Multi-swarm Particle Swarm Optimizer with Cauchy Mutation for Dynamic Optimization Problems

Authors:
Chengyu Hu;Xiangning Wu;Yongji Wang;Fuqiang Xie
Affiliations:
Department of Control Science and Engineering, Huazhong University of Science & Technology, Wuhan, China 430074 and School of Computer, China University of Geosciences, Wuhan, China 430074;School of Computer, China University of Geosciences, Wuhan, China 430074;Department of Control Science and Engineering, Huazhong University of Science & Technology, Wuhan, China 430074;Department of Control Science and Engineering, Huazhong University of Science & Technology, Wuhan, China 430074
Venue:
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
Year:
2009

Citing 6
Cited 1

Recent approaches to global optimization problems through Particle Swarm Optimization

Natural Computing: an international journal
Adaptive particle swarm optimization: detection and response to dynamic systems

CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Don't push me! Collision-avoiding swarms

CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
The particle swarm - explosion, stability, and convergence in amultidimensional complex space

IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey

IEEE Transactions on Evolutionary Computation
Multiswarms, exclusion, and anti-convergence in dynamic environments

IEEE Transactions on Evolutionary Computation

Adaptive particle swarm optimization algorithm for dynamic environments

ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

Many real-world problems are dynamic, requiring an optimization algorithm which is able to continuously track a changing optimum over time. This paper presents a new variant of Particle Swarm Optimization (PSO) specifically designed to work well in dynamic environments. The main idea is to divide the population of particles into a set of interacting swarms. These swarms interact locally by dynamic regrouping and dispersing. Cauchy mutation is applied to the global best particle when the swarm detects the environment of the change. The dynamic function (proposed by Morrison and De Jong) is used to test the performance of the proposed algorithm. The comparison of the numerical experimental results with those of other variant PSO illustrates that the proposed algorithm is an excellent alternative to track dynamically changing optima.