Plane four-bar linkage parameter optimization method and system based on improved GA-PSO algorithm

Abstract

The invention discloses a planar four-bar linkage parameter optimization method and system based on an improved GA-PSO algorithm. The method comprises the following steps: firstly, establishing an optimization model by taking an isolating switch planar four-connecting-rod transmission mechanism as a research object, selecting a connecting rod length and a rocker length as design variables and selecting a minimum square deviation of a mechanism output angle as a design target; secondly, a contraction factor and a GA algorithm are introduced to optimize a traditional PSO model, an improved GA-PSO model is constructed, the global optimization capacity of the PSO algorithm is enhanced, and then mechanism parameters are optimized; further, the optimization result is compared with GA-PSO, improved PSO, PSO, GA and other models, and it is found that the improved GA-PSO model has a more obvious optimization effect on parameters of the planar four-bar linkage transmission mechanism; and finally, outputting mechanism parameters optimized by the improved GA-PSO model as a final result.

Description

Technical Field

[0001] The invention belongs to the field of mechanical structure parameter optimization, and in particular relates to a planar four-link parameter optimization method and system based on an improved GA-PSO algorithm. Background Art

[0002] Among various switchgears, the most frequently used is the isolating switch. Its structure is simpler than that of the circuit breaker, but its usage is usually 2-3 times that of the circuit breaker. Isolating switches are widely used in substations. They are mainly used for electrical isolation on the maintenance side of the primary line equipment of the substation. They can open and close many devices directly connected to the busbar to protect the personal safety of maintenance personnel and related equipment. Its quality can directly affect the safety of the power grid, and it is one of the most important equipment in the power grid. If the isolating switch fails, it will cause the switching of related lines in the substation and the equipment maintenance work to be unable to proceed as scheduled. In serious cases, it will endanger the operation safety of the primary equipment and the personal safety of maintenance personnel.

[0003] Because the isolating switches are usually exposed to the air most of the time, they are always eroded by the wind and rain environment. With the transition of the power system mechanism from the periodic maintenance system to the condition maintenance system in recent years, the service life of most isolating switches is getting longer and longer. In addition, due to the large load of the equipment, many isolating switches have to be used for life once they are put into use, and they will not be replaced unless a major accident occurs. Therefore, further research on isolating switches is becoming more and more important. It is precisely based on the important role of isolating switches and with the rapid development of the national power industry in recent times, the development of isolating switches is also urgent. Under such a development momentum, people have also put forward higher requirements for the breaking performance and mechanical properties of isolating switches.

[0004] At the same time, as a widely used equipment in substations, there are many studies on the operation problems of disconnectors (such as heating, corrosion, transmission jamming, insulator breakage, etc.), but there are few studies on the optimization of the transmission structure parameters of disconnectors, especially the planar four-link transmission structure. Therefore, improving the transmission structure of disconnectors and improving the reliability of their transmission are of great significance to ensure the reliability and safety of the operation and maintenance of the primary line of substations.

[0005] Prior art document 1 (CN 118153735, application publication date 2024.06.07) discloses a charging station site selection and capacity determination method for multi-objective optimization, which obtains road network data and extracts road network structure information, determines charging station candidate points based on road network structure information analysis to obtain a candidate charging station set; obtains electric vehicle distribution location data and charging demand information to generate a charging demand distribution; based on the number of charging stations and capacity restrictions, combines the charging demand distribution map and the candidate charging station set to generate a charging station site selection and capacity determination plan. Comprehensive optimization reduces the construction and operation costs of charging stations and the costs of electric vehicle users, seeks the best solution to generate the best site selection and capacity determination plan, and provides users with high-quality charging services.

[0006] 1. From the perspective of the problem to be solved, the problem to be solved in the prior art document 1 is the site selection and capacity determination of the charging station, that is, selecting the best location for the charging station, while the present invention optimizes the parameters of the planar four-bar transmission mechanism of the disconnector, and finds the optimal values ​​of the transmission mechanism parameters through algorithm iteration, thereby achieving the purpose of optimizing the mechanical structure. The problems to be solved are essentially different;

[0007] From the objective function point of view, the prior art document 1 takes into account the interests of both electric vehicles and charging station contractors, and plans the charging station with the goal of minimizing the comprehensive cost consisting of the construction and operation cost of the charging station and the cost of the electric vehicle, while the present invention takes the actual output angle and the expected output angle as close as possible as the goal, that is, the square deviation of the output angle of the transmission mechanism is minimized as the design goal for optimization, and the optimization goals are also very different;

[0008] From the perspective of constraints, the prior art document 1 is based on the constraints that the number of charging piles in the charging station cannot exceed the upper and lower limits of the resource configuration of the corresponding charging station level, and the charging demand of the charging station cannot exceed the maximum charging power provided by the charging station. In order to improve the force transmission performance of the mechanism, the present invention requires that the minimum transmission angle or the maximum transmission angle of the planar four-bar transmission mechanism must be satisfied, and the constraints are also very different.

