收稿日期: 2017-11-02
修回日期: 2018-01-25
网络出版日期: 2019-01-31
基金资助
国家自然科学基金（No.61440049，No.61772255）；江西省创新驱动"5511"工程优势学科创新团队基金（No.20165BCB19007）；江西省优势科技创新团队计划项目基金（No.20152BCB24004）；江西省科技厅科技项目基金（No.20161BBG70047，No.20161BAB202038）；江西省教育厅科技项目基金（No.GJJ150716）；无损检测技术教育部重点实验室（南昌航空大学）开放基金（No.ZD201529004）；江西省图像处理与模式识别重点实验室开放基金（No.ET201604246）；江西省研究生创新专项资金（No.YC2016-s349）资助
Multi-population Evolutionary Algorithm Based on Dynamic Area Division
Received date: 2017-11-02
Revised date: 2018-01-25
Online published: 2019-01-31
陈昊, 许春蕾, 黎明, 张聪炫 . 基于动态区域划分的多种群进化算法[J]. 应用科学学报, 2019 , 37(1) : 126 -136 . DOI: 10.3969/j.issn.0255-8297.2019.01.012
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
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