Welcome to the InfoSci Platform
Reference Hub4
An Improved Multi-Objective Particle Swarm Optimization Based on Utopia Point Guided Search

An Improved Multi-Objective Particle Swarm Optimization Based on Utopia Point Guided Search

Swapnil Prakash Kapse, Shankar Krishnapillai
Copyright: © 2018 |Volume: 9 |Issue: 4 |Pages: 26
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781522544555|DOI: 10.4018/IJAMC.2018100104
Cite Article Cite Article

MLA

Kapse, Swapnil Prakash, and Shankar Krishnapillai. "An Improved Multi-Objective Particle Swarm Optimization Based on Utopia Point Guided Search." IJAMC vol.9, no.4 2018: pp.71-96. http://doi.org/10.4018/IJAMC.2018100104

APA

Kapse, S. P. & Krishnapillai, S. (2018). An Improved Multi-Objective Particle Swarm Optimization Based on Utopia Point Guided Search. International Journal of Applied Metaheuristic Computing (IJAMC), 9(4), 71-96. http://doi.org/10.4018/IJAMC.2018100104

Chicago

Kapse, Swapnil Prakash, and Shankar Krishnapillai. "An Improved Multi-Objective Particle Swarm Optimization Based on Utopia Point Guided Search," International Journal of Applied Metaheuristic Computing (IJAMC) 9, no.4: 71-96. http://doi.org/10.4018/IJAMC.2018100104

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

This article demonstrates the implementation of a novel local search approach based on Utopia point guided search, thus improving the exploration ability of multi- objective Particle Swarm Optimization. This strategy searches for best particles based on the criteria of seeking solutions closer to the Utopia point, thus improving the convergence to the Pareto-optimal front. The elite non-dominated selected particles are stored in an archive and updated at every iteration based on least crowding distance criteria. The leader is chosen among the candidates in the archive using the same guided search. From the simulation results based on many benchmark tests, the new algorithm gives better convergence and diversity when compared to existing several algorithms such as NSGA-II, CMOPSO, SMPSO, PSNS, DE+MOPSO and AMALGAM. Finally, the proposed algorithm is used to solve mechanical design based multi-objective optimization problems from the literature, where it shows the same advantages.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.