Comparison of various approaches in multi-objective particle swarm optimization (MOPSO): Empirical study

Abstract

This chapter presents a study of particle swarm optimization (PSO) method in multi-objective optimization problems. Many of these methods have focused on improving characteristics like convergence, diversity, and computational times by proposing effective ‘archiving’ and ‘guide selection’ techniques. What has still been lacking is an empirical study of these proposals in a common frame-work. In this chapter, an attempt to analyze these methods has been made; discussing their strengths and weaknesses. A multi-objective particle swarm optimization (MOPSO) algorithm, named dynamic multiple swarms in MOPSO is compared with other well known MOPSO techniques in which the number of swarms are adaptively adjusted throughout the search process via dynamic swarm strategy. The strategy allocates an appropriate number of swarms as required to support convergence and diversity criteria among the swarms. Additional novel designs include a PSO updating mechanism to better manage the communication within a swarm and among swarms and an objective space compression and expansion strategy to progressively exploit the objective space during the search process. Comparative study shows that the performance of the variant is competitive in comparison to the selected algorithms on standard benchmark problems. A dynamic MOPSO approach is compared and validated using several test functions and metrics taken from the standard literatures on evolutionary multi-objective optimization. Results indicate that the approach is highly competitive and that can be considered a viable alternative to solve multiobjective optimization problems.