Paper Info
Title
Fast multi-swarm optimization with cauchy mutation and crossover operation
Abstract
The standard Particle Swarm Optimization (PSO) algorithm is a novel evolutionary algorithm in which each particle studies its own previous best solution and the group's previous best to optimize problems. One problem exists in PSO is its tendency of trapping into local optima. In this paper, a multiple swarms technique(FMSO) based on fast particle swarm optimization(FPSO) algorithm is proposed by bringing crossover operation. FPSO is a global search algorithm witch can prevent PSO from trapping into local optima by introducing Cauchy mutation. Though it can get high optimizing precision, the convergence rate is not satisfied, FMSO not only can find satisfied solutions, but also speeds up the search. By proposing a new information exchanging and sharing mechanism among swarms. By comparing the results on a set of benchmark test functions, FMSO shows a competitive performance with the improved convergence speed and high optimizing precision.
Year
DOI
Venue
2007
10.1007/978-3-540-74581-5_38
ISICA
Keywords
Field
DocType
multi-swarm optimization,cauchy mutation,own previous best solution,local optimum,high optimizing precision,multiple swarms technique,novel evolutionary algorithm,fast particle swarm optimization,global search algorithm witch,convergence rate,improved convergence speed,crossover operation,swarm intelligence,search algorithm,particle swarm optimization,optimization problem,satisfiability,information exchange,evolutionary algorithm
Particle swarm optimization,Mathematical optimization,Search algorithm,Evolutionary algorithm,Local optimum,Swarm intelligence,Multi-swarm optimization,Imperialist competitive algorithm,Mathematics,Metaheuristic
Conference
Volume
ISSN
ISBN
4683
0302-9743
3-540-74580-7
Citations 
PageRank 
References 
6
0.52
8
Authors
4
Name
Order
Citations
PageRank
Qing Zhang111432.59
Changhe Li2104443.37
Yong Liu32526265.08
Lishan Kang477591.11