Indoor flight control method for unmanned aerial vehicle

Abstract

The application provides an indoor flight control method for a UAV, and belongs to the field of PID control optimization, and the specific steps are as follows: step one, converting the indoor flight control problem of the UAV into a mathematical model to be optimized; step two, establishing a PID control model of an indoor flight speed control system of the UAV; step three, improving a snake optimization algorithm, and the specific improvement strategies are as follows: D1, introducing a weight coefficient to improve a position updating expression of the snake when searching for food; D2, introducing a balance factor to balance the probability of the snake in a fighting mode and a mating mode; step four, optimizing the indoor flight speed PID controller of the UAV by using the improved snake optimization algorithm, so as to obtain optimal Kp, Ki and Kd parameters of the indoor flight speed PID controller of the UAV; and step five, inputting the obtained optimal parameters into the PID control model of the indoor flight speed control system of the UAV, so as to obtain the optimal effect of the indoor flight control of the UAV.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of PID control optimization, and particularly relates to an indoor flight control method for a UAV. BACKGROUND

[0002] A UAV is a remotely controlled or autonomously programmed aircraft, widely used in military, commercial, scientific research and entertainment fields; the flight control system of a UAV realizes precise attitude control, navigation and task execution through sensors, electronic speed controllers (ESC) and brushless motors; most modern UAVs use brushless DC motors (BLDC) in combination with multi-rotor structures to achieve stable flight capabilities; the core of a UAV includes a flight controller (FC), a GPS navigation system, a communication system, a camera and various sensors; through a ground control station (GCS), users can remotely control the UAV to perform aerial photography, mapping, reconnaissance and logistics delivery tasks; automatic return, fixed-point hovering and path planning are common intelligent functions; due to its flexibility, efficiency and relatively low cost, the UAV has been widely used in agriculture, environmental monitoring, security and transportation fields, and with the advancement of technology, the use scenarios and intelligence level of the UAV will continue to expand.

[0003] PID (Proportional-Integral-Derivative) control is a classic feedback control algorithm widely used in industrial control and automation fields; PID control adjusts the error of the system (i.e. the difference between the desired value and the actual value) to control the output to achieve the stable target state of the system; in the face of indoor flight environment of a UAV, the performance of PID control may be limited, in order to solve this problem, an intelligent optimization algorithm is used to automatically adjust the PID parameters, which not only effectively finds the optimal parameter value, but also significantly improves the performance and applicability of PID control.

[0004] Snake Optimizer (SOA) is a bionic heuristic algorithm that simulates the behavior of a snake searching for food in a complex environment; the snake optimization algorithm realizes global search and local development through the movement and deformation of individuals (snakes), aiming to solve global optimization problems; the algorithm is divided into "fighting mode" and "development mode", fighting mode: simulates the rapid response of a snake when facing obstacles, performs global search with large step size and direction change to avoid falling into local optimum; development mode: simulates the fine adjustment of a snake when approaching food, optimizes the accuracy of the solution through small step size and local search; the snake optimization algorithm balances exploration and development by introducing adaptive weights and environmental feedback to improve global search efficiency and convergence speed. SUMMARY

[0005] The purpose of the present application is to improve the snake optimization algorithm, improve the global search ability in the early stage and the local search ability in the late stage of the optimization process, reduce the possibility of falling into local optimum, and improve the optimization accuracy and response speed of the algorithm, optimize the speed PID controller control parameters of the indoor flight of the unmanned aerial vehicle by improving the snake optimization algorithm, improve the accuracy and stability of the speed control, and reduce the adverse effects of dynamic error on the control effect in the parameter setting process.

[0006] To achieve the above purpose, the present application adopts an indoor flight control method of unmanned aerial vehicle, and the specific steps are as follows.

[0007] Step one, the indoor flight control problem of unmanned aerial vehicle is converted into a mathematical model to be optimized.

[0008] Step two, the PID control model of the indoor flight speed control system of the unmanned aerial vehicle is established.

[0009] Step three, the snake optimization algorithm is improved, and the specific improvement strategy is as follows:

[0010] D1, the weight coefficient is introduced in the algorithm exploration stage to improve the position update expression of the snake searching for food.

[0011] D2, the balance factor is introduced in the algorithm development stage to balance the probability of the snake in the fighting mode and the mating mode, and the specific is as follows.

