Multi-population Evolutionary Algorithm Based on Dynamic Area Division
Received date: 2017-11-02
Revised date: 2018-01-25
Online published: 2019-01-31
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
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 . DOI: 10.3969/j.issn.0255-8297.2019.01.012
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