An exploratory study of sorting particle swarm optimizer for multiobjective optimization

To solve the problems such as low global search capability and insufficient diversity of Pareto optimal set existed in MOPSO, a multiobjective particle swarm optimization algorithm based on crowding distance sorting is proposed. An external population is preserved to store the nondominated individuals during the evolution process. The shrink of the external population is achieved based on individuals' crowding distance sorting by descending order, which deleting the redundant individuals in the crowding area. An individual with relatively big crowding distance is selected as the global best to lead the particles evolving to the disperse region. The dominant relation between individuals is compared with the constraint Pareto dominance to embody the constraints without external parameters. The experiments of six standard unconstraint test problems illustrate that the new algorithm is competitive with NSGA-II and SPEA2 in terms of converging to the true Pareto front and maintaining the diversity of the population. The effectiveness of the algorithm for constraint problems is proved by solving three constraint test problems. Moreover, the best value ranges of mutation rate and inertia weight are analyzed by numerical experiments to guarantee the steady convergence of the algorithm.