Oppositionbased multiobjective particle swarm optimization algorithm based on tripartite competition mechanism
1. School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China; 2. Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace, Jiangsu University, Zhenjiang, Jiangsu 212013, China
Abstract：To solve the problem of premature in traditional multiobjective particle swarm optimization algorithms, an oppositionbased multiobjective particle swarm optimization algorithm was proposed based on tripartite competition mechanism (MOPSOTCOL). The tripartite competitors were selected from the current population in each generation to guide the population evolution in MOPSOTCOL, which could effectively reduce the computational cost. In each competition, three particles were randomly selected from the population for comparison and updated by different strategies, which could help to maintain the diversity of population. A novel progressive particle update strategy was proposed based on oppositionbased learning (OBL). Some particles were updated by OBL strategy to avoid the algorithm from falling into local optima, and other particles were updated by learning from the specified better particles to improve the convergence. The experimental results on 14 benchmark test instances verify the superiority of the proposed algorithm in terms of diversity and convergence over 8 multiobjective optimization algorithms, and it has faster convergence rate.
