NSGA-II With Average Distance Clustering

被引：0
作者：
Cui Z.-H. [1 ]
Zhang M.-Q. [2 ]
Chang Y. [1 ]
Zhang J.-J. [1 ]
Wang H. [3 ]
Zhang W.-S. [4 ]
机构：
[1] Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan
[2] School of Electronics and Information, Tongji University, Shanghai
[3] Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang
[4] Institute of Automation, Chinese Academy of Sciences, Beijing
来源：
Cui, Zhi-Hua (zhihua.cui@hotmail.com) | 1600年 / Science Press卷 / 47期
基金：
中国国家自然科学基金;
关键词：
Crowding distance; Diversity; Multi-objective optimization algorithms; NSGA-II;
D O I：
10.16383/j.aas.c180540
中图分类号：
学科分类号：
摘要：
Crowding distance is an index for measuring the diversity of solutions. However, in many cases, it may fail to identify individuals with better diversity. The reason is that crowding distance mainly takes advantage of the local information of each position. To tackle this issue, based on the global position information of entire population, this paper designs average-distance-clustering diversity index, and further proposes NSGA-II with average distance clustering (ADCNSGA-II). ADCNSGA-II divides the entire population into several small populations using average distance, then the selection, crossover and mutation operators are performed in each small population. Simulation results show the proposed algorithm can maintain the diversify effectively. Copyright © 2021 Acta Automatica Sinica. All rights reserved.
引用
收藏
页码：1171 / 1182
页数：11
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