Rapid prototyping for low-level hybridization of PSO-GA

Responding to the difficulties of implementing low-Level Hybridization (LLH), this paper introduces general implementation frameworks for the algorithm focusing on the two well-known meta-heuristics namely Particle Swarm Optimization( PSO) and Genetic Algorithm (GA). Furthermore, in order to provide...

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Bibliographic Details
Published in:Frontiers in Artificial Intelligence and Applications
Main Author: Masrom S.; Abidin S.Z.Z.; Omar N.; Nasir K.
Format: Conference paper
Language:English
Published: IOS Press BV 2014
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84948783246&doi=10.3233%2f978-1-61499-434-3-495&partnerID=40&md5=99f0b593be3fde54bf05df7e24263291
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Summary:Responding to the difficulties of implementing low-Level Hybridization (LLH), this paper introduces general implementation frameworks for the algorithm focusing on the two well-known meta-heuristics namely Particle Swarm Optimization( PSO) and Genetic Algorithm (GA). Furthermore, in order to provide a more effective approach for the rapid algorithm design and programming implementation, a set of scripting language constructs has been developed that employs the proposed implementation frameworks. For evaluation, twelve algorithms that composed of nine LLHs and three single PSO have been coded and executed with the scripting language. The codes are shown to concisely describe the algorithm in a directly publishable form. In addition, the remarkable optimization results of the LLHs has demonstrated the effectiveness of the LLH mechanisms provided by the proposed implementation frameworks. © 2014 The authors and IOS Press. All rights reserved.
ISSN:9226389
DOI:10.3233/978-1-61499-434-3-495