Paper Info
Title
Enhancing particle swarm optimization using generalized opposition-based learning
Abstract
Particle swarm optimization (PSO) has been shown to yield good performance for solving various optimization problems. However, it tends to suffer from premature convergence when solving complex problems. This paper presents an enhanced PSO algorithm called GOPSO, which employs generalized opposition-based learning (GOBL) and Cauchy mutation to overcome this problem. GOBL can provide a faster convergence, and the Cauchy mutation with a long tail helps trapped particles escape from local optima. The proposed approach uses a similar scheme as opposition-based differential evolution (ODE) with opposition-based population initialization and generation jumping using GOBL. Experiments are conducted on a comprehensive set of benchmark functions, including rotated multimodal problems and shifted large-scale problems. The results show that GOPSO obtains promising performance on a majority of the test problems.
Year
DOI
Venue
2011
10.1016/j.ins.2011.03.016
Inf. Sci.
Keywords
Field
DocType
particle swarm optimization,promising performance,faster convergence,cauchy mutation,premature convergence,opposition-based population initialization,good performance,opposition-based differential evolution,opposition-based learning,enhancing particle swarm optimization,enhanced pso algorithm,generalized opposition-based learning,long tail,optimization problem,differential evolution
Convergence (routing),Particle swarm optimization,Population,Mathematical optimization,Premature convergence,Local optimum,Multi-swarm optimization,Differential evolution,Artificial intelligence,Optimization problem,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
181
20
0020-0255
Citations 
PageRank 
References 
109
2.65
30
Authors
5
Search Limit
100109
Name
Order
Citations
PageRank
Hui Wang138627.33
Zhijian Wu231321.20
Shahryar Rahnamayan3143983.52
Yong Liu42526265.08
Mario Ventresca540125.31