[1]胡皞,常军,巩文龙,等. 基于混合概率的新型小波变异量子粒子群算法[J].计算机技术与发展,2016,26(01):78-81.
　HU Hao,CHANG Jun,GONG Wen-long,et al. A New Particle Swarm Optimization Algorithm of Wavelet Mutation Quantum-behaved Based on Mixed-probability[J].,2016,26(01):78-81.
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 基于混合概率的新型小波变异量子粒子群算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年01期
页码:
78-81
栏目:
安全与防范
出版日期:
2016-01-10

文章信息/Info

Title:
 A New Particle Swarm Optimization Algorithm of Wavelet Mutation Quantum-behaved Based on Mixed-probability
文章编号:
1673-629X（2016)01-0078-04
作者:
 胡皞; 常军; 巩文龙; 刘文波
 苏州科技学院
Author(s):
 HU Hao; CHANG Jun; GONG Wen-long; LIU Wen-bo
关键词:
 ; 量子粒子群算法; 混合概率; 小波; 局部最优; 全局最优
Keywords:
 ; quantum-behaved particle swarm optimization; mixed probability; wavelet local optimization; global optimization
分类号:
TP301
文献标志码:
A
摘要:
 量子粒子群算法(QPSO)具有全局寻优能力不强，且容易陷入局部最优的缺陷，因此，针对这个问题，提出了一种基于混合概率的新型小波变异量子粒子群(M-WMQPSO)算法的改进算法。该算法首先在粒子进化方程中引入高斯分布，采用混合概率分布进化方程取代标准 QPSO 进化方程，以此来提高算法的寻优能力。接着，为了更好地提高算法的全局寻优能力，改善算法容易陷入局部最优的缺陷，在粒子进化过程中以一定的概率对粒子进行新型小波变异处理，增加粒子种群的多样性，避免了粒子在寻优过程中陷入局部最优，从而实现算法全局寻优的目的。最后，采用六个典型测试函数对该改进算法的性能进行验证。测试结果表明，改进算法的寻优能力和避免局部最优能力都有很大提高。
Abstract:
 Quantum-behaved Particle Swarm Optimization (QPSO) algorithm has defects that the capability of global optimization is not strong and is easy to fall into the local optimum. To solve this problem,an improved quantum-behaved particle swarm optimization is presented by introducing Gaussian distribution into the evolution equation,and the evolution equation of QPSO is substituted by mixed probability distribution evolution equation,and some particles were mutated in a definite probability by wavelet during evolution to in-crease the diversity of the particle population,avoiding the optimization process into local optimization,and improve the capability of global optimization. Finally,six typical test functions are employed to verify the improved method. The results show that the optimization capability and the ability to avoid local optimum of improved algorithm have been improved effectively.

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更新日期/Last Update: 2016-04-12