Harvester cross-region operation path optimization method based on intelligent optimization algorithm

Abstract

The invention discloses a harvester cross-regional operation path optimization method based on an intelligent optimization algorithm. The method comprises the following steps: (1) setting parameters; (2) coding mode: adopting a real number coding mode based on a hybrid improved real number coding emperor penguin optimization algorithm; (3) a decoding mode; (4) outputting the optimal operation path and the cross-regional operation length of the N operation points, ending circulation, and otherwise, entering the next step; (5) selecting a self-adaptive disturbance search strategy for judging whether to oscillate the individual with the minimum fitness value or not; (6) selecting to carry out a random strategy or a dynamic movement strategy; (7) executing local search operation to improve the convergence speed and the solving quality of the algorithm; and (8) updating the optimal individual and population, and returning to the step (4). The method combines the advantages of various intelligent optimization algorithms, and aims to improve the global and local search capabilities of the algorithms, thereby improving the solving quality and efficiency of the cross-regional operation path optimization problem of the harvester.

Description

Technical Field

[0001] The invention relates to the technical field of harvester path planning, and in particular to a harvester cross-region operation path optimization method based on an intelligent optimization algorithm. Background Art

[0002] Although traditional path planning methods, such as genetic algorithms, particle swarm optimization, and ant colony algorithms, have achieved certain results in some applications, they still have some limitations when solving the problem of cross-regional operation path optimization of harvesters. For example, these methods may fall into local optimality or fail to find solutions that meet actual needs in large-scale agricultural machinery scheduling.

[0003] In order to solve the above problems, researchers began to explore new optimization algorithms and methods. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method for optimizing the cross-regional operation path of a harvester based on an intelligent optimization algorithm, which improves the global and local search capabilities of the algorithm and thereby improves the solution quality and efficiency of the cross-regional operation path optimization problem of the harvester.

[0005] The technical problem to be solved by the present invention is achieved through the following technical solutions. The present invention is a method for optimizing the cross-region operation path of a harvester based on an intelligent optimization algorithm, and the steps of the method are as follows:

[0006] (1) Parameter settings:

[0007] The population size of the real number coding emperor penguin optimization algorithm based on hybrid improvement is n, the number of iterations of the algorithm is 200, the initial number of iterations gen = 0, the number of cross-region operation points is N, and the objective function of the cross-region operation path planning problem of the harvester with N operation points is expressed as:

[0008]

[0009] Where f(D) is the shortest distance for the harvester to operate across regions; t i represents the i-th operation point; d(t i ,t i+1 ) represents the distance between the i-th operating point and the i+1-th operating point, which is expressed by the following formula:

[0010]

[0011] (x i ,y i ) and (x i+1 ,y i+1 ) are t i , t i+1The coordinates of

[0012] (2) Encoding method:

[0013] The emperor penguin optimization algorithm based on hybrid improved real number coding adopts real number coding;

[0014] The number of operation points of the harvester's cross-region operation is N, so the individual initial

[0015] X=N*rand(1,N) (3)

[0016] Among them, X is an individual with a code length of N and a code value of a floating point number;

[0017] Generate n individuals using formula (3);

[0018] (3)Decoding method:

[0019] Take the kth individual X in the population k =(x1,x2,…,x N ) as an example, the specific decoding steps are as follows:

[0020] (3.1) Randomly generate an integer μ between [1, N] and use μ as the starting point;

[0021] (3.2) Delete X k The vector after the μth component in is X'k, X'k=(x1,x2,…,x μ-1 ,x μ+1 ,…,x N ), according to the distance between μ and other working points d = (d μ,1 ,d μ,2 ,…,d μ,μ-1 ,d μ,μ+1 ,…,d μ,N ), calculate the vector Tp related to the distance between the operation points and the size of the gene position value. The calculation formula of Tp is

[0022]

[0023] In the formula, represents the vector d and the vector Multiply elements at the same position;

[0024] (3.3) Find the minimum value in the vector Tp, and take the operation point i corresponding to the minimum value as the next operation point;

[0025] (3.4) Let μ = i, repeat Step 2 and Step 3 to determine the visit order of all work points;

[0026] Decode n individuals according to formula (4), calculate the fitness values of the n individuals according to formula (1), and sort them in ascending order;

