Impact of Controlling Parameters on the Performance of MOPSO Algorithm
Rajani Rajani, Dinesh Kumar, Vijay Kumar
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.
Topics & Concepts
Computer scienceBenchmark (surveying)InertiaParticle swarm optimizationConvergence (economics)Task (project management)Mathematical optimizationPareto principleMulti-objective optimizationAlgorithmOptimization algorithmMachine learningMathematicsGeographyManagementGeodesyClassical mechanicsEconomicsEconomic growthPhysicsMetaheuristic Optimization Algorithms ResearchAdvanced Multi-Objective Optimization AlgorithmsEvolutionary Algorithms and Applications