A multi-objective particle swarm optimizer with distance ranking and its applications to air compressor design optimization

Abstract

Multi-objective particle swarm optimization (MOPSO) has been steadily gaining attention from the research community because of its high convergence speed. On the other hand, in the face of increasing complexity and dimensionality of today’s application coupled with its tendency towards premature convergence related to the high convergence speeds, it is necessary to improve the global convergence and uniform distribution of MOPSO. A novel crowding distance ranking-based particle swarm optimizer is proposed (DMOPSO). With the elitism strategy, the evolution of the external swarm is achieved based on particles’ crowding distance ranking by descending order, to delete the repetitive ones in the crowded area. The update of the global optimum is performed by selecting a particle with relatively bigger crowding distance, to lead the swarm evolving to the disperse region. A small ratio mutation is also introduced to the inner swarm to enhance the global searching capacity of the algorithm. So the number of Pareto optimal solutions can be controlled, and the convergence and diversity of Pareto optimal set can also be guaranteed. The experiment on the optimization of single-stage air compressor showed that DMOPSO handled problems with two and three objectives efficiently, and outperformed the comparison algorithms in terms of the convergence and diversity of the Pareto front. The robustness was illustrated through sensitivity analysis for key parameters.