[0027] (4) If gen meets the maximum iteration condition, output the optimal job path and the cross-region operation length of N job points, end the loop, otherwise, go to the next step;

[0028] (5) Select whether to perform a shock adaptive perturbation search strategy on the individual with the smallest fitness value. The calculation formula for the selection probability is as follows:

[0029]

[0030] In the formula, t is the current iteration number, and Maxgen is the maximum iteration number of the algorithm;

[0031] Randomly generate a random number between [0, 1]. If the random number is less than Pr1, perform a shock adaptive perturbation search strategy on the optimal individual, otherwise, go to the next step;

[0032] (6) Select to perform a random strategy or a dynamic movement strategy. The calculation formula for the selection probability is as follows:

[0033]

[0034] In the formula, t is the current iteration number, and Maxgen is the maximum iteration number of the algorithm;

[0035] Randomly generate a random number between [0, 1]. If the random number is greater than Pr2, perform a dynamic movement strategy on the population individuals, otherwise, execute the random strategy;

[0036] (7) Perform a local search operation

[0037] Randomly generate a random number m between [0, 1]. If m < P, execute the 2-Opt algorithm; otherwise, execute an adaptive combined perturbation; if the adaptive combined perturbation strategy is executed, then randomly generate a random number μ between [0, 1]. If μ < P s , execute the double bridge; otherwise, execute the neighborhood 2-opt operator. The calculation formulas for P and P s are as shown below:

[0038]

[0039]

[0040] In the formula, t is the current running time, and t max is the maximum running time of the example, and P min is the minimum selection probability, and P max is the maximum selection probability, and P min=0.25, P max =0.7;

[0041] (8) Update the optimal individuals and populations, and return to (4).

[0042] The technical problem to be solved by the present invention can also be further achieved by the following technical solutions. For the above-mentioned harvester cross-region operation path optimization method based on the intelligent optimization algorithm, in step (5), the update formula of the oscillating adaptive disturbance search strategy is as follows:

[0043]

[0044]

[0045]

[0046]

[0047] In the formula, Xb(t) is the optimal individual, t is the current iteration number, N is the number of harvester operation points, q is a randomly generated N-dimensional 0-1 row vector, Maxgen is the maximum number of iterations of the algorithm, β is the disturbance term, and λ is the adaptive change amount. Represents the multiplication of the elements at the same position of two vectors.

[0048] The technical problem to be solved by the present invention can also be further achieved by the following technical solutions. For the above-mentioned harvester cross-region operation path optimization method based on the intelligent optimization algorithm, in step (6), the update formula of the dynamic movement strategy is as follows:

[0049]

[0050]

[0051] Where N is the total number of operating points of the harvester, and Xm r1(t-1) and Xm r2(t-1) are the optimal positions of two randomly selected emperor penguins.

[0052] The technical problem to be solved by the present invention can also be further achieved by the following technical solutions. For the above-mentioned harvester cross-region operation path optimization method based on the intelligent optimization algorithm, in step (6), the update formula of the random strategy is as follows:

[0053] X i (t) = X i (t-1)+A i (t) (15)

[0054]

[0055]

[0056]

[0057] Where Xr(t-1) is an individual randomly selected from the population, and Xmi(t-1) is the optimal position in the memory of the i-th individual Xi(t-1) in the population.

[0058] The technical problem to be solved by the present invention can also be further achieved by the following technical solutions. For the above-mentioned harvester cross-region operation path optimization method based on the intelligent optimization algorithm, in step (7), the specific steps of the neighborhood 2-opt operator are as follows:

[0059] Take a certain circuit X in the population = (x1, x2, ..., x N ) as an example, N is the number of operation points,

[0060] Step 1: Randomly select a work point a from N work points, take a as the center and a circle with a radius of r1 as the neighborhood, denoted as U(a,r1), and the calculation formula of r1 is as shown in formula (19):

[0061]

[0062] In the formula, Z is the route length of the optimal individual in the population;

[0063] Step 2: Determine the work point u contained in the neighborhood U(a,r1). If u≥2, randomly select two work points aa1 and aa2 in the neighborhood. If u=1, let aa1=a, and then randomly select a work point in [1,2,…,a-1,a+1,…,N] as aa2.

[0064] Step 3: Find the operating point bb1 adjacent to aa1 and the operating point bb2 adjacent to aa2 in X, and bb1≠aa2, bb2≠aa1. If bb1=bb2, repeat Step 1 and Step 2 until bb1≠bb2.

