基于动态区域划分的多种群进化算法

doi:10.3969/j.issn.0255-8297.2019.01.012

Abstract

Abstract:

Aiming at the problem that solution space cannot be divided accurately in multi-population evolutionary algorithms, a cloud model is used to estimate the optimization problem in the process of evolution. According to the difference between the cloud estimation and the original problem, the solution space can be partitioned dynamically. We build several sub-populations by using clustering algorithm, and adopt heterogeneous evolutionary strategy to sub-populations. The validity of area division is analyzed, and it is proved that the method can reduce the searching space. Experimental results show that the proposed partition strategy can not only reduce the difficulty of the optimization problem, but also improve the effectiveness and feasibility of the algorithm.

Key words: evolutionary algorithm, cloud model, area division, multi-population

CLC Number:

TP18

CHEN Hao, XU Chun-lei, LI Ming, ZHANG Cong-xuan. Multi-population Evolutionary Algorithm Based on Dynamic Area Division[J]. Journal of Applied Sciences, 2019, 37(1): 126-136.

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Multi-population Evolutionary Algorithm Based on Dynamic Area Division

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