An Improved Competitive Mechanism based Particle Swarm optimization Algorithm for Multi-Objective optimization

In this paper, an improved Competitive Mechanism-based Particle Swarm optimization algorithm called MCMOPSO is presented for multi-objective optimization. The algorithm consists of two main contributions: a new leader selection and the analysis of inertia weight. The new multi competition leader selection is introduced which is based on the pairwise competition. It will not only guide the particles to fly to the winner by comparing the nearest angle for two randomly selected elite particles, but also lead the particles to fly to the winner by comparing the nearest angle or farthest angle for several randomly selected elite particles in each iteration. To strike a balance between the exploration and exploitation of the velocity update equation for the original competitive mechanism-based MOPSO algorithm (CMOPSO), the influence of various inertia weights is investigated to control the previous velocity of each particle. The simulation results show that the proposed algorithm is outperformed four other famous multi-objective particle swarm optimization algorithms in thirty-seven benchmark test problems in terms of inverted generational distance.