[0065] Step 4: Delete the edges (aa1, bb1) and (aa2, bb2), and connect the edges (bb1, aa2) and (aa1, bb2).

[0066] The technical problem to be solved by the present invention can also be further achieved by the following technical solutions. For the above-mentioned harvester cross-region operation path optimization method based on the intelligent optimization algorithm, in step (7), the specific steps of the double bridge movement are as follows:

[0067] Take the individuals in the population X=(x1,x2,…,x N )(N is the number of operation points) as an example,

[0068] Step 1: Generate a random integer a1 in [2,N-6];

[0069] Step 2: Generate a random integer k0 according to [2+a1,N-4];

[0070] Step 3: Generate a random integer k1 according to [2+k0,N-2];

[0071] Step 4: Generate a random integer k2 according to [2+k1,N];

[0072] Step 5: Sort k0, k1, k2 from small to large, let a2 = min(k0, k1, k2), a3 = median(k0, k1, k2), a4 = max(k0, k1, k2);

[0073] Step 6: Let b1 = a1-1, b2 = a2-1, b3 = a3-1, b4 = a4-1;

[0074] Step 7: a1, b1, a2, b2, a3, b3, a4 and b4 are the index numbers of each component in individual X. Let the operation points corresponding to the index numbers a1, b1, a2, b2, a3, b3, a4 and b4 in X be aa1, bb1, aa2, bb2, aa3, bb3, aa4 and bb4 respectively;

[0075] Step 8: Delete the edges (aa1,bb1), (aa2,bb2), (aa3,bb3), (aa4,bb4) and reconnect the edges (aa1,bb3), (aa2,bb4), (aa3,bb1) and (aa4,bb2).

[0076] The technical problem to be solved by the present invention can also be further achieved through the following technical solutions. For the above-mentioned harvester cross-region operation path optimization method based on the intelligent optimization algorithm, in step (7), the 2-Opt algorithm is a local optimization algorithm. Specifically, for each route in the population, two non-adjacent edges in the given route are exchanged in turn to obtain a set of all paths, and the exchange that can improve the path is retained.

[0077] Compared with the prior art, the present invention can provide high-quality solutions in practical applications to meet the needs of different users and scenarios, and its beneficial effects are:

[0078] 1. Balance between global and local search capabilities: The algorithm proposed in this invention combines the advantages of global search and local search, ensuring that in the process of finding the optimal solution, it can not only explore the solution space extensively, but also find a better solution in a certain area in depth;

[0079] 2. Efficient convergence speed: Compared with other algorithms, the algorithm of the present invention shows a faster convergence speed in most cases, which means that the algorithm can find a better or near-optimal solution in a shorter time;

[0080] 3. Strong adaptability: By adjusting parameters in an adaptive way, the algorithm can perform well in different problem instances and different situations. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 is an algorithm flow chart of the present invention;

[0082] Figure 2 A schematic diagram of a neighborhood 2-opt operator method of the present invention;

[0083] Figure 3 It is a schematic diagram of the method of double bridge movement of the present invention. DETAILED DESCRIPTION

[0084] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0085] Reference Figure 1 , a method for optimizing the cross-region operation path of a harvester based on an intelligent optimization algorithm, the steps are as follows:

[0086] (1) Parameter settings:

[0087] The population size of the real number coding emperor penguin optimization algorithm based on hybrid improvement is n, the number of iterations of the algorithm is 200, the initial number of iterations gen=0; the number of cross-region operation points is N; there are N operation points

[0088] The objective function of the cross-region operation path planning problem of the harvester is expressed as:

[0089]

[0090] Where f(D) is the shortest distance for the harvester to operate across regions; t i represents the i-th operation point; d(t i ,t i+1 ) represents the distance between the i-th operating point and the i+1-th operating point, which is expressed by the following formula:

[0091]

[0092] (x i ,y i ) and (x i+1 ,y i+1 ) are t i , t i+1 The coordinates of

[0093] (2) Encoding method:

[0094] The emperor penguin optimization algorithm based on hybrid improved real number coding adopts real number coding; the number of operation points of the harvester's cross-region operation is N, and the individual initial

[0095] X=N*rand(1,N) (3)

