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Hybridization of particle swarm optimization with adaptive genetic algorithm operators

conference contribution
posted on 2023-05-23, 08:57 authored by Masrom, S, Moser, I, James MontgomeryJames Montgomery, Abidin, SZZ, Omar, N
Particle Swarm Optimization (PSO) is a popular algorithm used extensively in continuous optimization. One of its well-known drawbacks is its propensity for premature convergence. Many techniques have been proposed for alleviating this problem. One of the alternative approaches is hybridization. Genetic Algorithms (GA) are one of the possible techniques used for hybridization. Most often, a mutation scheme is added to the PSO, but some applications of crossover have been added more recently. Some of these schemes use adaptive parameterization when applying the GA operators. In this work, adaptively parameterized mutation and crossover operators are combined with a PSO implementation individually and in combination to test the effectiveness of these additions. The results indicate that an adaptive approach with position factor is more effective for the proposed PSO hybrids. Compared to single PSO with adaptive inertia weight, all the PSO hybrids with adaptive probability have shown satisfactory performance in generating near-optimal solutions for all tested functions.

History

Publication title

Proceedings of the 2013 International Conference on Intelligent Systems Design and Applications

Editors

A Abraham, MN Sulaiman, LS Affendey, D Lukose, E Corchado, CY Huoy, K Ma

Pagination

1-6

ISBN

978-1-4799-3516-1

Department/School

School of Information and Communication Technology

Publisher

IEEE

Place of publication

United States of America

Event title

2013 International Conference on Intelligent Systems Design and Applications

Event Venue

Malaysia

Date of Event (Start Date)

2013-12-08

Date of Event (End Date)

2013-12-10

Rights statement

Copyright 2013 IEEE

Repository Status

  • Restricted

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Expanding knowledge in the information and computing sciences

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