Abstract:

A new hybrid multiobjective optimizer is presented, which combines particle swarm optimization (PSO) with an innovative optimal particles local search strategy based on bound optimization by quadratic approximation (BOBYQA) algorithm. The main goal of the approach is to improve the convergence performance of PSO and diversity of nondominated set. The new approach constructs the leader particles set using crowding distance to select leader particles, then makes full use of the optimal particles method to guide leader particles approach the Pareto front quickly. Meanwhile, a new local optimal particles search strategy is proposed after analysis on disadvantage of the global optimal particles search method. Furthermore, the multidimensional uniform mutation is introduced to prevent algorithm from being trapped into local optimum. Simulation results of benchmark functions show that our approach is highly competitive in convergence speed and generates a well distributed and accurate set of nondominated solutions.