[0009] Second, at the algorithm level, the prior art document 1 has the disadvantage of premature convergence and easy to fall into the local optimal solution according to the PSO algorithm. When the particles are updated, the crossover and mutation operations in the GA algorithm are introduced to optimize this problem. However, when the present invention calculates the extreme value of the function according to the PSO algorithm, premature phenomenon often occurs, resulting in a large deviation in the solution of the extreme value of the function. When calculating the extreme value of the fitness function, the selection, crossover and mutation operator operations of the GA algorithm are introduced, and the objective function is directly used as the search information in a probabilistic manner, thereby accelerating the evolution speed of the algorithm and improving the convergence accuracy. The two algorithms have essential differences in the angles and formula arrangements introduced by the GA algorithm, so they cannot be treated equally. The convergence and accuracy of the improved GA-PSO algorithm proposed in this article are relatively high, and are suitable for the problems to be solved in this article. Summary of the invention

[0010] In order to solve the deficiencies in the prior art, the present invention provides a method for optimizing parameters of a planar four-bar linkage transmission mechanism of an isolating switch based on an improved GA-PSO algorithm.

[0011] The present invention adopts the following technical solution.

[0012] The present invention provides a planar four-link parameter optimization method and system based on an improved GA-PSO algorithm, comprising:

[0013] Determine the correspondence between the driving member and the driven member of the planar four-bar transmission mechanism of the disconnector, obtain the variables in the optimization process as the connecting rod length and the rocker length according to the structural characteristics of the transmission mechanism, and build the objective function of the optimization process; establish the constraints of the transmission angle and the rod length, and build the optimization model; build a GA-PSO variable parameter optimization model, and use the contraction factor model to improve it; use the optimization model to perform preliminary optimization and obtain the preliminary rod length parameter optimization value, use the GA-PSO model to perform optimization, and output the optimal values ​​of the connecting rod length and the rocker length.

[0014] Preferably, the corresponding relationship between the driving member and the driven member of the planar four-bar linkage transmission mechanism of the isolating switch includes: the relative length of the crank AB is l1, the relative length of the frame AD is l4, the relative length of the connecting rod BC is l2, and the relative length of the rocker CD is l3; when the rocker is in the right limit position, the initial position angle of the crank AB is l1, the relative length of the frame AD is l4, the relative length of the connecting rod BC is l2, and the relative length of the rocker CD is l3;

[0015]

[0016] When the joystick is at the right limit position, the initial position angle of the joystick CD

[0017]

[0018] The design variables are

[0019] Preferably, the objective function of the construction optimization process includes:

[0020] Expected output angle

[0021] Angle of crank AB s is the crank angle In the interval The number of discrete points in, i is the serial number of each discrete point,

[0022] The objective function is ψ si is the actual output angle.

[0023] Preferably, the actual output angle

[0024] where α i and β i l3 and r respectively i The angle and r i and l4, where r i is the distance between hinges B and D;

[0025]

[0026]

[0027]

[0028] Preferably, the constraints of the transmission angle include:

[0029] Minimum transmission angle γγ min ≥45°, maximum transmission angle γm ax ≥135°,

[0030] The constraint equation of the transmission angle is

[0031]

[0032]

[0033] The rod length constraints include:

[0034] l2≥l1, g3(X)=x1-l1≥0

[0035] l3≥l1, g4(X)=x2-l1≥0

[0036] l2+l3≥l1+l4, g5(X)=x1+x2-(l1+l4)≥0

[0037] l4+l3≥l1+l2, g6(X)=-x1+x2+(l4-l1)≥0

[0038] l4+l2≥l1+l3, g7(X)=x1-x2+(l4-l1)≥0.

