Multi-objective Particle Swarm Optimization based on adaptive mutation

In recent decade Evolutionary Algorithms plays an important role in many engineering design and optimization problems. Particle Swarm Optimization (PSO) is one of such algorithm which is based on the intelligent food searching behavior of swarm like birds flock, fish schooling. It has been shown that it works efficiently on noisy, multimodal and composite functions. However, it stuck at local optima at later stage of evolution due to unexplore the search space. Several variations of pso and mutation based approached was developed for this problem. In this paper, an adaptive mutation is proposed for multiobjective pso and called it AMPSO. In AMPSO, mutation is applied on the position and velocity of the particles based on the fitness values of the particles. Proposed algorithm carried on 5 multiobjective benchmark functions. The experimental results shown the better performance comparing with other algorithms in terms of best, mean and standard deviation.