[0096] Among them, X is an individual with a code length of N and a code value of a floating point number;

[0097] Generate n individuals using formula (3);

[0098] (3)Decoding method:

[0099] Take the kth individual X in the population k =(x1,x2,…,x N ) as an example, the specific decoding steps are as follows:

[0100] 1) Randomly generate an integer μ between [1, N] and use μ as the starting point;

[0101] 2) Delete X k The vector after the μth component in is X'k, X'k=(x1,x2,…,x μ-1 ,x μ+1 ,…,x N ), according to the distance between μ and other working points d = (d μ,1 ,d μ,2 ,…,d μ,μ-1 ,d μ,μ+1 ,…,d μ,N ), calculate the vector Tp related to the distance between the operation points and the size of the gene position value. The calculation formula of Tp is

[0102]

[0103] In the formula, represents the vector d and the vector Multiply elements at the same position;

[0104] 3) Find the minimum value in the vector Tp, and take the operation point i corresponding to the minimum value as the next operation point;

[0105] 4) Let μ = i, repeat Step 2 and Step 3, and determine the access order of all work points.

[0106] In order to facilitate the understanding of the solution method based on distance and place value, take N = 5 as an example, assuming μ = 2, X k =(1.3575, 4.5155, 3.4863, 0.7845, 0.0637), the distances between the five operating points are shown in Table 1, and the specific decoding steps are shown in Table 2.

[0107] Table 1 Distances between the five working points

[0108]

[0109] Table 2 Decoding example

[0110]

[0111] Decode n individuals using formula (4), calculate the fitness values ​​of n individuals using formula (1), and sort them in order from small to large;

[0112] (4) If gen meets the maximum iteration condition, output the optimal operation path and the cross-zone operation length of N operation points, and end the loop; otherwise, proceed to the next step;

[0113] (5) Choose whether to oscillate the adaptive perturbation search strategy for the individual with the smallest fitness value. The calculation formula for the selection probability is as follows:

[0114]

[0115] In the formula, t is the current iteration number, and Maxgen is the maximum iteration number of the algorithm;

[0116] Generate a random number between [0,1]. If the random number is less than Pr1, perform the oscillating adaptive perturbation search strategy on the optimal individual. Otherwise, proceed to the next step.

[0117] Among them, the update formula of the oscillating adaptive perturbation search strategy is as follows:

[0118]

[0119]

[0120]

[0121]

[0122] In the formula, Xb(t) is the optimal individual, t is the current iteration number, N is the number of harvester operation points, q is a randomly generated N-dimensional 0-1 row vector, Maxgen is the maximum number of iterations of the algorithm, β is the disturbance term, and λ is the adaptive change amount. Represents the multiplication of the elements at the same position of two vectors;

[0123] The oscillating adaptive perturbation search strategy uses a disturbance term that changes adaptively with the number of iterations to update the optimal individual. It is simple to implement, easy to understand, and has a wide range of applications. It has strong optimization capabilities and the ability to escape from local optimal solutions, which helps to reduce the time complexity of the algorithm and improve the performance of the algorithm.

[0124] (6) Choose to use a random strategy or a dynamic movement strategy. The formula for calculating the selection probability is as follows:

[0125]

[0126] Where t is the current iteration number, and Maxgen is the maximum iteration number of the algorithm.

[0127] A random number is randomly generated between [0, 1]. If the random number is greater than Pr2, a dynamic mobile strategy is used on the individuals in the population. Otherwise, a random strategy is executed.

[0128] The update formula of the dynamic mobility strategy is as follows:

[0129]

[0130]

[0131] Where N is the total number of harvester operation points, Xm r1(t-1) and Xm r2(t-1) are the optimal positions of two randomly selected emperor penguins;

[0132] The dynamic mobility strategy is a flexible strategy that can dynamically adjust the search behavior of individuals according to the current state of the population. By combining local search and global search, the algorithm aims to effectively traverse the complex multi-dimensional search space while avoiding premature aggregation near the local optimal solution, thereby increasing the probability of finding the global optimal solution.