[0039] Preferably, the optimization model is The fitness function is the objective function

[0040] Preferably, the optimization of the rod length parameters by using the GA-PSO model includes initializing the PSO algorithm, initializing a particle swarm of size N, and randomly initializing the position and velocity of the particles: for any i, d, in [x min , x max ] follows uniform distribution to generate x id ; For any i, d, in [v min , v max ] follows uniform distribution to generate V id ; where i = 1, ..., N, d = 1, ..., D, x min is the minimum particle position preset by the algorithm, x max is the maximum particle position preset by the algorithm, v min The minimum particle velocity preset by the algorithm, v max is the maximum particle speed preset by the algorithm, the x id Refers to the value of the i-th particle of the d-dimensional vector, where d refers to the d-th dimension value of the D-dimensional vector, V id refers to the velocity of the ith particle of the d-dimensional vector;

[0041] The population X consists of N particles:

[0042] X=(X1,X2,…,X N )

[0043] The i-th particle in population X represents a D-dimensional vector:

[0044] X i =(x i1 , x i2 , …, x iD ) T

[0045] The velocity of the ith particle is:

[0046] V i =(V i1 , V i2 , …, V iD ) T

[0047] During initialization, the position of the random particle is set to the optimal position of the current particle, the objective function is used as the fitness function, and the optimal position of the current particle is substituted into the fitness function to calculate the fitness value;

[0048] For each particle, its fitness value and the best position P it has ever experienced id The fitness value is compared with that of the other, if it is better, it is used as the current optimal position, and the individual extreme value is:

[0049] P i =(P i1 , P i2 ,…,P iD ) T

[0050] The individual extreme value is the initial rod length parameter optimization value under the current dimension;

[0051] For each particle, its fitness value and the global optimal position P gd The fitness value is compared with that of the other, and if it is better, it is used as the current global best position; the global extreme value is:

[0052] P g =(P g1 , P g2 ,…,P gD ) T

[0053] The global extreme value is the initial rod length parameter optimization value;

[0054] Update the velocity and position of the particle, and the velocity is updated as follows:

[0055]

[0056] Among them, c1 and c2 are two learning factors, r1 and r2 are random numbers uniformly distributed between 0 and 1;

[0057] The location is updated to:

[0058] x i.j (t+1)=x i.j (t)+v i.j (t+1), j=1, ..., D

[0059] The speed is updated to:

[0060] v i.j =δ{v i.j (t)+c1r1[p i.j -x i，j (t)]+c2r2[p g，j -x i.j (t)]}

[0061] Where δ is the shrinkage factor, expressed as:

[0062]

[0063] Where, C = c1 + c2, and C>4;

[0064] When the number of iterations reaches 200, the optimal value of the rod length parameter is output; otherwise, the fitness value of each particle is recalculated.

[0065] The present invention also provides a planar four-link parameter optimization system based on an improved GA-PSO algorithm, the system being a system used by the aforementioned planar four-link parameter optimization method based on an improved GA-PSO algorithm, comprising:

[0066] The optimization model module is used to establish the connecting rod length and the rocker arm length as variables, establish the minimum square deviation of the mechanism output angle as the objective function, and establish the constraint conditions with the transmission angle and the rod length as the target;

[0067] The GA-PSO variable parameter optimization model module is used to optimize parameter variables using the shrinkage factor and the GA-PSO algorithm.

[0068] The present invention also provides a terminal, including a processor and a storage medium; the characteristics are:

[0069] The storage medium is used to store instructions;

[0070] The processor is configured to operate according to the instructions to execute the steps according to the aforementioned method.

[0071] The present invention also provides a computer-readable storage medium on which a computer program is stored, characterized in that the program implements the steps of the above method when executed by a processor.

[0072] The beneficial effect of the present invention is that, compared with the prior art, the present invention proposes a parameter optimization method for the planar four-bar transmission mechanism of the disconnector based on the improved GA-PSO algorithm. For the original PSO model, by introducing a contraction factor and selecting appropriate parameters, the convergence of the PSO algorithm is ensured, and the boundary limit on the speed is cancelled. At the same time, in order to avoid the premature phenomenon that causes a large deviation in the solution of the function extreme value when the improved PSO algorithm calculates the function extreme value, the GA algorithm is introduced, and the selection, crossover and mutation operator operations are used for function optimization. The objective function is directly used as the search information, and it is performed in a probabilistic manner, which enhances the global optimization ability, accelerates the evolution speed of the algorithm, and improves the convergence accuracy, thereby accurately optimizing the mechanism parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1It is a flow chart of the planar four-link parameter optimization method based on the improved GA-PSO algorithm in the present invention;

