Improved particle swarm optimization with a collective local unimodal search for continuous optimization problems.
A new local search technique is proposed and used to improve the performance of particle swarm optimization algorithms by addressing the problem of premature convergence. In the proposed local search technique, a potential particle position in the solution search space is collectively constructed by...
| Publicado en: | Scientific World Journal pp. 798129 - 798130 |
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| Autores principales: | , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=103817801&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103817801 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1537744X 1BX5 jtl: Scientific World Journal issn: 1537744X maglogo: N pubinfo: dt: 2014 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 103817801 103817801 NLM24723827 2012547753 10.1155/2014/798129 NLM24723827 PMC3956635 103817801 ppf: 798129 ppct: 1 formats: tig: atl: Improved particle swarm optimization with a collective local unimodal search for continuous optimization problems. aug: au: Arasomwan, Martins Akugbe Adewumi, Aderemi Oluyinka affil: School of Mathematics, Statistics, and Computer Science, University of Kwazulu-Natal South Africa, Private Bag X54001, Durban 4000, South Africa. sug: subj: Models, Theoretical Particle Swarm Optimization Algorithms ab: A new local search technique is proposed and used to improve the performance of particle swarm optimization algorithms by addressing the problem of premature convergence. In the proposed local search technique, a potential particle position in the solution search space is collectively constructed by a number of randomly selected particles in the swarm. The number of times the selection is made varies with the dimension of the optimization problem and each selected particle donates the value in the location of its randomly selected dimension from its personal best. After constructing the potential particle position, some local search is done around its neighbourhood in comparison with the current swarm global best position. It is then used to replace the global best particle position if it is found to be better; otherwise no replacement is made. Using some well-studied benchmark problems with low and high dimensions, numerical simulations were used to validate the performance of the improved algorithms. Comparisons were made with four different PSO variants, two of the variants implement different local search technique while the other two do not. Results show that the improved algorithms could obtain better quality solution while demonstrating better convergence velocity and precision, stability, robustness, and global-local search ability than the competing variants. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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