[0012] Step 1, in the algorithm exploration stage, the weight coefficient w is introduced to improve the position update expression of the snake searching for food, and the improved position update expression is as follows:

[0013]

[0014]

[0015] In the formula, X i，m (t+1) is the position of the i male snake in the t+1 iteration, X i，f (t+1) is the position of the i female snake in the t+1 iteration, X rand,m (t) and X rand，f (t) are the positions of the randomly selected male snake and female snake respectively, A m and A f represent the ability of the male snake and the female snake to find food respectively, w is the weight coefficient, X max is the upper limit of the algorithm search, X min is the lower limit of the algorithm search, rand is a random number between 0 and 1, and c2 is a constant, which is set to 0.05.

[0016] Step 2, in the algorithm development stage, a balance factor is introduced to balance the probability of the snake in the fighting mode and the mating mode, and the balance factor expression is:

[0017]

[0018] In the formula, η is a weight coefficient, t is the current iteration number, T is the maximum iteration number, N is the population size of the algorithm, and rand(N, 1) represents a random number between N and 1.

[0019] Step 4, the improved snake optimization algorithm is used to optimize the indoor flight speed PID controller of the unmanned aerial vehicle, and the optimal Kp, Ki and Kd parameters of the indoor flight speed PID controller of the unmanned aerial vehicle are obtained.

[0020] Step 5, the optimal parameters Kp, Ki and Kd obtained are input into the PID control model of the indoor flight speed control system of the unmanned aerial vehicle, and the optimal effect of the indoor flight control of the unmanned aerial vehicle is obtained.

[0021] Preferably, the mathematical model to be optimized in step 1 is:

[0022]

[0023] In the formula, f(t) is the system output, e(t) is the system error input, t is the current time, and T is the total running time of the system.

[0024] Preferably, the PID control model of the indoor flight speed control system of the unmanned aerial vehicle in step 2 includes an input module, an error calculation module, a speed PID controller module, an improved snake optimization algorithm module, a speed detection module, and a motor module. The desired speed value is input from the input module, and the actual speed value detected by the speed detection module is input to the error calculation module to obtain the speed error value. The speed PID controller receives the speed error value and applies the optimized PID parameters of the improved snake optimization algorithm module to calculate the reference speed value, and then controls the motor to run at high speed to realize the control of the indoor flight speed of the unmanned aerial vehicle.

[0025] Preferably, the weight coefficient is introduced to improve the position update expression of the snake searching for food in the algorithm exploration stage in step 3. In the optimization process, the weight coefficient can help the snake optimize the step size and direction selection when approaching the target solution, making the position update more accurate, thereby speeding up the convergence speed of the algorithm and reducing unnecessary exploration steps.

[0026] Preferably, the algorithm development stage of step three introduces a balance factor to balance the probabilities of the snake being in the fighting mode and the mating mode. By dynamically adjusting the balance factor, the two modes can be switched according to the problem characteristics and the optimization stage. In this way, the rapid global search capability of the fighting mode is retained, and the local development advantage of the mating mode is utilized, thereby enhancing the adaptability and stability of the algorithm.

[0027] Preferably, the improved snake optimization algorithm is used to optimize the indoor flight speed PID controller of the unmanned aerial vehicle in step four, and the specific steps are as follows:

[0028] S1, simulate the working condition of the indoor flight speed control system of the unmanned aerial vehicle, and design a transfer function to describe the dynamic characteristics of the system;

[0029] S2, give the input of the indoor flight speed control system of the unmanned aerial vehicle, which is the expected speed value of the motor in the control system;

[0030] S3, initialize the population size N of the improved snake optimization algorithm, the problem dimension d, the upper bound X max of the algorithm search, the lower bound X min of the algorithm search, and the maximum number of iterations T;

[0031] S4, encode the parameters Kp, Ki, and Kd of the indoor flight speed PID controller of the unmanned aerial vehicle into the solution of the search space of the improved snake optimization algorithm. With the iteration of the algorithm, the updated population individual position reflects the corresponding parameters of the indoor flight speed PID controller of the unmanned aerial vehicle;

[0032] S5, calculate the fitness value of the current iteration population individual according to the fitness function, record the minimum fitness value, and compare it with the optimal fitness value of the population in the last iteration to retain the optimal fitness value;

[0033] S6, simulate the optimization process of each stage of the improved snake optimization algorithm, i.e., update the Kp, Ki, and Kd parameters of the indoor flight speed PID controller of the unmanned aerial vehicle;

[0034] S7, if the updated population individual is better than the last iteration, retain the current optimal solution;

[0035] S8, judge whether the current iteration number reaches the maximum iteration number. If it does, exit the loop, output the global optimal solution, and assign it to the indoor flight speed PID controller of the unmanned aerial vehicle as the Kp, Ki, and Kd parameters. Otherwise, return to execute S2.