[0074] Figure 2 It is a simplified schematic diagram of the planar four-bar linkage transmission mechanism of the isolating switch in the present invention;

[0075] Figure 3 is the crank angle of the plane four-bar transmission mechanism of the isolating switch in the present invention Schematic diagram of actual input angle when ;

[0076] Figure 4 is the crank angle of the plane four-bar transmission mechanism of the isolating switch in the present invention Schematic diagram of actual input angle when ;

[0077] Figure 5 Schematic diagram of minimum and maximum transmission angles of the planar four-bar transmission mechanism of the disconnector in the present invention;

[0078] Figure 6 It is a schematic diagram of the design plane of the constraint conditions of the planar four-bar linkage transmission mechanism of the disconnector in the present invention;

[0079] Figure 7 This is a schematic diagram of the improved GA-PSO algorithm flow in the present invention;

[0080] Figure 8 Schematic diagram of the convergence curves of the five algorithm models in the present invention. DETAILED DESCRIPTION

[0081] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0082] like Figure 1 As shown, embodiment 1 of the present invention provides a planar four-link parameter optimization method based on an improved GA-PSO algorithm, comprising:

[0083] Step 1, establish the corresponding relationship between the driving member and the driven member of the planar four-bar linkage transmission mechanism of the disconnector, and establish the variables as the connecting rod length and the rocker length according to the independent parameters;

[0084] The simplified diagram of the plane four-bar transmission mechanism of the isolating switch is as follows Figure 2As shown. A, B, C, and D are different hinges, AB is the crank, AD is the frame, BC is the connecting rod, and CD is the rocker. Their relative lengths are l1, l4, l2, and l3 respectively. B' and C' are the positions of B and C when the planar four-bar linkage is at the right extreme position. When the angle of rotation of the crank AB is When the rocker output angle is required to realize the function in ψ0 is the initial position angle of crank AB and rocker CD when the corresponding rocker is in the right limit position. The counterclockwise measurement of the frame AD direction is used as the standard direction. The transmission angle range of the mechanism should meet the following requirements: 45°≤γ≤135°. Since there are multiple length combinations under certain four-bar ratios, to determine whether the design of the planar four-bar mechanism is reasonable, the four-bar mechanism needs to meet the following conditions:

[0085] (1) In a crank-rocker mechanism, the crank is the shortest rod;

[0086] (2) The sum of the lengths of the shortest and longest rods is less than or equal to the sum of the lengths of the remaining two rods.

[0087] The independent parameters of the planar four-bar linkage according to the corresponding relationship between the prime mover and the follower are l2, l3, l4, ψ0 and other 5. Figure 2 It can be seen that if the acute angle between the two connecting rods and the frame at the right limit position of the mechanism is selected as the initial position angle ψ0 can be determined according to the geometric relationship of the position and is no longer an independent parameter. For the convenience of calculation, the relative length l1 of the crank AB and the relative length l4 of the frame AD are selected as specific values ​​in the actual verification. Therefore, the connecting rod length l2 and the rocker length l3 of the mechanism are selected as design variables.

[0088] Step 2: Establish the minimum square deviation of the output angle of the mechanism as the design goal and establish the objective function, establish constraint conditions with the transmission angle and rod length as the goal, and thus construct an optimization model;

[0089] like Figure 2 As shown, ψ0 can be determined based on the kinematic geometry of the planar four-bar linkage at a special position:

[0090]

[0091] The design variables can be obtained as:

[0092]

[0093] The goal of the optimization design is to make the actual output angle as close to the expected output angle as possible, that is, to minimize the square deviation of the mechanism output angle as the design goal, so the objective function is:

[0094]

[0095] in:

[0096]

[0097] In the formula, ψ i is the desired output angle, s is the crank angle In the interval The number of discrete points uniformly distributed within, i is the serial number of each discrete point. si is the actual output angle, according to Figure 3 , 4 As shown, the expression of the actual output angle can be determined by the kinematic geometric relationship of the mechanism:

[0098]

[0099] α in formula (7) i and β i l3 and r respectively i The angle and r i and l4, where r i is the relative length of the auxiliary line BD, which refers to the distance between hinges B and D. According to the cosine theorem of ΔΔBDC and ΔABD, we get:

[0100]

[0101] The constraints are established as follows:

[0102] 1) Transmission angle constraints

[0103] In order to ensure good force transmission performance of the mechanism, the minimum transmission angle of the planar four-bar transmission mechanism must satisfy γ min ≥45° and the maximum transmission angle meets γ max ≥135°.

