| 引用本文: | 郑永康,陈维荣,戴朝华,王维博.随机聚焦搜索优化算法[J].控制理论与应用,2009,26(8):915~917.[点击复制] |
| ZHENG Yongkang,CHEN Weirong,DAI Chaohua,WANG Weibo.Optimization algorithm with stochastic focusing search[J].Control Theory and Technology,2009,26(8):915~917.[点击复制] |
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| 随机聚焦搜索优化算法 |
| Optimization algorithm with stochastic focusing search |
| 摘要点击 2548 全文点击 1451 投稿时间：2008-01-09 修订日期：2008-11-04 |
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| DOI编号 |
| 2009,26(8):915-917 |
| 中文关键词 群集智能 随机聚焦搜索 人类随机搜索 粒子群优化 |
| 英文关键词 swarm intelligence stochastic focusing search human randomized searching particle swarm optimization |
| 基金项目 国家自然科学基金资助项目(60870004); 西南交通大学博士生创新基金资助项目(2007¡3). |
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| 中文摘要 |
| 提出了一种新的优化算法: 随机聚焦搜索. 该算法属于群集智能, 它模仿了人类的搜索行为及其在搜索过程中的随机性, 算法简单并且计算复杂度小. 在对一系列典型复杂函数的优化测试中, 通过与差分进化算法和全面学习的粒子群算法进行对比, 验证了该算法性能. 仿真结果表明, 该算法能解决大多数benchmark函数问题, 并且有较快的寻优速度, 可以在一定程度上替代现有的优化算法. |
| 英文摘要 |
| A novel optimization algorithm with stochastic focusing search(SFS) is proposed. This algorithm is a swarmintelligence algorithm, which imitates the random action in human searching behaviors. The algorithm performance is
studied by using a set of typical complex functions, and is compared with that of the differential evolution(DE) algorithm and the comprehensive learning-particle-swarm-optimizer(CLPSO) algorithm. The simulation results show that SFS solves most of the benchmark problems and can be considered a promising candidate of search algorithms when the existing algorithms have difficulties in solving some problems. |
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