Intelligent optimization algorithm based on simplex neighbourhood and multi-role evolutionary policy
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An intelligent optimization algorithm, multi-role technology, applied in computing, computing models, instruments, etc., can solve problems such as poor stability, easy to fall into local optimum, large variance, etc.
Inactive Publication Date: 2017-08-22
KUNMING UNIV OF SCI & TECH
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However, these algorithms also have some disadvantages, such as the search is easy to fall into the local optimum and cannot converge to the global optimum; the performance of the algorithm to converge to the global optimum depends on the control parameters of the algorithm; the stability of the algorithm to converge to the global optimum is not good, and the variance Too big
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Embodiment 1
[0050] Embodiment 1: See the implementation process figure 1 , figure 2 , image 3 shown. details as follows:
[0051] An objective function to be optimized is:
[0052]
[0053] It is a multimodal function containing a large number of local optimum points, and the domain is [-600,600] n , while its global optimal value is 0, and its two-dimensional distribution is as figure 2 shown. This algorithm will randomly locate the initial position of each particle with a uniform distribution within the search space set by the definition domain, and then the algorithm will guide all the particles in the group to converge to the global optimal point.
[0054] see figure 1 , the specific steps of the search algorithm are as follows:
[0055] S1), the initial random positioning of m particles in the search space based on uniform distribution;
[0056]
[0057] in, is the i-th particle in R n Searches for a position along the kth dimension of the subspace. and x k -6...
Embodiment 2
[0076] Embodiment 2: See the implementation process figure 1 , Figure 4 , Figure 5 shown. details as follows:
[0077] An objective function to be optimized is:
[0078]
[0079] It is a multimodal function containing a large number of local optimum points, and the domain of definition is: [-100, 100] n , and its global optimal point is: 0, and its two-dimensional distribution is as follows Figure 4 shown. This algorithm will randomly locate the initial position of each particle with a uniform distribution in the domain search space, and then the algorithm will guide all particles in the population to converge to the global optimal point.
[0080] see figure 1 , the specific steps of the search algorithm are as follows:
[0081] S1), the initial random positioning of m particles in the search space based on uniform distribution;
[0082]
[0083] in, is the i-th particle in R n Searches for a position along the kth dimension of the subspace. and x k Th...
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Abstract
The invention relates to an intelligent optimization algorithm based on simplex neighbourhood and a multi-role evolutionary policy, which belongs to the field of novel intelligent global optimization algorithm calculation. The method comprises the steps that the particle position is randomly initialized in a search space with uniform distribution probability; each particle searches the new position through a simplex neighbourhood search operator; a test function is used to evaluate the merits of the new position of each particle; according to the merits of the new position of each particle, three role positions of each particle are determined; the optimal particle in the iterative period and the optimal position are recorded, and the iterative search period is ended; and a next iterative search period is started until particles converge to the global optimal point position. According to the invention, the multi-role state evolutionary search policy and a simplex neighbourhood search mechanism are provided; group cooperative search and a competitive selection evolutionary policy are combined; and the intelligent optimization algorithm is an effective intelligent global optimization algorithm.
Description
technical field [0001] The invention relates to an intelligent optimization algorithm based on a simplex neighborhood and a multi-role evolution strategy, which realizes searching for a global optimal point position in a given search space, and belongs to the computing field of a new intelligent global optimization algorithm. Background technique [0002] In recent years, in order to solve the global optimization problem in a large number of practical application problems, many global optimization algorithms have been proposed. Among them, the intelligent optimization algorithm based on swarm intelligence has become a hot spot in the research of optimization algorithms. Such as genetic algorithm, immune algorithm, particle swarm algorithm, differential evolution algorithm, bee colony algorithm, fireworks algorithm, etc. However, these algorithms also have some disadvantages, such as the search is easy to fall into the local optimum and cannot converge to the global optimum; ...
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