution domain to determine the thruster thrust and thruster azimuth angle as the thrust allocation decision variables of the mathematical model of the ship thrust target allocation function; The decision variables for thrust allocation are encoded as individuals in a flock of egrets, resulting in an encoded mathematical model; and the flock of egrets includes several egret squads composed of individuals from the flock. Step S5.2: Construct an egret flock optimization algorithm focusing on egret flock waiting strategies, aggressive strategies, and the number of iterations; The calculation formula for the waiting strategy is as follows: In the formula: Indicates the position of the individual egret after iteration; Indicates the position of the egret individual after the last iteration; Represents an exponential function; Indicates the number of iterations; This represents the maximum number of iterations. It indicates the feasible domain of the independent variable; Represented as gradient; The aggressive strategy includes random walks and encirclement mechanisms, and the formula for updating the position of egret individuals during random walks is as follows: In the formula: Indicates the position of the individual egret after iteration; Indicates the position of the egret individual after the last iteration; Represents a random number; Indicates the number of iterations; It indicates the feasible domain of the independent variable; The formula for updating the individual egret position in the encirclement mechanism is as follows: In the formula: This represents the difference between the optimal value of the egret squad and the current position of the individual egret. This represents the difference between the optimal value of the egret flock and the current position of an individual egret. , , express Random numbers between; This represents the optimal value for the egret team; This represents the optimal value for the egret flock; Step S5.3: Update the optimal solutions for the waiting strategy and the aggressive strategy, wherein the optimal solution is the position of the egret individual that minimizes the value of the encoded mathematical model; Step S5.4: Compare the fitness of the updated egret individual position with the fitness of the egret individual position in the previous iteration; If the fitness of the updated egret individual position is better than the fitness of the egret individual position in the previous iteration, then the updated egret individual position is adopted; otherwise, the update is abandoned; until the maximum number of iterations is reached. Step S5.5: Decode the egret individuals' positions obtained after the maximum number of iterations to obtain the thrust allocation decision variables of the mathematical model of the ship thrust target allocation function; The ship's thrust is allocated by acquiring the thrust allocation decision variables.

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