A novel adaptive hybrid crossover operator for multiobjective evolutionary algorithm
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- 作者：Qingling Zhu zhuqingling@email.szu.edu.cn" class="auth_mail" title="E-mail the corresponding author ; Qiuzhen Lin ; qiuzhlin@szu.edu.cn" class="auth_mail" title="E-mail the corresponding author ; qiuzhlin@qq.com" class="auth_mail" title="E-mail the corresponding author ; Zhihua Du ; Zhengping Liang ; Wenjun Wang ; Zexuan Zhu ; Jianyong Chen ; Peizhi Huang ; Zhong Ming
- 关键词：Evolutionary algorithm ; Multiobjective optimization ; Simulated binary crossover ; Differential evolution
- 刊名：Information Sciences
- 出版年：2016
- 出版时间：1 June 2016
- 年：2016
- 卷：345
- 期：Complete
- 页码：177-198
- 全文大小：4339 K
文摘
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A novel adaptive hybrid crossover (AHX) is designed for MOEAs.
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The parameters setting in AHX is adjusted adaptively.
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AHX is embedded into five frameworks for solving MOPs.
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AHX is better than SBX and DE operators in solving most of test problems.