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粒子群算法在求解优化问题中的应用 被引量：39

Application of Particle Swarm Optimization for Solving Optimization Problems
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摘要 粒子群优化(PSO:ParticleSwarmOptimization)算法是一种新兴的优化技术,其思想来源于人工生命和进化计算理论。PSO算法通过粒子追随自己找到的最好解和整个群体的最好解完成优化。为了避免PSO算法在求解最优化问题时陷入在局部最优及提高PSO算法的收敛速度,提出了对PSO算法增加更新概率。对无约束和有约束最优化问题分别设计了基于PSO算法的不同的求解方法和测试函数,并对PSO算法求解多目标优化问题进行了研究。仿真实验表明了改进的PSO算法求解最优化问题时的有效性。 PSO (Particle Swarm Optimization)is a new optimization technique originating from artificial life and evolutionary computation. The algorithm completes the optimization through following the personal best solution of each particle and the global best value of the whole swarm. To avoids the local minimum problems and to improve convergent speed, a new probability of PSO algorithm was proposed. Different solving methods and test functions have been designed for unconstrained and constrained optimization problems, and to do research for solving multi objective optimization problems with PSO. Numerical experiments have shown the feasibility and effectiveness of the proposed algorithm.
作者 张利彪 周春光 刘小华 马铭
机构地区 吉林大学计算机科学与技术学院
出处 《吉林大学学报（信息科学版）》 CAS 2005年第4期385-389,共5页 Journal of Jilin University（Information Science Edition）
基金 国家自然科学基金资助项目(60433020) 教育部符号计算与知识工程重点实验室资助(02090)
关键词 粒子群算法 最优化问题 多目标优化问题 <Keyword>particle swarm optimization optimization problems multi objective optimization problems
分类号 TP303 [自动化与计算机技术—计算机系统结构]
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参考文献18

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二级参考文献57

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共引文献1002

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引证文献39

二级引证文献186

吉林大学学报（信息科学版）
2005年 第4期

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