摘要
首先针对常规多目标优化算法求解高维多目标优化时面临的选择压力衰减问题进行论述;然后针对该问题,按照选择机制的不同详细介绍基于Pareto支配、基于分解策略和基于性能评价指标的典型高维多目标优化算法,并分析各自的优缺点;接着立足于一种全新的性能评价指标—–R2指标,给出R2指标的具体定义,介绍基于R2指标的高维多目标优化算法,分析此类算法的本质,并按照R2指标的4个关键组成部分进行综述;最后,发掘其存在的潜在问题以及未来发展空间.
The paper first presents a comprehensive description of the selection pressure degradation problem when the traditional multi-objective evolutionary algorithms are adopted to solve many-objective optimization problems.According to the selection strategies, the Pareto-based, decomposition-based and indicator-based many-objective evolutionary algorithms（Ma OEAs） are proposed to address the selection pressure degradation problem. Then, the definition of R2 indicator proposed as a new performance indicator is provided, and a series of R2-Ma OEAs are infroduced. After analyzing the nature of R2-Ma OEAs, this paper gives a general review of these algorithms in consideration of their four main components. Finally, the inherent basis and the future research arevintroduced.
作者
刘建昌
李飞
王洪海
李田军
LIU Jian-chang1, LI Fei1, WANG Hong-hai1, LI Tian-jun2(1. College of Information Science and Engineering, Northeastern University, Shenyang 110004, China; 2. Department of Radar Engineering, PLA Artillery Air Defense Force Academy, Hefei 230031, Chin)
出处
《控制与决策》
EI
CSCD
北大核心
2018年第5期879-887,共9页
Control and Decision
基金
国家自然科学基金项目(61773106
61703086)
流程工业国家重点实验室基础科研业务项目(2013ZCX02-03)
中央高校基本科研业务费专项资金项目(N160403003)
