A novel hybrid charge system search and particle swarm optimization method for multi-objective optimization

In this paper a new hybrid method is proposed for multi-objective optimization problem. In multi-objective particle swarm optimization methods, selecting the global best particle for each particle of the population from a set of Pareto-optimal solutions has a great impact on the convergence and diversity of solutions. Here, this problem is solved by incorporating charged system search method into the search process of the particle swarm optimization algorithm. In this approach, each particle is guided by its personal best and also resultant force which acted on this particle. This force is the consequence of the attraction field which is created around each archive member, where the magnitude of this force is related to the charge magnitude of the particles and also the distance between them. Each particle is guided by just archive members, which are located in the same region of the objective space as this particle. The proposed method is examined for different test functions and the results are compared to the results of three state-of-art multi-objective algorithms.