[0133] The update formula of the random strategy is as follows:

[0134] X i (t) = X i (t-1)+A i (t) (13)

[0135]

[0136]

[0137]

[0138] Wherein, Xr(t - 1) is an individual randomly selected from the population, and Xm i(t - 1) is the optimal position in the memory of the i-th individual Xi(t - 1) in the population;

[0139] The random movement strategy combines the current position, the position of a randomly selected individual, and the historical optimal position in the individual's memory. By increasing the guidance of the randomly selected individual position to the individual, the individuals in the population have the ability to search in a random direction in the solution space, which helps the algorithm to explore more widely in the solution space; by dynamically adjusting the weight factors a1 and a2, the algorithm can adjust the behavior of the individuals according to the current state of the population and the characteristics of the solution space, aiming to increase the probability of finding the global optimal solution. In summary, the random movement strategy can enhance the global search ability of the algorithm.

[0140] (7) Perform local search operations to improve the convergence speed and solution quality of the algorithm;

[0141] Randomly generate a random number m in the range of [0, 1]. If m < P, execute the 2-Opt algorithm; otherwise, execute the adaptive combined perturbation. If the adaptive combined perturbation strategy is executed, then randomly generate a random number μ in the range of [0, 1]. If μ < P s , execute the double bridge; otherwise, execute the neighborhood 2-opt operator. The calculation formulas of P and P s are as follows.

[0142]

[0143]

[0144] Wherein, t is the current running time, and t max is the maximum running time of the example, P min is the minimum selection probability, and P max is the maximum selection probability, P min = 0.25, and P max = 0.7;

[0145] 1) Neighborhood 2-opt operator

[0146] To facilitate the description of the neighborhood 2-opt move, taking a certain circuit X = (x1, x2,..., x N ) in the population as an example, where N is the number of job points, the specific steps of the neighborhood 2-opt operator are as follows:

[0147] Step 1: Randomly select a work point a from N work points, take a as the center and a circle with radius r1 as the neighborhood, denoted as U(a,r1). The calculation formula of r1 is as follows:

[0148]

[0149] In the formula, Z is the route length of the optimal individual in the population;

[0150] Step 2: Determine the work point u contained in the neighborhood U(a,r1). If u≥2, randomly select two work points aa1 and aa2 in the neighborhood. If u=1, let aa1=a, and then randomly select a work point in [1,2,…,a-1,a+1,…,N] as aa2.

[0151] Step 3: Find the operating point bb1 adjacent to aa1 and the operating point bb2 adjacent to aa2 in X, and bb1≠aa2, bb2≠aa1. If bb1=bb2, repeat Step 1 and Step 2 until bb1≠bb2.

[0152] Step 4: Delete the edges (aa1, bb1) and (aa2, bb2), and connect the edges (bb1, aa2) and (aa1, bb2).

[0153] In order to explain the neighborhood 2-opt move, we take the operation paths with geometric cross edges and the operation paths without geometric cross edges in the individuals of the population as examples;

[0154] Randomly select an individual X=(1,2,10,9,8,7,6,5,4,3) with geometric cross edges from the population. Suppose the randomly selected operation point from X is 3, and the operation points contained in the neighborhood with 3 as the center and r1 as the radius are 2 and 3, then aa1=2, aa2=3. According to step 3, bb1=10, bb2=11. Delete the edges (aa1,bb1)=(2,10), (aa2,bb2)=(3,11), connect the edges (bb1,aa2)=(10,11) and (aa1,bb2)=(2,3), and the route obtained by individual X and the neighborhood 2-opt operator is as follows Figure 2 a and Figure 2 As shown in b; Figure 2 a and Figure 2 In b, d(2,10)+d(3,11)=96, d(2,3)+d(10,11)=41. The path length after reconnection is better than the path length before perturbation, and the individuals generated by the neighborhood 2-opt operator are better than individual X.

[0155] Randomly select an individual X'=(1,2,3,4,5,6,7,8,9,10,11) from the population that does not contain geometric cross edges. Suppose the randomly selected operation point from X' is 6. The operation points contained in the neighborhood with 6 as the center and r as the radius are 5, 6, 7 and 8. The two randomly selected operation points from the neighborhood are 6 and 7 respectively. Then aa1=6, aa2=7. According to step 3, bb1=5, bb2=8. Delete the edges (aa1, bb1)=(6,5), (aa2, bb2)=(7,8), and connect the edges (bb1, aa2)=(5,7) and (aa1, bb2)=(6,8). The individual X and the route obtained after passing through the neighborhood 2-opt operator are as follows. Figure 2 c and Figure 2 d. Figure 2 c and Figure 2 In d, d(5,7)+d(6,8)=46,d(5,6)+d(7,8)=61. The path length after reconnection is better than the path length before perturbation, and the individuals generated by the neighborhood 2-opt operator are better than the individual X'.

