Hybrid particle swarm optimization algorithm with cooperation of multiple particle roles

doi:10.11772/j.issn.1001-9081.2014.08.2306

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (8): 2306-2310.DOI: 10.11772/j.issn.1001-9081.2014.08.2306

• Artificial intelligence • Previous Articles Next Articles

Hybrid particle swarm optimization algorithm with cooperation of multiple particle roles

WU Yiting,DAI Mingyue,JI Zhicheng,WU Dinghui

School of Internet of Things Engineering, Jiangnan University, Wuxi Jiangsu 214122, China

Received:2014-02-26 Revised:2014-04-13 Online:2014-08-10 Published:2014-08-01
Contact: WU Yiting

多粒子角色协同作用的混合粒子群优化算法

吴逸庭,戴月明,纪志成,吴定会

江南大学物联网工程学院，江苏无锡214122

通讯作者: 吴逸庭
作者简介:吴逸庭(1988-)，男，江苏无锡人，硕士研究生，主要研究方向：人工智能、软件工程、软件测试；戴月明(1964-)，男，江苏常熟人，副教授，硕士，主要研究方向：人工智能、模式识别、数据挖掘；纪志成(1959-)，男，浙江杭州人，教授，博士，主要研究方向：风能转换系统控制、复杂非线性系统控制、网络控制、智能控制；吴定会(1970-)，男，安徽庐江人，副教授，博士，主要研究方向：新能源控制。
基金资助:
国家863计划项目;江苏省产学研联合创新基金资助项目

Abstract

Abstract:

Concerning the problem that Particle Swarm Optimization (PSO) falls into local minima easily and converges slowly at the last stage, a kind of hybrid PSO algorithm with cooperation of multiple particle roles (MPRPSO) was proposed. The concept of particle roles was introduced into the algorithm to divide the population into three roles: Exploring Particle (EP), Patrolling Particle (PP) and Local Exploiting Particle (LEP). In each iteration, EP was used to search the solution space by the standard PSO algorithm, and then PP which was based on chaos was used to strengthen the global search capability and replace some EPs to restore population vitality when the algorithm trapped in local optimum. Finally, LEP was used to strengthen the local search to accelerate convergence by unidimensional asynchronous neighborhood search. The 30 times independent runs in the experiment show that, the proposed algorithm in the conditions that particle roles ratio is 0.8∶〖KG-*3〗0.1∶〖KG-*3〗0.1 has the mean value of 2.352E-72,4.678E-29,7.780E-14 and 2.909E-14 respectively in Sphere, Rosenbrock, Ackley and Quadric, and can converge to the optimal solution of 0 in Rastrigrin and Griewank, which is better than the other contrastive algorithms. The experimental results show that proposed algorithm improves the optimal performance with certain robustness.

摘要：

针对粒子群优化(PSO)算法易陷入局部最优和后期收敛速度慢的问题，提出一种多粒子角色协同作用的混合粒子群算法(MPRPSO)。引入粒子角色的概念，将种群粒子分成探索粒子(EP)、巡逻粒子(PP)和局部开发粒子(LEP)三类角色，在每次迭代中利用探索粒子以标准PSO算法搜索解空间，用基于混沌的巡逻粒子加强全局搜索，并在陷入局部最优时替代部分探索粒子，恢复种群活力。最后通过局部开发粒子的单维异步邻域搜索加强算法局部搜索能力，加快收敛。实验独立运行30次，所提算法在粒子角色比例为0.8∶〖KG-*2〗0.1∶〖KG-*2〗0.1的条件下，在Sphere、Rosenbrock、Ackley和Quadric函数中获得的平均值分别为2.352E-72、4.678E-29、7.780E-14和2.909E-14，尤其在Rastrigrin与Griewank函数中能收敛到最优解0，优于其他对比算法。实验结果表明，所提算法在优化性能上有所提高，并有一定的鲁棒性。

CLC Number:

TP301.6
TP18

WU Yiting DAI Mingyue JI Zhicheng WU Dinghui. Hybrid particle swarm optimization algorithm with cooperation of multiple particle roles[J]. Journal of Computer Applications, 2014, 34(8): 2306-2310.

吴逸庭戴月明纪志成吴定会. 多粒子角色协同作用的混合粒子群优化算法[J]. 计算机应用, 2014, 34(8): 2306-2310.

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Hybrid particle swarm optimization algorithm with cooperation of multiple particle roles

多粒子角色协同作用的混合粒子群优化算法

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