An R2 Indicator and Reference Vector Based Many-objective Optimization Evolutionary Algorithm

被引：0
|
作者：
Chen G.-Y. [1 ]
Li J.-H. [1 ]
Li M. [1 ]
Chen H. [1 ]
机构：
[1] Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition, Nanchang Hangkong University, Nanchang
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2021年 / 47卷 / 11期
基金：
中国国家自然科学基金;
关键词：
Evolutionary algorithm; Many-objective optimization; R2; indicator; Reference vector;
D O I：
10.16383/j.aas.c180722
中图分类号：
学科分类号：
摘要：
Some researches point out that the most of existing multi-objective optimization algorithms (MOEAs) shown poor versatility on different shapes of Pareto front (PF) in many-objective optimization. To address this issue, this paper proposes an R2 indicator and reference vector based evolutionary algorithm for many-objective optimization (R2-RVEA). R2-RVEA adopts pareto dominance to select the non-dominated solutions to guide the evolution of population, it will further introduce population partition strategy and R2 indicator selection strategy to manage the diversity when the number of non-dominated solutions is greater than population size. The experimental results demonstrate that the proposed algorithm has good performance in handling different shapes of Pareto front. Copyright © 2021 Acta Automatica Sinica. All rights reserved.
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收藏
页码：2675 / 2690
页数：15
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