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A Novel Multi-Objective Competitive Swarm Optimization Algorithm

A Novel Multi-Objective Competitive Swarm Optimization Algorithm

Prabhujit Mohapatra, Kedar Nath Das, Santanu Roy, Ram Kumar, Nilanjan Dey
Copyright: © 2020 |Volume: 11 |Issue: 4 |Pages: 16
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781799802877|DOI: 10.4018/IJAMC.2020100106
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MLA

Mohapatra, Prabhujit, et al. "A Novel Multi-Objective Competitive Swarm Optimization Algorithm." IJAMC vol.11, no.4 2020: pp.114-129. http://doi.org/10.4018/IJAMC.2020100106

APA

Mohapatra, P., Das, K. N., Roy, S., Kumar, R., & Dey, N. (2020). A Novel Multi-Objective Competitive Swarm Optimization Algorithm. International Journal of Applied Metaheuristic Computing (IJAMC), 11(4), 114-129. http://doi.org/10.4018/IJAMC.2020100106

Chicago

Mohapatra, Prabhujit, et al. "A Novel Multi-Objective Competitive Swarm Optimization Algorithm," International Journal of Applied Metaheuristic Computing (IJAMC) 11, no.4: 114-129. http://doi.org/10.4018/IJAMC.2020100106

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Abstract

In this article, a new algorithm, namely the multi-objective competitive swarm optimizer (MOCSO), is introduced to handle multi-objective problems. The algorithm has been principally motivated from the competitive swarm optimizer (CSO) and the NSGA-II algorithm. In MOCSO, a pair wise competitive scenario is presented to achieve the dominance relationship between two particles in the population. In each pair wise competition, the particle that dominates the other particle is considered the winner and the other is consigned as the loser. The loser particles learn from the respective winner particles in each individual competition. The inspired CSO algorithm does not use any memory to remember the global best or personal best particles, hence, MOCSO does not need any external archive to store elite particles. The experimental results and statistical tests confirm the superiority of MOCSO over several state-of-the-art multi-objective algorithms in solving benchmark problems.

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