[0104] When the crank AB is in the same line with the frame AD, the mechanism has the minimum or maximum transmission angle, such as Figure 5 As shown. According to the cosine theorem of the mechanism at these two positions:

[0105]

[0106] After sorting, the constraint equation is:

[0107]

[0108] 2) Constraints on rod length

[0109] According to the sum of the lengths of the cranks in the planar four-bar linkage, the constraint equation can be obtained by rearranging:

[0110] l2≥l1,g3(X)=x1-l1≥0 (15)

[0111] l3≥l1, g4(X)=x2-l1≥0 (16)

[0112] l2+l3≥l1+l4,g s (X)=x1+x2-(l1+l4)≥0 (17)

[0113] l4+l3≥l1+l2, g6(x)=-x1+x2+(l4-l1)≥0 (18)

[0114] l4+l2≥l1+l3, g7(X)=x1-x2+(l4-l1)≥0 (19) According to the above constraint equation, draw the design plane associated with the constraint equation, such as Figure 6 As shown. Figure 6 It can be seen that the constraint condition of the sum of the rod lengths of the crank is a non-effective constraint for this optimization design problem. The effective constraints are the mechanism transmission angle constraints g1(X)≥0 and g2(X)≥0, which form a feasible domain.

[0115] In summary, the mathematical model of the optimization problem is:

[0116]

[0117] Step 3, based on variables, objective functions and constraints, the PSO algorithm with added shrinkage factor model and GA algorithm optimization is introduced to optimize the variable parameters;

[0118] Step 3.1, the basic principle of the PSO algorithm includes the update of the particle's velocity and position. Each particle represents a potential solution in the problem solution space, and the solution space is explored by continuously updating the velocity and position. The velocity update formula takes into account the particle's own optimal position and the global optimal position, and the position update is calculated based on the velocity and current position. Through multiple iterations, the particle gradually approaches the optimal solution.

[0119] In the problem of optimizing the length of a four-link, the PSO algorithm first initializes a particle swarm, where each particle represents a pair of rod length solutions. By iteratively updating the speed and position of the particles, the algorithm continuously optimizes the length combination of the four-link until the fitness function is met or the maximum number of iterations is reached.

[0120] Initialize the PSO algorithm:

[0121] Initialize a particle swarm of size N, and randomly initialize the position and velocity of the particles: for any i, d, in [x min ,x max ] follows uniform distribution to generate x id ; For any i,d, in [v min ,v max ] follows uniform distribution to generate V id . Where i = 1, ..., N, d = 1, ..., D, x min is the minimum particle position preset by the algorithm, x max is the maximum particle position preset by the algorithm, v min The minimum particle velocity preset by the algorithm, v max is the maximum particle speed preset by the algorithm, the x id Refers to the value of the i-th particle of the d-dimensional vector, where d refers to the d-th dimension value of the D-dimensional vector, V id It refers to the velocity of the ith particle of the d-dimensional vector. The formula is as follows:

[0122] The population X consists of N particles:

[0123] X=(X1,X2,…,X N ) (twenty one)

[0124] The i-th particle in population X represents a D-dimensional vector:

[0125] X i =(x i1 ,x i2 ,…,x iD ) T (twenty two)

[0126] The velocity of the ith particle is:

[0127] V i =(V i1 ,V i2 ,…,V iD ) T (twenty three)

[0128] During initialization, the position of the random particle is set as the optimal position of the current particle, the objective function is used as the fitness function, and the optimal position of the current particle is substituted into the fitness function to calculate the fitness value.

[0129] Step 3.2: For each particle, its fitness value and the best position P it has ever experienced are calculated. id The fitness value of is compared, and if it is better, it is used as the current optimal position. The individual extreme value is:

[0130] P i =(P i1,P i2 ,…,P iD ) T (twenty four)

[0131] The individual extreme value is the optimal solution for the particle position in the current dimension, that is, the initial optimized value of the rod length parameter in the current dimension.

[0132] Step 3.3: For each particle, its fitness value and the global optimal position P gd The fitness value of is compared, and if it is better, it is used as the current global best position. The global extreme value is:

[0133] P g =(P g1 ,P g2 ,…,P gD ) T (25)

[0134] The global extreme value is the optimal solution for the particle position in all dimensions, that is, the optimization value of the initial rod length parameter in all dimensions.

