| 基于动态参数的杂交粒子群优化算法 |
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| 引用本文： | 黄伟,罗世彬,王振国. 基于动态参数的杂交粒子群优化算法[J]. 计算机科学, 2010, 37(12): 165-166 |
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| 作者姓名： | 黄伟 罗世彬 王振国 |
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| 作者单位： | 国防科技大学航天与材料工程学院,长沙,410073 |
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| 基金项目： | 本文受国防科技大学优秀研究生创新项目(B070101) ,湖南省研究生科研创新项目(3206)资助。 |
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| 摘 要： | 粒子群优化算法的局部搜索能力较差，搜索精度不够高，容易陷入局部极小解，且搜索性能对参数具有一定的依赖性。本文针对这些缺点，在借鉴遗传算法中杂交概念的基础上，进一步通过在速度进化方程中引进动态参数来提高算法的收敛速度和收敛率。经LevyNo. 5函数对改进算法的测试表明，相对杂交粒子群优化算法，该方法的收敛速度和平均收敛率均得到了不同程度的提高。
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| 关 键 词： | 粒子群优化算法，优化，杂交，动态参数 |
Crossbreeding Particle Swarm Optimization Algorithm Based on Dynamic Parameter |
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| HUANG Wei,LUO Shi-bin,WANG Zhen guo. Crossbreeding Particle Swarm Optimization Algorithm Based on Dynamic Parameter[J]. Computer Science, 2010, 37(12): 165-166 |
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| Authors: | HUANG Wei LUO Shi-bin WANG Zhen guo |
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| Affiliation: | (Inst. of Aerospace and Materials Engineering,National University of Defense Technology,Changsha 410073,China) |
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| Abstract: | The particle swarm optimization (PSO) algorithm is easy to trapped into local extremum, and its convergence speed is lower and the precision is worse in the late evolution. Furthermore, the parameter selection can affect the algorithm. Aimed at these disadvantages of PSO,based on using the crossbreeding concept in the genetic algorithm for reference, the new algorithm by introducing dynamical parameters in the evolution of the speed equation is proposed. The convergence speed and the convergence rate were improved. The new method arc tested by function Levy No. 5 shows that the convergence speed and the average convergence rate was increased. |
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| Keywords: | Particle swarm optimization Optimization Crossbreeding Dynamic parameter |
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