DOI: 10.4018/IJAMC.2020100106 International Journal of Applied Metaheuristic Computing Volume 11 • Issue 4 • October-December 2020 Copyright © 2020, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 114 A Novel Multi-Objective Competitive Swarm Optimization Algorithm Prabhujit Mohapatra, VIT University, Vellore, Tamilnadu, India Kedar Nath Das, NIT Silchar, Silchar, India Santanu Roy, NIT Silchar, Silchar, India Ram Kumar, Katihar Engineering College, Katihar, India Nilanjan Dey, Techno India College of Technology, West Bengal, India 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. KeywORdS Competitive Swarm Optimizer, Evolutionary Algorithms, Multi-Objective Optimization, Non-Dominating Sorting, Pareto Front, Particle Swarm Optimization, Particle Swarm Optimizer, Swarm Intelligence INTROdUCTION The general form of a multi-objective problem can be represented as follows: min , , , ., Fv f v f v f v f v m () = () () () … () ( ) 1 2 3 subject to v ∈Ψ (1) where v is decision variable in the search space Ψ . The objective vector F R m : Ψ→ maps the decision variable v in Ψ to m number of objective functions in the objective space R m . In real life, many optimization problems as defined in (1) often need to optimize conflicting objectives simultaneously. Optimization of one objective often costs the other objectives due to the trade-offs between them. Hence, it is not possible to find a single solution to optimize all the objectives