[0135] Step 3.4, update the velocity and position of the particle, the velocity is updated as:

[0136]

[0137] Among them, c1 and c2 are two positive learning factors, or acceleration constants, and r1 and r2 are random numbers uniformly distributed between 0 and 1.

[0138] The location is updated to:

[0139]

[0140] In each iteration, the objective function of each particle is evaluated to determine the optimal position p of each particle at time t. best and the best position g found by the group best By tracking these two optimal positions, the speed and position of each particle are updated according to equations (28) and (29).

[0141] v i.j (t+1)=ωv i.j (t)+c1r1[p i.j -x i.j (t)]+c2r2[p g.j -x i.j (t)] (28)

[0142] x i.j (t+1)=x i.j (t)+v i.j(t+1),j=1,…,D (29)

[0143] In the formula, ω is the inertia weight coefficient, which is used to control and adjust the particle flight speed. In addition, by setting the particle speed range [v min ,v max ] and the position range [x min ,x max ]The movement of particles can be appropriately restricted.

[0144] The PSO algorithm includes global optimization and local optimization. For global extreme value optimization, the two extreme values ​​tracked by the particle are its own optimal position and the group's optimal position. For local extreme value optimization, in addition to tracking its own optimal position, the particle does not track the group's optimal position, but tracks the optimal position of all particles in the topological neighborhood. Its speed update is shown in formula (30):

[0145] v i.j (t+1)==ωv i.j (t)+c1r1[p i.j -x i,j (t)]+c2r2[p 1，j -x i.j (t)] (30)

[0146] Where P i =[p i，1 , p i，2 , ..., p i，d ] is the best position in the local neighborhood.

[0147] The learning factors c1 and c2 determine the influence of the particle's own experience information and the experience information of other particles on the particle's trajectory, reflecting the information exchange between the particle group. Setting a larger value for c1 will cause the particles to wander too much in the local range, while a larger c2 value will cause the particles to converge to the local minimum too early. The particles effectively control the flight speed of the particles, so that the algorithm can achieve an effective balance between global detection and local mining. At this time, the contraction factor model is introduced. By selecting appropriate parameters, the convergence of the PSO algorithm can be ensured, and the boundary limit on the speed can be removed. The speed formula is shown in formula (31):

[0148] v i.j =δ{v i.j (t)+c1r1[p i.j -x i，j (t)]+c2r2[p g，j -x i.j (t)]} (31)

[0149] Where δ is the shrinkage factor, expressed as:

[0150]

[0151] In the formula, C=c1+c2, and C>4.

[0152] The role of δ in this model is similar to that of parameter V max , but can more effectively control the vibration of particle velocity and enhance the local search capability of the algorithm.

[0153] Finally, equation (31) and equation (29) are used to update the particle velocity and position respectively.

[0154] Step 3.5: When the number of iterations reaches 200, the optimal value of the rod length parameter is output; otherwise, the fitness value of each particle is recalculated.

[0155] The termination condition of the iteration can be adaptively adjusted according to actual operation conditions.

[0156] Furthermore, when the PSO algorithm calculates the extreme value of a function, premature phenomenon often occurs, resulting in a large deviation in solving the extreme value of the function. However, the GA algorithm uses crossover and mutation operator operations for function optimization, directly uses the objective function as search information, and performs it in a probabilistic manner, thereby enhancing the global optimization ability of the PSO algorithm, accelerating the evolution of the algorithm, and improving the convergence accuracy. The formula for calculating the extreme value part of the GA algorithm instead of the original PSO algorithm is as follows:

[0157] The crossover operation is:

[0158]

[0159] Where: and represents any two initial individuals; and represents the new individual after crossover; r represents a random number between the interval [0,1].

[0160] The mutation operation formula is:

[0161]

[0162] Where: A min , A max Represents the minimum and maximum values ​​of individual particles; a represents an adjustable parameter; t represents the current number of iterations; and T represents the maximum number of iterations.

[0163] Step 4, set the values ​​of discrete points s, crank length l1 and frame length l4, and set the initial value of the sum of square errors of the objective function output angle to zero. For the convenience of calculation, in the actual verification, the relative length of the crank l1 is selected as 100, the relative length of the frame l4 is selected as 500, the number of discrete points s is selected as 30, and the optimal values ​​of the connecting rod length and rocker length l2 and l3 are output as the optimized parameters. Finally, the improved hybrid algorithm process is obtained as follows Figure 7 shown.

