Multiobjective Particle Swarm Optimization Algorithm Based on Adaptive Angle Division

It is difficult to balance the convergence and diversity at the same time and select the globally optimal particle (gbest) in the multiobjective particle swarm optimization (MOPSO) process. In this paper, a novel method based on adaptive angle division is proposed targeting at archive maintenance and gbest selection. According to the number of particles in the current archive, the angle region of the target space is adaptively adjusted and uniformly divided. The globally optimal particle is selected from the low-density angle region of the particle distribution; in the meanwhile, the superfluous particles are deleted from the high-density angle region. Consequently, the selection of gbest and update of external archive set which including the generation of the optimal solution set and maintenance of external archive set are synchronized. The convergence and diversity are ensured while improving the uniformity of the optimal solution set. In addition, the current highest non-dominated particles in each angle region are preserved, and the particles in adjacent regions are selected to conduct global guidance for such regions and regions of the particle-free distribution area. Through these two ways, the diversity of the population is maintained while the coverage spreadability of the optimal solution set in the target space is enhanced. Four state-of-the-art evolution multiobjective optimization (EMO) algorithms and the two classic EMO algorithms are selected as the peer algorithms to validate multiobjective particle swarm optimization based on adaptive angle division (AADMOPSO) algorithm. A series of extensive experiments are conducted on five groups of standard test functions. The experimental results show the effectiveness and competitiveness of the proposed AADMOPSO in balancing convergence and diversity.