A Fast Multi-Objective Particle Swarm Optimization Algorithm Based on a New Archive Updating Mechanism

Multi-objective optimization has received increasing attention over the past few decades, and a large number of nature-inspired metaheuristic algorithms have been developed to solve multi-objective problems. An external archive is often used to store elite solutions in multi-objective algorithms. Since the archive size is limited, it must be truncated when the number of nondominated solutions exceeds its maximum size. Thus, the archive updating strategy is crucial due to its influence in the performance of the algorithm. However, achieving a fast convergence speed while assuring diversity of the obtained solutions is always a challenging task. In this paper, a novel multi-objective particle swarm optimization algorithm based on a new archive updating mechanism which depends on the nearest neighbor approach, called MOPSONN, is proposed. Two archive updating strategies are adopted to update nondominated solutions in the archive, which are beneficial to accelerate the convergence speed and maintain diversity of the swarm. The performance of MOPOSNN is evaluated on several benchmark problems and compared with seven state-of-the-art multi-objective algorithms, including four multi-objective particle swarm optimization algorithms and three multi-objective evolutionary algorithms. The experimental results demonstrate the significant effectiveness of MOPSONN in terms of convergence speed and spread of solutions.