[0156] Note: Figure 2 In the figure, the number in the circle is the work point, the number on the line connecting the two circles is the distance between the two work points, and the dotted line between the two circles represents the path after reconnection.

[0157] 2) Double bridge movement

[0158] The double bridge movement can change the overall shape of the ring and reduce the probability of the algorithm falling into the local optimum. N )(N is the number of operation points) as an example to illustrate the specific steps of double bridge movement.

[0159] Step 1: Generate a random integer a1 in [2,N-6];

[0160] Step 2: Generate a random integer k0 according to [2+a1,N-4];

[0161] Step 3: Generate a random integer k1 according to [2+k0,N-2];

[0162] Step 4: Generate a random integer k2 according to [2+k1,N];

[0163] Step 5: Sort k0, k1, k2 from small to large, let a2 = min(k0, k1, k2), a3 = median(k0, k1, k2), a4 = max(k0, k1, k2);

[0164] Step 6: Let b1 = a1-1, b2 = a2-1, b3 = a3-1, b4 = a4-1;

[0165] Step 7: a1, b1, a2, b2, a3, b3, a4 and b4 are the index numbers of each component in individual X. Let the operating points corresponding to the index numbers a1, b1, a2, b2, a3, b3, a4 and b4 in X be aa1, bb1, aa2, bb2, aa3, bb3, aa4 and bb4 respectively.

[0166] Step 8: Delete the edges (aa1,bb1), (aa2,bb2), (aa3,bb3), (aa4,bb4) and reconnect the edges (aa1,bb3), (aa2,bb4), (aa3,bb1) and (aa4,bb2).

[0167] In order to facilitate the description of the specific steps of double bridge movement, we take the individual X = (3, 2, 1, 6, 5, 4, 7, 10, 9, 11, 8) without geometric cross edges and the individual X' = (1, 10, 7, 4, 5, 11, 9, 2, 3, 8, 6) with geometric cross edges as an example. The routes represented by X and X' are as follows Figure 3 a and Figure 3 As shown in c;

[0168] Perform double bridge movement on route X. Assume that the four randomly selected work points are aa1=1, aa2=5, aa3=9, and aa4=8. Then bb1=2, bb2=6, bb3=10, and bb4=11. Delete the geometric cross edges (1,2), (5,6), (9,10), and (8,11). Connect (1,10), (5,11), (9,2), and (8,6) to get a new route. Figure 3 As shown in b;

[0169] Perform double bridge movement on route X'. aa1=1,aa2=5,aa3=9,aa4=8, then bb1=10,bb2=11,bb3=2,bb4=6, delete the geometric cross edges (1,10), (5,11), (9,2) and (8,6), connect (1,2), (5,6), (9,10) and (8,11) to get a new route as follows: Figure 3 As shown in d.

[0170] Note: Figure 3 In the figure, the work point in the circle is the work point, the number on the line connecting the two circles is the distance between the two work points, and the dotted line between the two circles represents the path after reconnection.

[0171] 3) 2-Opt algorithm

[0172] The 2-Opt algorithm is a local optimization algorithm. Its main idea is to exchange two non-adjacent edges in a given line for each route in the population, obtain a set of all paths, and retain the exchange that can improve the path. The 2-Opt algorithm can quickly eliminate the geometric cross edges in each path, improve the quality of the solution and the convergence speed. In the early stages of the algorithm iteration, the possibility of geometric cross edges in the path is high. As the number of iterations increases, the geometric cross edges will become fewer and fewer, and the quality of the solution will become better and better.

[0173] (8) Update the optimal individuals and populations, and return to (4).

[0174] The present invention is a method for optimizing the cross-region operation path of a harvester based on an intelligent optimization algorithm, which has the following advantages:

[0175] 1. Balance between global and local search capabilities: The algorithm proposed in this invention combines the advantages of global search and local search, ensuring that in the process of finding the optimal solution, it can not only explore the solution space extensively, but also find a better solution in a certain area in depth;

[0176] 2. Efficient convergence speed: Compared with other algorithms, the algorithm of the present invention shows a faster convergence speed in most cases, which means that the algorithm can find a better or near-optimal solution in a shorter time;

[0177] 3. Strong adaptability: By adjusting parameters in an adaptive way, the algorithm can show good performance in different problem instances and different situations;

[0178] 4. The present invention can provide high-quality solutions in practical applications to meet the needs of different users and scenarios.

