Memetic particle swarm optimization
| dc.contributor.author | Petalas, Y. G. | en |
| dc.contributor.author | Parsopoulos, K. E. | en |
| dc.contributor.author | Vrahatis, M. N. | en |
| dc.date.accessioned | 2015-11-24T17:01:36Z | |
| dc.date.available | 2015-11-24T17:01:36Z | |
| dc.identifier.issn | 0254-5330 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/10951 | |
| dc.rights | Default Licence | - |
| dc.subject | global optimization | en |
| dc.subject | particle swarm optimization | en |
| dc.subject | memetic algorithms | en |
| dc.subject | local search | en |
| dc.subject | fuzzy cognitive maps | en |
| dc.subject | convergence | en |
| dc.subject | computation | en |
| dc.subject | algorithms | en |
| dc.subject | selection | en |
| dc.subject | evolution | en |
| dc.subject | design | en |
| dc.title | Memetic particle swarm optimization | en |
| heal.abstract | We propose a new Memetic Particle Swarm Optimization scheme that incorporates local search techniques in the standard Particle Swarm Optimization algorithm, resulting in an efficient and effective optimization method, which is analyzed theoretically. The proposed algorithm is applied to different unconstrained, constrained, minimax and integer programming problems and the obtained results are compared to that of the global and local variants of Particle Swarm Optimization, justifying the superiority of the memetic approach. | en |
| heal.access | campus | - |
| heal.fullTextAvailability | TRUE | - |
| heal.identifier.primary | DOI 10.1007/s10479-007-0224-y | - |
| heal.journalName | Annals of Operations Research | en |
| heal.journalType | peer reviewed | - |
| heal.language | en | - |
| heal.publicationDate | 2007 | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
| heal.type | journalArticle | - |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.type.en | Journal article | en |
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