引用本文:马海平,李寰,阮谢永.一种群体迁移优化算法及性能分析[J].控制理论与应用,2010,27(3):329~334.[点击复制]
MA Hai-ping,LI Huan,RUAN Xie-yong.Species migration-based optimization algorithm and performance analysis[J].Control Theory and Technology,2010,27(3):329~334.[点击复制]
一种群体迁移优化算法及性能分析
Species migration-based optimization algorithm and performance analysis
摘要点击 1939  全文点击 1518  投稿时间：2008-11-18  修订日期：2009-04-16
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DOI编号  10.7641/j.issn.1000-8152.2010.3.CCTA081275
  2010,27(3):329-334
中文关键词  优化算法  群体迁移  函数优化  计算智能
英文关键词  optimization algorithm  species migration  function optimization  computer intelligence
基金项目  
作者单位E-mail
马海平* 绍兴文理学院 物理与电子信息系 mhping1981@tom.com 
李寰 聊城大学 计算机系  
阮谢永 绍兴文理学院物理与电子信息系  
中文摘要
      受生态系统中迁移机制的激发, 提出了一种基于群体迁移的优化算法. 该算法是根据生态学中群体分布的迁移模型而提出的一种新的优化算法. 借鉴其他智能算法思想, 用栖息地来表示优化问题的解集, 通过生物群体的迁入与迁出实现解集之间特征信息的共享, 从而完成进化过程. 该文讨论了基于群体迁移的优化算法基本原理和实现步骤, 同时进行一些基准函数的性能测试. 通过分析表明提出的新算法是有效的, 是一种具有潜在优越性的优 化算法.
英文摘要
      Motivated by migration mechanisms of ecosystems, a species migration-based optimization algorithm (SMOA) is proposed. SMOA is a new optimization method based on the migration model of organism distribution in biological systems. Inspired by the development of other intelligence algorithms, problem solutions are represented as habitats; and the sharing of features between solutions is represented as species immigration and emigration in SMOA. This paper discusses the principle and steps of implementation in SMOA, and explores performance through benchmark functions. The performance study shows that the proposed algorithm is effective and is a promising candidate for optimization.