Scholarlink

<p>In Multiobjective Differential Evolution (MODE) algorithms, many techniques of balancing between convergence-divergence and exploration–exploitation issues have been investigated. These techniques include archive maintenance, selection mechanism, population size, and proper parameter setting. There are very few MODE algorithms that use feedback information extracted from the evolutionary environment to create an adaptive balance between exploitation and exploration. As a result, this study describes a novel adaptive MODE algorithm based on a parallel cell coordinate system. The proposed algorithm, known as pccs Adaptive Cross-Generation Differential Evolution (pccsACGDE), utilizes information from two consecutive generations and a parallel cell coordinate system to adaptively adjust the parameters. The pccsACGDE algorithm also introduces two new mutation operators in order to better balance exploitation and exploration. To demonstrate the ability of the pccsACGDE algorithm compared to other algorithms, 34 benchmark functions consisting of constraint and unconstraint problems are considered. The outcomes of the simulation demonstrate that the pccsACGDE algorithm has been more successful in producing superior solutions than state-of-the-art methods.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Adaptive multiobjective differential evolution based on parallel cell coordinate system

  • 期刊论文

  • 发表日期：

    2025年8月30日

查看全文
  • Seyed Naghi Seyedaghaee,
  • Ali Broumandnia,
  • Reza Tavakkoli-Moghaddam

摘要

In Multiobjective Differential Evolution (MODE) algorithms, many techniques of balancing between convergence-divergence and exploration–exploitation issues have been investigated. These techniques include archive maintenance, selection mechanism, population size, and proper parameter setting. There are very few MODE algorithms that use feedback information extracted from the evolutionary environment to create an adaptive balance between exploitation and exploration. As a result, this study describes a novel adaptive MODE algorithm based on a parallel cell coordinate system. The proposed algorithm, known as pccs Adaptive Cross-Generation Differential Evolution (pccsACGDE), utilizes information from two consecutive generations and a parallel cell coordinate system to adaptively adjust the parameters. The pccsACGDE algorithm also introduces two new mutation operators in order to better balance exploitation and exploration. To demonstrate the ability of the pccsACGDE algorithm compared to other algorithms, 34 benchmark functions consisting of constraint and unconstraint problems are considered. The outcomes of the simulation demonstrate that the pccsACGDE algorithm has been more successful in producing superior solutions than state-of-the-art methods.