Paper Info
Title
An improved local best searching in Particle Swarm Optimization using Differential Evolution
Abstract
Particle Swarm Optimization (PSO) has achieved remarkable attentions for its capability to solve diverse global optimization problems. However, this method also shows several limitations. PSO easily trapped in the global optimum and often required vast computational cost when solving high dimensional problems. Therefore, we propose some modifications to overcome these issues. In this work, Differential Evolution (DE) mutation and crossover operations are implemented to improve local best particles searching in PSO. A numerical analysis is carried out using benchmark functions and is compared with standard PSO and DE method. Results presented suggest the prospective of our proposed method.
Year
DOI
Venue
2011
10.1109/HIS.2011.6122090
HIS
Keywords
Field
DocType
hybrid method,evolutionary computation,local best searching,differential evolution,pso,particle swarm optimisation,global optimization problems,search problems,particle swarm optimization,improved local best searching,de,benchmark testing,genetic algorithms,genetic algorithm,global optimization,numerical analysis,hybrid intelligent system
Particle swarm optimization,Mathematical optimization,Crossover,Evolutionary computation,Multi-swarm optimization,Differential evolution,Numerical analysis,Mathematics,Benchmark (computing),Genetic algorithm
Conference
ISBN
Citations 
PageRank 
978-1-4577-2151-9
3
0.44
References 
Authors
9
5
Name
Order
Citations
PageRank
Afnizanfaizal Abdullah1303.62
Safaai Deris225642.99
Siti Zaiton Mohd Hashim329526.44
Mohd Saberi Mohamad413728.91
Satya Nanda Vel Arjunan531.12