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

应用科学学报 ›› 2019, Vol. 37 ›› Issue (1): 126-136.doi: 10.3969/j.issn.0255-8297.2019.01.012

基于动态区域划分的多种群进化算法

陈昊¹, 许春蕾², 黎明², 张聪炫¹

1. 南昌航空大学无损检测技术教育部重点实验室, 南昌 330063;
2. 南昌航空大学信息工程学院, 南昌 330063

收稿日期:2017-11-02 修回日期:2018-01-25 出版日期:2019-01-31 发布日期:2019-01-31
作者简介:陈昊,博士,研究方向:智能算法理论与应用,E-mail:chenhaoshl@163.com;黎明,教授,博导,研究方向:智能计算、图像处理与模式识别,E-mail:limingnchu@163.com
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

CHEN Hao¹, XU Chun-lei², LI Ming², ZHANG Cong-xuan¹

1. Key Laboratory of Nondestructive Testing Ministry of Education, Nanchang Hangkong University, Nanchang 330063, China;
2. School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China

Received:2017-11-02 Revised:2018-01-25 Online:2019-01-31 Published:2019-01-31

摘要/Abstract

摘要：

针对多种群进化算法中解空间无法准确划分的问题，在进化过程中利用云模型估计优化问题.根据云估计与原问题的差异动态划分解空间；采用聚类算法构建多个子种群，并设计异构进化策略；对区域划分的有效性进行理论分析，证明划分方法能准确缩小搜索空间.实验分析表明：所提出的划分策略既可降低优化问题难度，又能提高算法的有效性与可行性.

关键词: 进化算法, 区域划分, 多种群, 云模型

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

中图分类号:

TP18

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.

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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基于动态区域划分的多种群进化算法

Multi-population Evolutionary Algorithm Based on Dynamic Area Division

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