自适应分解式多目标粒子群优化算法
Adaptive Multiobjective Particle Swarm Optimization Based on Decomposition Archive
查看参考文献26篇
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文摘
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为了提高多目标粒子群优化算法解的分布性,文中提出了一种自适应分解式多目标粒子群优化算法(Adaptive Multiobjective Particle Swarm Optimization based on Decomposed Archive,AMOPSO-DA).首先,设计了一种基于优化解空间分布信息的外部档案更新策略,有效提升了AMOPSO-DA的空间搜索能力;其次,提出了一种基于粒子进化方向信息的飞行参数调整方法,有效平衡了AMOPSO-DA的探索和开发能力.最后,将提出的AMOPSO-DA应用于多目标优化问题,实验结果表明,文中提出的AMOPSO-DA能够获得分布性较好的优化解. |
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其他语种文摘
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To improve the distribution performance of multiobjective particle swarm optimization algorithm,an adaptive multiobjective particle swarm optimization algorithm,based on the decomposed archive,named AMOPSO-DA,is developed in this paper.First,an external archive update strategy,based on the spatial distribution information of optimal solutions,is designed to improve the searching ability of AMOPSO-DA.Second,an adaptive flying parameter adjustment strategy,based on the evolutionary direction information of each particle,is proposed to balance the exploration ability and the exploitation ability.Finally,this proposed AMOPSO-DA is applied to some multiobjective optimization problems.The experiment results demonstrate that AMOPSO-DA can obtain well-distributed optimal solutions. |
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来源
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电子学报
,2020,48(7):1245-1254 【核心库】
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DOI
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10.3969/j.issn.0372-2112.2020.07.001
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关键词
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多目标粒子群优化算法
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分解
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外部档案
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分布性
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自适应
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地址
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1.
北京工业大学信息学部, 北京, 100124
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计算智能与智能系统北京市重点实验室, 计算智能与智能系统北京市重点实验室, 北京, 100124
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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0372-2112 |
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学科
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自动化技术、计算机技术 |
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基金
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国家自然科学基金
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国家重点研发计划
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北京高校卓越青年科学家项目
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北京市朝阳区协同创新
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文献收藏号
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CSCD:6770190
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