[0179] To sum up, the main purpose of the present invention is to provide a hybrid improved real-coded emperor penguin optimization algorithm that can effectively solve the cross-regional operation path optimization problem of harvesters. The algorithm combines the advantages of multiple intelligent optimization algorithms and aims to improve the global and local search capabilities of the algorithm, thereby improving the solution quality and efficiency of the cross-regional operation path optimization problem of harvesters.

Claims

1. A method for optimizing the cross-region operation path of a harvester based on an intelligent optimization algorithm, characterized in that: The steps of this method are as follows: (1) Parameter settings: The population size of the real number coding emperor penguin optimization algorithm based on hybrid improvement is n, the number of iterations of the algorithm is 200, the initial number of iterations gen = 0, the number of cross-region operation points is N, and the objective function of the cross-region operation path planning problem of the harvester with N operation points is expressed as: Where f(D) is the shortest distance for the harvester to operate across regions; t i represents the i-th operation point; d(t i ,t i+1 ) represents the distance between the i-th operating point and the i+1-th operating point, which is expressed by the following formula: (x i ,y i ) and (x i+1 ,y i+1 ) are t i , t i+1 The coordinates of (2) Encoding method: The emperor penguin optimization algorithm based on hybrid improved real number coding adopts real number coding; The number of operation points of the harvester's cross-region operation is N, so the individual initial X=N*rand(1,N) (3) Among them, X is an individual with a code length of N and a code value of a floating point number; Generate n individuals using formula (3); (3)Decoding method: Take the kth individual X in the population k =(x1,x2,…,x N ) as an example, the specific decoding steps are as follows: (3.1) Randomly generate an integer μ between [1, N] and use μ as the starting point; (3.2) Delete X k The vector after the μth component in is X'k, X'k=(x1,x2,…,x μ-1 ,x μ+1 ,…,x N ), according to the distance between μ and other working points d = (d μ,1 ,d μ,2 ,…,d μ,μ-1 ,d μ,μ+1 ,…,d μ,N ), calculate the vector Tp related to the distance between the operation points and the size of the gene position value. The calculation formula of Tp is In the formula, represents the vector d and the vector Multiply elements at the same position; (3.3) Find the minimum value in the vector Tp, and take the operation point i corresponding to the minimum value as the next operation point; (3.4) Let μ = i, repeat Step 2 and Step 3 to determine the visit order of all work points; Decode n individuals using formula (4), calculate the fitness values ​​of n individuals using formula (1), and sort them in order from small to large; (4) If gen meets the maximum iteration condition, output the optimal operation path and the cross-zone operation length of N operation points, and end the loop; otherwise, proceed to the next step; (5) Choose whether to oscillate the adaptive perturbation search strategy for the individual with the smallest fitness value. The calculation formula for the selection probability is as follows: In the formula, t is the current iteration number, and Maxgen is the maximum iteration number of the algorithm; Generate a random number between [0, 1]. If the random number is less than Pr1, perform the oscillating adaptive perturbation search strategy on the optimal individual. Otherwise, proceed to the next step. (6) Choose to use a random strategy or a dynamic movement strategy. The formula for calculating the selection probability is as follows: In the formula, t is the current iteration number, and Maxgen is the maximum iteration number of the algorithm; A random number is randomly generated between [0, 1]. If the random number is greater than Pr2, a dynamic mobile strategy is used on the individuals in the population. Otherwise, a random strategy is executed. (7) Perform local search operations Randomly generate a random number \(m\) in the interval \([0, 1]\). If \(m < P\), execute the 2 - Opt algorithm; otherwise, execute the adaptive combined perturbation. If the adaptive combined perturbation strategy is executed, then randomly generate a random number \(\mu\) between \([0, 1]\). If \(\mu < P\) s , execute the double - bridge; otherwise, execute the neighborhood 2 - opt operator. The calculation formulas of \(P\) and \(P\) s are as follows: Where t is the current running time, t max is the maximum running time of the example, P min is the minimum selection probability, P max is the maximum selection probability, P min =0.25, P max =0.7; (8) Update the optimal individuals and populations, and return to (4).

