Multi-Objective Genetic and Adaptive Particle Swarm Optimization Algorithms: A Performance Analysis with Benchmark functions

Abstract

The multi-objective genetic algorithm(MOGA) and adaptive particle swarm optimization(APSO) algorithm are new variants of natural population-based search methods with the effective capability to solve extremely nonlinear mixed integer optimized complex engineering problems. This paper attempts to examine the performance and convergence analysis for MOGA and APSO algorithm with benchmarked test functions namely Rosen Brock, Six Hump Camel Back, Goldstein-Price’s and Rastrigin. The numerical simulation results indicate the adaptive particle swarm optimization algorithm was able to find the best solutions than a multi-objective genetic algorithm.