[0036] Preferably, the S1 simulates the working condition of the indoor flight speed control system of the unmanned aerial vehicle, and designs a transfer function to describe the dynamic characteristics of the system, and the transfer function formula is:

[0037]

[0038] where s represents a complex frequency variable in Laplace transform.

[0039] Preferably, the parameters Kp, Ki and Kd of the indoor flight speed PID controller of the unmanned aerial vehicle in S4 are encoded into the solution of the improved snake optimization algorithm search space, and with the iteration of the algorithm, the updated population individual position, i.e., the solution of the algorithm search space, is updated, that is, the parameters of the indoor flight speed PID controller of the unmanned aerial vehicle are updated, and the encoding vector is:

[0040] x = [Kp, Ki, Kd];

[0041] where x is the encoding vector, i.e., the solution of the improved snake optimization algorithm search space, and Kp, Ki and Kd are the proportional parameter, integral parameter and differential parameter of the indoor flight speed PID controller of the unmanned aerial vehicle, respectively.

[0042] Preferably, S6 simulates the optimization process of each stage of the improved snake optimization algorithm, and the specific steps are: Step 1, simulating the exploration stage of the algorithm, introducing a weight coefficient w to improve the position update expression of the snake when searching for food, and executing formulas (1)-(2);

[0043] Step 2, simulating the development stage of the algorithm, introducing a balance factor to balance the probability of the snake being in the fighting mode and the mating mode, when the balance factor η is greater than 0.5, the snake is in the fighting mode, and when the balance factor η is less than or equal to 0.5, the snake is in the mating mode, and executing formulas (3)-(5);

[0044]

[0045]

[0046] where X i，m (t+1) is the position of the ith male snake at the t+1th iteration, X i，f (t+1) is the position of the ith female snake at the t+1th iteration, X i，m (t) and X i，f (t) are the position of the ith male snake at the tth iteration and the position of the ith female snake at the tth iteration, respectively, F m and F f represent the fighting ability of the male snake and the female snake, respectively, M m and M f represent the mating ability of the male snake and the female snake, respectively, rand is a random number between 0 and 1, c3 is a constant and is set to 0.05, Q is the food amount, X best，f is the position of the optimal individual in the male snake group, and X best，m is the position of the optimal individual in the female snake group, and η is the balance factor.

[0047] Therefore, by introducing the weight coefficient and the balance factor to improve the snake optimization algorithm, the accuracy and performance of the algorithm are improved. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 The flow chart of the indoor flight control method of the unmanned aerial vehicle.

[0049] Figure 2 The PID control model diagram of the indoor flight speed control system of the unmanned aerial vehicle.

[0050] Figure 3 The comparison diagram of the optimal fitness function of the basic snake optimization algorithm and the improved snake optimization algorithm.

[0051] Figure 4 The comparison diagram of the effect of optimizing the PID parameters of the basic snake optimization algorithm and the improved snake optimization algorithm. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0053] The present application provides a technical solution: an indoor flight control method of an unmanned aerial vehicle, which specifically comprises the following steps, as shown in Figure 1

[0054] Step 1: Convert the indoor flight control problem of the unmanned aerial vehicle into a mathematical model to be optimized.

[0055] Further, the mathematical model to be optimized in step 1 is:

[0056]

[0057] In the formula, f(t) is the system output, e(t) is the system error input, t is the current time, and T is the total running time of the system.

