Particle swarm optimization algorithm with composite strategy inertia weight

doi:10.3724/SP.J.1087.2012.02216

Journal of Computer Applications ›› 2012, Vol. 32 ›› Issue (08): 2216-2218.DOI: 10.3724/SP.J.1087.2012.02216

• Artificial intelligence • Previous Articles Next Articles

Particle swarm optimization algorithm with composite strategy inertia weight

GAO Zhen-hua¹,MEI Li¹,ZHU Yuan-jian²

1. School of Management Science and Engineering, Anhui University of Technology, Maanshan Anhui 343002, China
2. School of Software Engineering, Huazhong University of Science and Technology, Wuhan Hubei 430074, China

Received:2012-02-23 Revised:2012-04-16 Online:2012-08-28 Published:2012-08-01
Contact: GAO Zhen-hua

复合策略惯性权重的粒子群优化算法

郜振华¹,梅莉¹,祝远鉴²

1. 安徽工业大学管理科学与工程学院，安徽马鞍山 243002
2. 华中科技大学软件学院，武汉 430074

通讯作者: 郜振华
作者简介:郜振华(1972-)，男，安徽濉溪人，副教授，博士，CCF会员，主要研究方向：人工智能;
梅莉(1984-)，女，江苏盐城人，硕士研究生，主要研究方向：物流系统优化;
祝远鉴(1979-)，男，江苏徐州人，主要研究方向：人工智能。
基金资助:
国家自然科学基金资助项目(71172219);国家自然科学基金资助项目(61174175)

Abstract

Abstract: A new Particle Swarm Optimization (PSO) algorithm with linearly decreasing and dynamically changing inertia weight named L-DPSO was presented to solve the problem that the linearly decreasing inertia weight of the PSO cannot match with the nonlinear changing characteristic. The linear strategy of linearly decreasing inertia weight and the nonlinear strategy of dynamically changing inertia weight were used in the algorithm. The weights were given to two methods separately. Using the test functions of Griewank and Rastrigin to compare L-DPSO with linearly decreasing inertia weight (LPSO) and dynamically changing inertia weight (DPSO), the experimental results show that the convergence speed of L-DPSO is obviously superior to LPSO and DPSO, and the convergence accuracy is also increased. At last, the test functions of Griewank and Rastrigin were used to compare L-DPSO with several commonly used inertia weights, and results show that L-DPSO has obvious advantage too.

Key words: Particle Swarm Optimization (PSO), inertia weight, composite strategy

摘要： 针对粒子群优化算法中典型线性递减策略的惯性权重不能和运算过程中非线性变化的特点相匹配的问题，提出一种用典型线性递减策略和动态变化策略相结合的方法来确定惯性权重的粒子群优化算法(L-DPSO)。该算法充分利用了线性递减策略的线性和动态变化策略的非线性特点，对两种策略赋予了相应的权重。然后将L-DPSO算法和单独使用典型线性递减策略来确定惯性权重的粒子群优化算法(LPSO)及单独使用动态变化策略来确定惯性权重的粒子群优化算法(DPSO)进行比较，用Griewank和Rastrigin函数进行测试，结果表明，适当调整典型线性递减策略和动态变化策略的权重，L-DPSO算法的收敛速度明显优于LPSO和DPSO算法，收敛精度也有所提高。最后，对L-DPSO算法和几种常用的惯性权重计算方法确定的粒子群优化算法作比较，用Griewank和Rastrigin函数进行测试，结果表明L-DPSO算法也有明显优势。

关键词: 粒子群优化, 惯性权重, 复合策略

CLC Number:

TP183

GAO Zhen-hua MEI Li ZHU Yuan-jian. Particle swarm optimization algorithm with composite strategy inertia weight[J]. Journal of Computer Applications, 2012, 32(08): 2216-2218.

郜振华梅莉祝远鉴. 复合策略惯性权重的粒子群优化算法[J]. 计算机应用, 2012, 32(08): 2216-2218.

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Particle swarm optimization algorithm with composite strategy inertia weight

复合策略惯性权重的粒子群优化算法

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