School of Information Technology and Software, Northwest University, Xi'an 710127, China.
Key Laboratory for Geo-Hazards in Loess Area, MNR, Xi'an Center of Geological Survey, China Geological Survey, Xi'an 710054, China.
Comput Intell Neurosci. 2020 Feb 19;2020:5132803. doi: 10.1155/2020/5132803. eCollection 2020.
The recently proposed multiobjective particle swarm optimization algorithm based on competition mechanism algorithm cannot effectively deal with many-objective optimization problems, which is characterized by relatively poor convergence and diversity, and long computing runtime. In this paper, a novel multi/many-objective particle swarm optimization algorithm based on competition mechanism is proposed, which maintains population diversity by the maximum and minimum angle between ordinary and extreme individuals. And the recently proposed -dominance is adopted to further enhance the performance of the algorithm. The proposed algorithm is evaluated on the standard benchmark problems DTLZ, WFG, and UF1-9 and compared with the four recently proposed multiobjective particle swarm optimization algorithms and four state-of-the-art many-objective evolutionary optimization algorithms. The experimental results indicate that the proposed algorithm has better convergence and diversity, and its performance is superior to other comparative algorithms on most test instances.
最近提出的基于竞争机制的多目标粒子群优化算法无法有效地处理多目标优化问题，其特点是收敛性和多样性相对较差，计算运行时间长。本文提出了一种新的基于竞争机制的多/多目标粒子群优化算法，通过普通个体和极值个体之间的最大和最小角度来保持种群多样性。并采用最近提出的 -支配进一步增强算法的性能。在标准基准问题 DTLZ、WFG 和 UF1-9 上对所提出的算法进行了评估，并与最近提出的四种多目标粒子群优化算法和四种最先进的多目标进化优化算法进行了比较。实验结果表明，所提出的算法具有更好的收敛性和多样性，在大多数测试实例上的性能优于其他比较算法。
