A Competitive Mechanism Multi-Objective Particle Swarm Optimization Algorithm and Its Application to Signalized Traffic Problem

In this paper, a modified Competitive Mechanism Multi-Objective Particle Swarm Optimization (MCMOPSO) algorithm is presented for multi-objective optimization. The algorithm consists of an improved leader selection scheme called multi-competition leader selection. Under this scheme, particles move to the winner among the elite particles for the social cognitive by comparing the nearest angle or the farthest angle of several randomly selected elite particles. Besides, as the inertia weight plays an important role in controlling the previous velocity of each particle, the competitive mechanism is applied to the inertia weight in order to investigate for the most suitable balance between the exploration and exploitation abilities of the algorithm during the search process. The experimental results show that the proposed algorithm outperforms four other popular multi-objective particle swarm optimization algorithms most of the time on thirty-seven benchmarks in terms of inverted generational distance. Furthermore, the proposed algorithm is applied to the signalized traffic problem to optimize the effective green time of each phase, and the proposed algorithm performs better than other MOPSO algorithms for the traffic problem in terms of hypervolume.