[0164] The initial parameter settings of the improved GA-PSO algorithm are shown in Table 1.

[0165] Table 1 Initial parameters of improved GA-PSO algorithm

[0166]

[0167] The improved GA-PSO algorithm, GA-PSO algorithm, improved PSO algorithm, PSO algorithm and GA algorithm are used to optimize the length of the connecting rod and the rocker of the planar four-bar linkage, and the length of each rod is controlled to meet the constraints. The convergence characteristic curves based on different algorithms are as follows: Figure 8 shown.

[0168] Figure 8 The curves in show that compared with the GA-PSO algorithm, GA algorithm, improved PSO algorithm and PSO algorithm, the algorithm proposed in this paper converges faster and obtains the optimal solution with higher accuracy. Table 2 shows the optimal lengths and optimal fitness values ​​of the two rods obtained by each algorithm.

[0169] Table 2 Optimal parameters obtained by each algorithm

[0170]

[0171] In order to compare the performance of each algorithm in solving the optimization problem, each algorithm was run independently 30 times, and the average, minimum, maximum and standard deviation of the optimal fitness value were calculated as shown in Table 3.

[0172] Table 3 Performance comparison of various algorithms

[0173]

[0174] As can be seen from Table 3, the average value, maximum value and standard deviation obtained by the proposed algorithm are the smallest, indicating that the optimal solution accuracy, convergence and robustness of the algorithm are better than those of several other algorithms, which also verifies the superiority of the proposed algorithm in mechanism parameter optimization.

[0175] Embodiment 2 of the present invention provides a planar four-link parameter optimization system based on an improved GA-PSO algorithm, and the system is a system used by the aforementioned planar four-link parameter optimization method based on an improved GA-PSO algorithm, and is characterized in that it includes:

[0176] The optimization model module is used to establish the connecting rod length and the rocker arm length as variables, establish the minimum square deviation of the mechanism output angle as the objective function, and establish the constraint conditions with the transmission angle and the rod length as the target;

[0177] The GA-PSO variable parameter optimization model module is used to optimize parameter variables using the shrinkage factor and the GA-PSO algorithm.

[0178] The beneficial effect of the present invention is that, compared with the prior art, the present invention proposes a parameter optimization method for the planar four-bar transmission mechanism of the disconnector based on the improved GA-PSO algorithm. For the original PSO model, by introducing a contraction factor and selecting appropriate parameters, the convergence of the PSO algorithm is ensured, and the boundary limit on the speed is cancelled. At the same time, in order to avoid the premature phenomenon that causes a large deviation in the solution of the function extreme value when the improved PSO algorithm calculates the function extreme value, the GA algorithm is introduced, and the selection, crossover and mutation operator operations are used for function optimization. The objective function is directly used as the search information, and it is performed in a probabilistic manner, which enhances the global optimization ability, accelerates the evolution speed of the algorithm, and improves the convergence accuracy, thereby accurately optimizing the mechanism parameters.

[0179] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0180] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0181] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0182] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0183] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A planar four-link parameter optimization method based on an improved GA-PSO algorithm, characterized in that: include: Determine the correspondence between the driving member and the driven member of the planar four-bar transmission mechanism of the disconnector, obtain the variables in the optimization process as the connecting rod length and the rocker length according to the structural characteristics of the transmission mechanism, and build the objective function of the optimization process; establish the constraints of the transmission angle and the rod length, and build the optimization model; build a GA-PSO variable parameter optimization model, and use the contraction factor model to improve it; use the optimization model to perform preliminary optimization and obtain the preliminary rod length parameter optimization value, use the GA-PSO model to perform optimization, and output the optimal values ​​of the connecting rod length and the rocker length.

2. The method for optimizing the parameters of a planar four-linkage based on an improved GA-PSO algorithm according to claim 1, characterized in that: The corresponding relationship between the driving member and the driven member of the planar four-bar transmission mechanism of the disconnector includes: the relative length of the crank AB is l1, the relative length of the frame AD is l4, the relative length of the connecting rod BC is l2, and the relative length of the rocker CD is l3; when the rocker is in the right limit position, the initial position angle of the crank AB is l1. When the joystick is at the right limit position, the initial position angle of the joystick CD The design variables are 3. The method for optimizing the parameters of a planar four-linkage based on an improved GA-PSO algorithm according to claim 2, characterized in that: The objective function of the construction optimization process includes: Expected output angle Angle of crank AB s is the crank angle In the interval The number of discrete points in, i is the serial number of each discrete point, The objective function is ψ si is the actual output angle.

