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Multi-objective evolutionary algorithm based on double-population cooperation

A multi-objective evolution and population technology, applied in computing, computing models, instruments, etc., can solve the problems of MOEA algorithm falling into local optimality, poor solution set diversity and distribution, etc., and achieve a balance between global exploration capability and local mining. Ability to solve the effect of easy precocious convergence

Inactive Publication Date: 2020-05-19
GUANGXI TEACHERS EDUCATION UNIV
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Problems solved by technology

[0013] The purpose of the present invention is to provide a multi-objective evolutionary algorithm based on dual-population collaboration, which can effectively solve the problems that the MOEA algorithm is easy to fall into local optimum, and the diversity and distribution of the solution sets obtained by the algorithm are not good.

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  • Multi-objective evolutionary algorithm based on double-population cooperation
  • Multi-objective evolutionary algorithm based on double-population cooperation
  • Multi-objective evolutionary algorithm based on double-population cooperation

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[0046] The accompanying drawings are for illustration purposes only, and should not be construed as limiting the patent. In order to better illustrate this embodiment, some components in the drawings will be omitted, enlarged or reduced, and do not represent the performance effect of the actual invention. For those skilled in the art, some known results and their description omissions in the accompanying drawings are understandable.

[0047] The technical solution of the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0048] Such as figure 1 As shown, a multi-objective evolutionary algorithm based on dual-population coordination includes the following process:

[0049] S1: Set the target number M, the maximum number of iterations T max , decision vector dimension n, population size N, external archive size N', pseudo-binary cross distribution index η, mutation probability p m , the differ...

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Abstract

The invention provides a multi-objective evolutionary algorithm based on double-population cooperation. Two initial sub-populations are respectively generated by adopting a randomization method and ahybrid level orthogonal initialization method. Binary crossover-like operations and differential mutation operations are alternately applied to the two sub-populations in successive evolution to produce offspring individuals. The two sub-populations and the offspring individuals of the two sub-populations are respectively merged to generate two temporary intermediate populations, and then quick non-dominated sorting operation is respectively executed on the two intermediate populations to select better individuals from the two intermediate populations to update the external archive set. In thewhole evolutionary process, the two sub-populations keep respective evolutionary modes, and meanwhile, the two sub-populations can share and interact information through an external archive set. According to the method, the strategies and the methods are organically coordinated, the global exploration capability and the local mining capability of the multi-objective evolutionary algorithm are well balanced, and the problems that the multi-objective evolutionary algorithm is prone to premature convergence, the diversity of obtained solution sets is poor and the like are effectively solved.

Description

technical field [0001] The invention relates to an intelligent optimization algorithm, and more specifically, to a multi-objective evolutionary algorithm based on dual-population collaboration. Background technique [0002] In scientific computing and engineering practice, there are many problems that need to optimize two or more objectives at the same time, and these problems are usually called multi-objective optimization problems (MOP). The objectives of the MOP problem are often in the same conflict, that is, improving one of the objective values ​​​​usually causes the deterioration of other objective values. Therefore, the MOP problem generally does not have a unique optimal solution so that each objective can obtain the optimal value at the same time, but is often a compromise solution, that is, the Pareto solution set. Evolutionary Algorithm (EA) is a simple and effective method to solve the MOP problem. Based on the characteristics of group search, EA can get a set ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/00
CPCG06N3/006
Inventor 谢承旺张飞龙周慧闭应洲
Owner GUANGXI TEACHERS EDUCATION UNIV
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