2. The method for optimizing the cross-region operation path of a harvester based on an intelligent optimization algorithm according to claim 1, characterized in that: In step (5), the update formula of the oscillating adaptive perturbation search strategy is as follows: In the formula, Xb(t) is the optimal individual, t is the current iteration number, N is the number of harvester operation points, q is a randomly generated N-dimensional 0-1 row vector, Maxgen is the maximum number of iterations of the algorithm, β is the disturbance term, and λ is the adaptive change amount. Represents the multiplication of the elements at the same position of two vectors.

3. The method for optimizing the cross-region operation path of a harvester based on an intelligent optimization algorithm according to claim 1, characterized in that: In step (6), the update formula of the dynamic mobility strategy is as follows: Where N is the total number of operating points of the harvester, and Xm r1(t-1) and Xm r2(t-1) are the optimal positions of two randomly selected emperor penguins.

4. The method for optimizing the cross-region operation path of a harvester based on an intelligent optimization algorithm according to claim 1 or 3, characterized in that: In step (6), the update formula of the random strategy is as follows: X i (t)=X i (t-1)+A i (t) (15) Where Xr(t-1) is an individual randomly selected from the population, and Xmi(t-1) is the optimal position in the memory of the i-th individual Xi(t-1) in the population.

5. The method for optimizing the cross-region operation path of a harvester based on an intelligent optimization algorithm according to claim 1, characterized in that: In step (7), the specific steps of the neighborhood 2-opt operator are as follows: Take a certain circuit X in the population = (x1, x2, ..., x N ) as an example, N is the number of operation points, Step 1: Randomly select a work point a from N work points, take a as the center and a circle with a radius of r1 as the neighborhood, denoted as U(a,r1), and the calculation formula of r1 is as follows: In the formula, Z is the route length of the optimal individual in the population; Step 2: Determine the work point u contained in the neighborhood U(a,r1). If u≥2, randomly select two work points aa1 and aa2 in the neighborhood. If u=1, let aa1=a, and then randomly select a work point in [1,2,…,a-1,a+1,…,N] as aa2. Step 3: Find the operating point bb1 adjacent to aa1 and the operating point bb2 adjacent to aa2 in X, and bb1≠aa2, bb2≠aa1. If bb1=bb2, repeat Step 1 and Step 2 until bb1≠bb2. Step 4: Delete the edges (aa1, bb1) and (aa2, bb2), and connect the edges (bb1, aa2) and (aa1, bb2).

6. The method for optimizing the cross-region operation path of a harvester based on an intelligent optimization algorithm according to claim 1, characterized in that: In step (7), the specific steps of double bridge movement are as follows: Take the individuals in the population X=(x1,x2,…,x N )(N is the number of operation points) as an example, Step 1: Generate a random integer a1 in [2,N-6]; Step 2: Generate a random integer k0 according to [2+a1,N-4]; Step 3: Generate a random integer k1 according to [2+k0,N-2]; Step 4: Generate a random integer k2 according to [2+k1,N]; Step 5: Sort k0, k1, k2 from small to large, let a2 = min(k0, k1, k2), a3 = median(k0, k1, k2), a4 = max(k0, k1, k2); Step 6: Let b1 = a1-1, b2 = a2-1, b3 = a3-1, b4 = a4-1; Step 7: a1, b1, a2, b2, a3, b3, a4 and b4 are the index numbers of each component in individual X. Let the operation points corresponding to the index numbers a1, b1, a2, b2, a3, b3, a4 and b4 in X be aa1, bb1, aa2, bb2, aa3, bb3, aa4 and bb4 respectively; Step 8: Delete the edges (aa1,bb1), (aa2,bb2), (aa3,bb3), (aa4,bb4) and reconnect the edges (aa1,bb3), (aa2,bb4), (aa3,bb1) and (aa4,bb2).

7. The method for optimizing the cross-region operation path of a harvester based on an intelligent optimization algorithm according to claim 1, characterized in that: In step (7), the 2-Opt algorithm is a local optimization algorithm. Specifically, for each route in the population, two non-adjacent edges in the given route are exchanged in turn to obtain a set of all paths, and the exchange that can improve the path is retained.

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