Abstract
Parameter setting plays a vital role in the performance of optimization algorithms. Selecting the right parameters for the given problem is a challenging task. In this paper, the effect of three parameters on Optimized Multi-objective Particle Swarm Optimization algorithm is analyzed. The parameters include inertia, cognitive, and social. The impact of these parameters is evaluated on five well-known benchmark test functions. The convergence and Pareto front analysis are also done on OMOPSO. Experimental results show the impact of parameters using three performance metrics.
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