Quantum particle swarm optimization algorithm with adaptive mutation

doi:10.3778/j.issn.1002-8331.2011.03.012

Computer Engineering and Applications ›› 2011, Vol. 47 ›› Issue (3): 41-43.DOI: 10.3778/j.issn.1002-8331.2011.03.012

• 研究、探讨 • Previous Articles Next Articles

Quantum particle swarm optimization algorithm with adaptive mutation

LIU Junfang¹，GAO Yuelin²

1.School of Mathematics and Computer，Ningxia University，Yinchuan 750021，China
2.Research Institute of Information and System Computation Science，North National University，Yinchuan 750021，China

Received:2009-05-13 Revised:2009-07-15 Online:2011-01-21 Published:2011-01-21
Contact: LIU Junfang

带自适应变异的量子粒子群优化算法

刘俊芳¹，高岳林²

1.宁夏大学数学计算机学院，银川 750021
2.北方民族大学信息与系统科学研究所，银川 750021

通讯作者: 刘俊芳

Abstract

Abstract: A Quantum Particle Swarm Optimization Algorithm with Adaptive Mutation（AMQPSO） is given.When the proposed algorithm is found to sink into the local optimization by fitness variance and space position aggregation degree，a new adaptive mutation operator is implemented at the best position of each particle at first so as to realize global optimization.The experiments show that the AMQPSO is better than QPSO and the AMPSO in global optimization.

Key words: global optimization, Particle Swarm Optimization（PSO）, Quantum Particle Swarm Optimization（QPSO）, adaptive mutation

摘要： 提出了一种带有自适应变异的量子粒子群优化（AMQPSO）算法，利用粒子群的适应度方差和空间位置聚集度来发现粒子群陷入局部寻优时，对当前每个粒子经历过的最好位置进行自适应变异以实现全局寻优。通过对典型函数的测试以及与量子粒子群优化（QPSO）算法和自适应粒子群优化（AMPSO）算法的比较，说明AMQPSO算法增强了全局搜索的性能，优于其他算法。

关键词: 全局最优化, 粒子群优化, 量子粒子群优化, 自适应变异

CLC Number:

TP18

LIU Junfang¹，GAO Yuelin². Quantum particle swarm optimization algorithm with adaptive mutation[J]. Computer Engineering and Applications, 2011, 47(3): 41-43.

刘俊芳¹，高岳林². 带自适应变异的量子粒子群优化算法[J]. 计算机工程与应用, 2011, 47(3): 41-43.

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Quantum particle swarm optimization algorithm with adaptive mutation

带自适应变异的量子粒子群优化算法

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