Welcome to the InfoSci Platform
Reference Hub13
Population Based Equilibrium in Hybrid SA/PSO for Combinatorial Optimization: Hybrid SA/PSO for Combinatorial Optimization

Population Based Equilibrium in Hybrid SA/PSO for Combinatorial Optimization: Hybrid SA/PSO for Combinatorial Optimization

Kenneth Brezinski, Michael Guevarra, Ken Ferens
Copyright: © 2020 |Volume: 12 |Issue: 2 |Pages: 13
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781799806103|DOI: 10.4018/IJSSCI.2020040105
Cite Article Cite Article

MLA

Brezinski, Kenneth, et al. "Population Based Equilibrium in Hybrid SA/PSO for Combinatorial Optimization: Hybrid SA/PSO for Combinatorial Optimization." IJSSCI vol.12, no.2 2020: pp.74-86. http://doi.org/10.4018/IJSSCI.2020040105

APA

Brezinski, K., Guevarra, M., & Ferens, K. (2020). Population Based Equilibrium in Hybrid SA/PSO for Combinatorial Optimization: Hybrid SA/PSO for Combinatorial Optimization. International Journal of Software Science and Computational Intelligence (IJSSCI), 12(2), 74-86. http://doi.org/10.4018/IJSSCI.2020040105

Chicago

Brezinski, Kenneth, Michael Guevarra, and Ken Ferens. "Population Based Equilibrium in Hybrid SA/PSO for Combinatorial Optimization: Hybrid SA/PSO for Combinatorial Optimization," International Journal of Software Science and Computational Intelligence (IJSSCI) 12, no.2: 74-86. http://doi.org/10.4018/IJSSCI.2020040105

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

This article introduces a hybrid algorithm combining simulated annealing (SA) and particle swarm optimization (PSO) to improve the convergence time of a series of combinatorial optimization problems. The implementation carried out a dynamic determination of the equilibrium loops in SA through a simple, yet effective determination based on the recent performance of the swarm members. In particular, the authors demonstrated that strong improvements in convergence time followed from a marginal decrease in global search efficiency compared to that of SA alone, for several benchmark instances of the traveling salesperson problem (TSP). Following testing on 4 additional city list TSP problems, a 30% decrease in convergence time was achieved. All in all, the hybrid implementation minimized the reliance on parameter tuning of SA, leading to significant improvements to convergence time compared to those obtained with SA alone for the 15 benchmark problems tested.

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.