Online elite archiving in multi-objective particle swarm optimization

A multi-objective particle swarm optimization algorithm based on online elite archiving is proposed. The elite particles are put into repository. Fitness sharing is adopted to select global best position from the repository, thus the diversity of the population is guaranteed. In the course of evolution the online archiving technique is adopted. The elite particles in the repository are introduced into the population and inferior individuals are eliminated. Thus an excellent population is ensured. Two Zitzler functions are used to evaluate the performance of the proposed approach. Experiments demonstrated that the proposed method can rapidly converge and can effectively generate a satisfactory approximation of the Pareto front.