[0058] Step 2: Establish the PID control model of the indoor flight speed control system of the unmanned aerial vehicle.​

[0059] Further, the PID control model of the indoor flight speed control system of the unmanned aerial vehicle in step two is shown in the figure Figure 2 , which comprises an input module, an error calculation module, a speed PID controller module, an improved snake optimization algorithm module, a speed detection module, and a motor module. The expected speed value is input from the input module and is input to the error calculation module together with the actual speed value detected by the speed detection module to obtain a speed error value. The speed PID controller receives the speed error value and applies the optimized PID parameters of the improved snake optimization algorithm module to calculate a reference speed value, thereby controlling the high-speed operation of the motor and achieving the control of the indoor flight speed of the unmanned aerial vehicle.

[0060] Step three, improve the snake optimization algorithm, and the specific improvement strategy is:

[0061] D1, introduce a weight coefficient to improve the position update expression of the snake searching for food in the algorithm exploration stage.

[0062] D2, introduce a balance factor to balance the probabilities of the snake being in the fighting mode and the mating mode in the algorithm development stage, which is as follows.

[0063] Step 1, in the algorithm exploration stage, introduce a weight coefficient w to improve the position update expression of the snake searching for food, and the improved position update expression is:

[0064]

[0065]

[0066] wherein, X i，m (t+1) is the position of the ith male snake at the t+1th iteration, X i，f (t+1) is the position of the ith female snake at the t+1th iteration, X rand,m (t) and X rand，f (t) are the positions of the randomly selected male snake and female snake, respectively, A m and A f represent the abilities of the male snake and the female snake to find food, respectively, w is the weight coefficient, X max is the upper bound of the algorithm search, and X min is the lower bound of the algorithm search, rand is a random number between 0 and 1, and c2 is a constant, which is set to 0.05.

[0067] Step 2, in the algorithm development stage, introduce a balance factor to balance the probabilities of the snake being in the fighting mode and the mating mode, and the expression of the balance factor is:

[0068]

[0069] In the formula, η is a weight coefficient, t is the current iteration number, T is the maximum iteration number, N is the population size of the algorithm, and rand(N, 1) represents a random number between N and 1.

[0070] Step four, the improved snake optimization algorithm is used to optimize the indoor flight speed PID controller of the unmanned aerial vehicle, and the optimal Kp, Ki and Kd parameters of the indoor flight speed PID controller of the unmanned aerial vehicle are obtained. The specific steps are as follows:

[0071] S1, simulate the working condition of the indoor flight speed control system of the unmanned aerial vehicle, and design a transfer function to describe the dynamic characteristics of the system;

[0072] S2, give the input of the indoor flight speed control system of the unmanned aerial vehicle, which is the expected speed value of the motor in the control system;

[0073] S3, initialize the population size N of the improved snake optimization algorithm, the problem dimension d, the upper limit X max of the algorithm search, the lower limit X min of the algorithm search, and the maximum iteration number T;

[0074] S4, encode the parameters Kp, Ki and Kd of the indoor flight speed PID controller of the unmanned aerial vehicle into the solution of the search space of the improved snake optimization algorithm. With the iteration of the algorithm, the updated population individual position reflects the corresponding parameters of the indoor flight speed PID controller of the unmanned aerial vehicle;

[0075] S5, calculate the fitness value of the current iteration population individual according to the fitness function, record the minimum fitness value, and compare it with the optimal fitness value of the population in the last iteration, and keep the optimal fitness value;

[0076] S6, simulate the optimization process of each stage of the improved snake optimization algorithm, that is, update the Kp, Ki and Kd parameters of the indoor flight speed PID controller of the unmanned aerial vehicle;

[0077] S7, if the updated population individual is better than the last iteration, keep the current optimal solution;

[0078] S8, judge whether the current iteration number reaches the maximum iteration number, if yes, exit the loop, output the global optimal solution, and assign it to the indoor flight speed PID controller of the unmanned aerial vehicle as the Kp, Ki and Kd parameters, otherwise return to execute S2.

[0079] Further, the S1 simulates the working condition of the indoor flight speed control system of the unmanned aerial vehicle, and designs a transfer function to describe the dynamic characteristics of the system, and the transfer function formula is:

[0080]

[0081] In the formula, s represents a complex frequency domain variable in Laplace transform.

