Dynamic Population Size in PSO-based Multiobjective Optimization

Most existing multiobjective particle swarm optimization (MOPSO) designs generally "estimate" a fixed population size sufficiently to explore the search space without incurring excessive computational complexity. In this paper, we propose a MOPSO design to solve multiobjective optimization problems, known as the dynamic population multiple-swarm MOPSO (DMOPSO). The proposed algorithm incorporates strategies to adjust the population size to enhance exploration capability. An additional feature, adaptive local archive, is designed to improve the diversity within each swarm. Compared with some state-of-the-art MOPSO algorithms, the proposed algorithm shows competitive results with improved diversity and convergence.