| 改进的粒子群算法求解非线性约束优化问题 |
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| 引用本文： | 吴 茜,郑金华,宋 武.改进的粒子群算法求解非线性约束优化问题[J].计算机工程与应用,2007,43(24):61-64. |
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| 作者姓名： | 吴 茜 郑金华 宋 武 |
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| 作者单位： | 湘潭大学信息工程学院,湖南湘潭411105 |
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| 基金项目： | 国家高技术研究发展计划(863计划)
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教育部留学回国人员科研启动基金
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湖南省自然科学基金
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湖南省教育厅资助项目 |
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| 摘 要： | 提出了一种改进的粒子群算法(Improved Particle Swarm Optimization,IPSO),使用了一种新型的变异策略,并在搜索过程中将部分邻近的个体聚集成核,从而形成多子群引导粒子探测新的搜索区域,采用了简单易行的罚函数约束处理机制,使算法在求解较难的非线性约束优化问题时具有很强的全局搜索能力与效率。对比数值实验结果表明,该算法能够有效、稳定地求解非线性约束优化问题。
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| 关 键 词： | 粒子群 多子群 非线性约束优化 |
| 文章编号： | 1002-8331（2007）24-0061-04 |
| 修稿时间： | 2007-04 |
Improved particle swarm algorithm for solving nonlinear constrained optimization problems |
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| WU Qian,ZHENG Jin-hua,SONG Wu.Improved particle swarm algorithm for solving nonlinear constrained optimization problems[J].Computer Engineering and Applications,2007,43(24):61-64. |
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| Authors: | WU Qian ZHENG Jin-hua SONG Wu |
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| Affiliation: | Institute of Information Engineering,Xiangtan University,Xiangtan,Hunan 411105,China |
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| Abstract: | This paper proposes an Improved Particle Swarm Optimization algorithm(IPSO).IPSO adopts a new mutation operator and a new method that congregates some neighboring individuals to form multiple sub-populations in order to lead particles to explore new search space.Additionally,this algorithm incorporates a mechanism with a simple and easy penalty function to handle constraint.Thus,this algorithm has strong global exploratory capability and efficiency while being applied to solve nonlinear constrained optimization problems.Experimental results indicate that the IPSO is robust and efficient in solving nonlinear constrained optimization problems. |
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| Keywords: | particle swarm multiple sub-populations nonlinear constrained optimization |
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