[0082] Further, the parameters Kp, Ki and Kd of the indoor flight speed PID controller of the unmanned aerial vehicle in the S4 are encoded into the solution of the search space of the improved snake optimization algorithm, and with the iteration of the algorithm, the updated population individual position, i.e., the solution of the search space of the algorithm, is updated, that is, the parameters of the indoor flight speed PID controller of the unmanned aerial vehicle are updated, and the encoding vector is:

[0083] x = [Kp, Ki, Kd];

[0084] In the formula, x is the encoding vector, i.e., the solution of the search space of the improved snake optimization algorithm, and Kp, Ki and Kd are respectively the proportional parameter, integral parameter and differential parameter of the indoor flight speed PID controller of the unmanned aerial vehicle.

[0085] Further, the S6 simulates the optimization process of each stage of the improved snake optimization algorithm, and the specific steps are: Step 1, simulating the exploration stage of the algorithm, introducing a weight coefficient w to improve the position update expression of the snake when searching for food, and executing formulas (1)-(2);

[0086] Step 2, simulating the development stage of the algorithm, introducing a balance factor to balance the probability of the snake being in the fighting mode and the mating mode, when the balance factor η is greater than 0.5, the snake is in the fighting mode, and when the balance factor η is less than or equal to 0.5, the snake is in the mating mode, and executing formulas (3)-(5);

[0087]

[0088]

[0089] In the formula, X i，m (t+1) is the position of the i-th male snake at the t+1 iteration, X i，f (t+1) is the position of the i-th female snake at the t+1 iteration, X i，m (t), X i，f (t) are respectively the position of the i-th male snake at the t iteration and the position of the i-th female snake at the t iteration, F m , F f respectively represent the fighting ability of the male snake and the female snake, M m , M f respectively represent the mating ability of the male snake and the female snake, rand is a random number between 0 and 1, c3 is a constant and is set to 0.05, Q is the food amount, X best，f is the position of the optimal individual in the male snake group, X best，m is the position of the optimal individual in the female snake group, and η is the balance factor.

[0090] Step five, input the obtained optimal parameters Kp, Ki and Kd into the PID control model of the indoor flight speed control system of the unmanned aerial vehicle to obtain the optimal effect of indoor flight control of the unmanned aerial vehicle.

[0091] To verify the indoor flight control method of the unmanned aerial vehicle, the improved snake optimization algorithm and the basic snake optimization algorithm are compared by simulation experiments through Matlab and Simulink. First, the mathematical model of the snake optimization algorithm is improved through Matlab, and the PID control model of the indoor flight speed control system of the unmanned aerial vehicle is established through Simulink to initialize the parameters of the improved snake optimization algorithm, including population size N = 50, problem dimension d = 3, upper limit of algorithm search X max = 1, lower limit of algorithm search X min = 0, maximum number of iterations T = 30, and running the Matlab program to obtain the optimal fitness value comparison curve of the basic snake optimization algorithm and the improved snake optimization algorithm as shown in the figure. Figure 3 The improved snake optimization algorithm reaches the optimal fitness value 14.55 at about the 18th iteration, and the optimization accuracy is better than that of the basic snake optimization algorithm, and the convergence speed is faster, so that the control performance of the indoor flight control system of the unmanned aerial vehicle can be improved.

[0092] Further, Figure 4 To compare the PID control effect of the basic snake optimization algorithm and the improved snake optimization algorithm, the target input value is set to 100 units, and it can be seen from Figure 4 that the overshoot of the improved snake optimization algorithm is lower, and the response speed is faster, so that the parameters of the improved snake optimization algorithm are better than those of the basic snake optimization algorithm, and the speed of the unmanned aerial vehicle can reach the stable state quickly, and the indoor flight control effect of the unmanned aerial vehicle can be improved.

