Improvement of Elitism Preservation and Optimum Selection of Multi-objective Particle Swarm Optimization Algorithm

In order to improve the performance of MOPSO(multi-objective particle swarm optimization) algorithm for solving multiple objective problems,decrease the calculation complexity and ameliorate the convergence of this algorithm,a modified MOPSO algorithm was proposed.In this modified algorithm,the extended dominance(E-dominance) method is used to confirm the preference among all solutions and determine the best global position of current generation particles randomly.The modified algorithm considers the convergence and diversity of solutions.In addition,an exterior population file is utilized to preserve the elitist solutions and a non-linear function is used to map the objective space into a finite domain where the preference and distribution of the solutions are considered.Series of classical testing problems were investigated numerically.The simulation results show that this modified MOPSO algorithm surpasses the initial MOPSO and NSGA2(non-dominated sorting genetic algorithms 2) algorithms in the calculation complexity and the convergence when a multi-objective optimization problem possesses over three objectives.