粒子群优化
多群优化
数学优化
元优化
稳健性(进化)
计算机科学
帝国主义竞争算法
网格
水准点(测量)
早熟收敛
算法
元启发式
趋同(经济学)
无导数优化
数学
基因
几何学
经济
化学
大地测量学
生物化学
地理
经济增长
作者
Rui Leng,Aijia Ouyang,Yanmin Liu,Yuan Lian,Zongyue Wu
标识
DOI:10.1142/s0218001420590089
摘要
In modern intelligent algorithms and real-industrial applications, there are many fields involving multi-objective particle swarm optimization algorithms, but the conflict between each objective in the optimization process will easily lead to the algorithm falling into local optimal. In order to prevent the algorithm from quickly falling into local optimization and improve the robustness of the algorithm, a multi-objective particle swarm optimization algorithm based on grid distance (GDMOPSO) was proposed, which has to improve the diversity of the algorithm and the search ability. Based on the MOPSO algorithm, a new external archive control strategy was established by using the grid technology and Pareto-dominant ordering principle, and the learning samples were improved. The proposed GDMOPSO is compared with a group of benchmark function tests and four classical algorithms. The results of experiment show that our proposed algorithm can effectively avoid premature convergence in terms of generational distance and hyper-volume (HV) indicator compared with other four classical MOPSO algorithms.
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