4. The method for optimizing the parameters of a planar four-linkage based on an improved GA-PSO algorithm according to claim 3 is characterized in that: The actual output angle where α i and β i l3 and r respectively i The angle and r i and l4, where r i is the distance between hinges B and D; 5. The method for optimizing the parameters of a planar four-linkage based on an improved GA-PSO algorithm according to claim 4, characterized in that: The constraints of the transmission angle include: Minimum transmission angle γ min ≥45°, maximum transmission angle γ max ≥135°, The constraint equation of the transmission angle is The rod length constraints include: l2≥l1,g3(X)=x1-l1≥0 l3≥l1,g4(X)=x2-l1≥0 l2+l3≥l1+l4,g5(X)=x1+x2-(l1+l4)≥0 l4+l3≥l1+l2,g6(X)=-x1+x2+(l4-l1)≥0 l4+l2≥l1+l3, g7(X)=x1-x2+(l4-l1)≥0.

6. The method for optimizing the parameters of a planar four-linkage based on an improved GA-PSO algorithm according to claim 5, characterized in that: The optimization model is The fitness function is the objective function X∈D.

7. The method for optimizing the parameters of a planar four-linkage based on an improved GA-PSO algorithm according to claim 6, characterized in that: The GA-PSO model is used to optimize the rod length parameters. Including, initializing the PSO algorithm, initializing a particle swarm of size N, and randomly initializing the position and velocity of the particles: for any i, d, in [x min ,x max ] follows uniform distribution to generate x id ; For any i,d, in [v min ,v max ] follows uniform distribution to generate V id ; where i = 1, ..., N, d = 1, ..., D, x min is the minimum particle position preset by the algorithm, x max is the maximum particle position preset by the algorithm, v min The minimum particle speed preset by the algorithm, v max is the maximum particle speed preset by the algorithm, the x id Refers to the value of the i-th particle of the d-dimensional vector, where d refers to the d-th dimension value of the D-dimensional vector, V id refers to the velocity of the ith particle of the d-dimensional vector; The population X consists of N particles: X=(X1,X2,…,X N ) The i-th particle in population X represents a D-dimensional vector: X i =(x i1 ,x i2 ,…,x iD ) T The velocity of the ith particle is: V i =(V i1 ,V i2 ,…,V iD ) T During initialization, the position of the random particle is set to the optimal position of the current particle, the objective function is used as the fitness function, and the optimal position of the current particle is substituted into the fitness function to calculate the fitness value; For each particle, its fitness value and the best position P it has ever experienced id The fitness value is compared with that of the other, if it is better, it is used as the current optimal position, and the individual extreme value is: P i =(P i1 ,P i2 ,…,P iD ) T The individual extreme value is the initial rod length parameter optimization value under the current dimension; For each particle, its fitness value and the global optimal position P gd The fitness value is compared with that of the other, and if it is better, it is used as the current global best position; the global extreme value is: P g =(P g1 ,P g2 ,…,P gD ) T The global extreme value is the initial rod length parameter optimization value; Update the velocity and position of the particle, and the velocity is updated as follows: Among them, c1 and c2 are two learning factors, r1 and r2 are random numbers uniformly distributed between 0 and 1; The location is updated to: x i.j (t+1)=x i.j (t)+v i.j (t+1),j=1,…,D The speed is updated to: v i.j =δ{v i.j (t)+c1r1[p i.j -x i,j (t)]+c2r2[p g,j -x i.j (t)]} Where δ is the shrinkage factor, expressed as: Wherein, C = c1 + c2, and C > 4; When the number of iterations reaches 200, the optimal value of the rod length parameter is output; otherwise, the fitness value of each particle is recalculated.

8. A planar four-link parameter optimization system based on an improved GA-PSO algorithm, the system being a system used in a planar four-link parameter optimization method based on an improved GA-PSO algorithm as claimed in any one of claims 1 to 7, characterized in that: include: The optimization model module is used to establish the connecting rod length and the rocker arm length as variables, establish the minimum square deviation of the mechanism output angle as the objective function, and establish the constraint conditions with the transmission angle and the rod length as the target; The GA-PSO variable parameter optimization model module is used to optimize parameter variables using the shrinkage factor and the GA-PSO algorithm.

9. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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