粒子群优化与差分进化混合算法的综述与分类
A SURVEY AND TAXONOMY ON HYBRID ALGORITHMS BASED ON PARTICLE SWARM OPTIMIZATION AND DIFFERENTIAL EVOLUTION
查看参考文献95篇
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文摘
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优化算法的性能改进长期以来一直是算法研究者们追求的一个重要目标,对不同算法进行混合以期利用算法的互补优势来获得性能更优异的算法代表了一类典型的设计思想。针对两类基于群体演化的优化算法——粒子群优化(PSO)与差分进化(DE)算法,对基于二者的各种混合算法(DEPSO)进行了系统而全面的综述,并在此基础上提出了一种混合策略分类方法,对现有的各种典型DEPSO算法进行了分类,比较了各种混合策略的异同,并指出了一些新的研究方向和混合设计原则. |
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其他语种文摘
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Improving the performance of optimization algorithms has long been an important pursuit of researchers.It is a typical design idea and paradigm to combine different optimizers for a synergy of their complementary advantages.Regarding two kinds of population-based evolutionary algorithms,the particle swarm optimizer(PSO) and the differential evolution (DE),we present a systematic and comprehensive survey on their hybrids(DEPSOs) in the literature and propose a taxonomy of hybridization strategies.Based on the taxonomy, we make a classification of different DEPSOs and analyze their similarities and differences. We also point out some new directions for future research and provide several guidelines for hybridization design of optimizers. |
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来源
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系统科学与数学
,2011,31(9):1130-1150 【核心库】
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关键词
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优化
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混合策略
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粒子群优化
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差分进化
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探索与开发
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地址
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北京理工大学自动化学院, "复杂系统智能控制与决策"教育部重点实验室, 北京, 100081
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语种
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中文 |
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文献类型
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综述型 |
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ISSN
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1000-0577 |
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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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国家自然科学基金国家杰出青年科学基金
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文献收藏号
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CSCD:4420599
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