Claims

1. A method for indoor flight control of an unmanned aerial vehicle (UAV), characterized in that, The specific steps are as follows: Step 1: Transform the indoor flight control problem of the UAV into a mathematical model to be optimized; Step 2: Establish a PID control model for the indoor flight speed control system of the UAV; Step 3: Improve the snake optimization algorithm. The specific improvement strategy is as follows: D1. In the algorithm exploration phase, a weight coefficient w is introduced to improve the position update expression when the snake is searching for food. Specifically: （1）； （2）； In the formula, Let be the position of the i-th male snake in the (t+1)-th iteration. Let be the position of the i-th female snake in the (t+1)-th iteration. , The locations of the randomly selected male and female snakes are shown. , represents the ability of male and female snakes to find food, respectively; w is the weighting coefficient; t is the current iteration number; and T is the maximum iteration number. This is the upper bound for the algorithm's search. To provide a lower bound for the algorithm's search, rand is a random number between 0 and 1. It is a constant, set to 0.05; D2. During the algorithm development phase, a balance factor is introduced to balance the probability of the snake being in combat mode and mating mode. The balance factor is specifically: （3）； In the formula, The balance factor is N, and the algorithm population size is N. This indicates generating a random number between N and 1; Step 4: Optimize the indoor flight speed PID controller of the UAV using the improved snake optimization algorithm to obtain the optimal Kp, Ki, and Kd parameters of the indoor flight speed PID controller of the UAV. Step 5: Input the obtained optimal parameters Kp, Ki, and Kd into the PID control model of the UAV indoor flight speed control system to obtain the optimal effect of UAV indoor flight control.

2. The indoor flight control method for unmanned aerial vehicles according to claim 1, characterized in that, In step two, a PID control model for the indoor flight speed control system of the UAV is established. The model includes an input module, an error calculation module, a speed PID controller module, an improved snake optimization algorithm module, a speed detection module, and a motor module. The desired speed value is input from the input module and is combined with the actual speed value detected by the speed detection module. The difference between these two values ​​is then calculated by the error calculation module to obtain the speed error value. The speed PID controller receives the speed error value and uses the optimized PID parameters from the improved snake optimization algorithm module to calculate the reference speed value. This value is then used to control the motor to run at high speed, thereby controlling the indoor flight speed of the UAV.

3. The indoor flight control method for unmanned aerial vehicles according to claim 2, characterized in that, In step four, the improved snake optimization algorithm is used to optimize the PID controller for the indoor flight speed of the UAV. The specific steps are as follows: S1. Simulate the operating conditions of the indoor flight speed control system of the UAV and design a transfer function to describe the dynamic characteristics of the system. S2. Given the input to the indoor flight speed control system of the UAV, the input is the desired speed value of the motor in the control system; S3. Initialize the population size N, problem dimension d, and upper bound of the algorithm search for the improved snake optimization algorithm. Algorithm search lower bound Maximum number of iterations T; S4. Encode the parameters Kp, Ki, and Kd of the UAV indoor flight speed PID controller into the solution of the search space of the improved snake optimization algorithm. As the algorithm iterates, the updated population individual positions reflect the corresponding parameters of the UAV indoor flight speed PID controller. S5. Calculate the fitness value of individuals in the current iteration population according to the fitness function, record the minimum fitness value, compare it with the optimal fitness value of the population in the previous iteration, and retain the optimal fitness value. S6. Simulate the optimization process of each stage of the improved snake optimization algorithm, that is, update the Kp, Ki, and Kd parameters of the UAV indoor flight speed PID controller; S7. If the updated population individuals are better than those in the previous iteration, then retain the current optimal solution; S8. Determine if the current iteration count has reached the maximum iteration count. If it has, exit the loop, output the global optimal solution, and assign it to the UAV indoor flight speed PID controller as three parameters: Kp, Ki, and Kd. Otherwise, return to execute S2.

4. The indoor flight control method for unmanned aerial vehicles according to claim 3, characterized in that, The optimization process of the improved snake optimization algorithm at each stage is simulated, and the specific steps are as follows: Step 1: Simulation algorithm exploration stage, introduce weight coefficient w to improve the position update expression when the snake is looking for food, and execute formula (1)-(2); Step 2, in the simulation algorithm development stage, introduces a balance factor to balance the probability of the snake being in combat mode and mating mode. When the balance factor... When the balance factor is greater than 0.5, the snake is in combat mode. When the value is less than or equal to 0.5, the snake is in mating mode and executes formulas (3)-(5). （4）； （5）； In the formula, Let be the position of the i-th male snake in the (t+1)-th iteration. Let be the position of the i-th female snake in the (t+1)-th iteration. , Let be the positions of the i-th male snake and the i-th female snake at the t-th iteration, respectively. , These respectively represent the fighting abilities of male and female snakes. , These represent the mating ability of male and female snakes, respectively, where rand is a random number between 0 and 1. It is a constant, set to 0.05, where Q is the amount of food. This represents the position of the optimal individual within the male snake group. This represents the position of the optimal individual within the female snake group. It is a balancing factor.

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