A Distance Sorting Based Multi-Objective Particle Swarm Optimizer and Its Applications

Multi-objective particle swarm optimization (MOPSO) is an optimization technique inspired by bird flocking, which 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 of premature convergence due to the high convergence speeds, there is a need to improve the efficiency and effectiveness of MOPSO. A novel crowding distance sorting based particle swarm optimizer is proposed (called DSMOPSO). It includes three major improvements: (I) With the elitism strategy, the evolution of the external population is achieved based on individuals’ crowding distance sorting by descending order, to delete the redundant individuals in the crowded area; (II) The update of the global optimum is performed by selecting individuals with a relatively bigger crowding distance, which leading particles evolve to the disperse region; (III) A small ratio mutation is introduced to the inner swarm to enhance the global searching capability. Experiment results on the design of single-stage air compressor show that DSMOPSO handling problems with two and three objectives efficiently, and outperforms SPEA2 in the convergence and diversity of the Pareto front.