Combining Particle Swarm Optimizer with SQP Local Search for Constrained Optimization Problems

Carwyn Pelley, Mauro Innocente, Johann Sienz

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Abstract

The combining of a General-Purpose Particle Swarm Optimizer (GP-PSO) with Sequential Quadratic Programming (SQP) algorithm for constrained optimization problems has been shown to be highly beneficial to the refinement, and in some cases, the success of finding a global optimum solution. It is shown that the likely difference between leading algorithms are in their local search ability. A comparison with other leading optimizers on the tested benchmark suite, indicate the hybrid GP-PSO with implemented local search to compete along side other leading PSO algorithms.
Original languageEnglish
Title of host publicationProceedings of the 8th ASMO UK Conference on Engineering Design Optimization
PublisherAssociation for Structural and Multidisciplinary Optimization in the UK
Pages268-279
Number of pages12
ISBN (Print)978–0–85316–292–6
Publication statusPublished - 2010
Externally publishedYes
Event8th ASMO UK Conference on Engineering Design Optimization - Queen Mary University of London, London, United Kingdom
Duration: 8 Jul 20109 Jul 2010
Conference number: 8
http://www.asmo-uk.com/8th-asmo-uk/html/menu_page.html

Conference

Conference8th ASMO UK Conference on Engineering Design Optimization
CountryUnited Kingdom
CityLondon
Period8/07/109/07/10
Internet address

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