Multiobjective evolutionary algorithms: A survey of the state of the art
Multiobjective evolutionary algorithms: A survey of the state of the art
复制标题
多目标进化算法：现有技术的调查
DOI：
10.1016/j.swevo.2011.03.001
复制
发表时间：
2011-03-01
影响因子：
10
通讯作者：
Zhang, Qingfu
中科院分区：
文献类型：
--
作者：
Zhou, Aimin;Qu, Bo-Yang;Zhang, Qingfu
多目标优化问题涉及多个相互冲突的目标，并具有一组Pareto最优解。多目标进化算法（MOEAs）通过进化解的种群，能够在单次运行中逼近Pareto最优集。MOEA在过去的20年里吸引了大量的研究工作，并且仍然是进化计算领域中最热门的研究领域之一。本文主要回顾了过去八年来MOEA的发展情况。它涵盖了算法框架，如基于分解的MOEA（MOEA/Ds），模因MOEA，协同进化MOEA，选择和后代繁殖算子，MOEA与特定的搜索方法，MOEA多模态问题，约束处理和MOEA，计算昂贵的多目标优化问题（MOPs），动态MOPs，噪声MOPs，组合和离散MOPs，基准问题，性能指标和应用程序。最后，对今后的研究方向进行了展望。(C)2011爱思唯尔有限公司版权所有。
A multiobjective optimization problem involves several conflicting objectives and has a set of Pareto optimal solutions. By evolving a population of solutions, multiobjective evolutionary algorithms (MOEAs) are able to approximate the Pareto optimal set in a single run. MOEAs have attracted a lot of research effort during the last 20 years, and they are still one of the hottest research areas in the field of evolutionary computation. This paper surveys the development of MOEAs primarily during the last eight years. It covers algorithmic frameworks such as decomposition-based MOEAs (MOEA/Ds), memetic MOEAs, coevolutionary MOEAs, selection and offspring reproduction operators, MOEAs with specific search methods, MOEAs for multimodal problems, constraint handling and MOEAs, computationally expensive multiobjective optimization problems (MOPs), dynamic MOPs, noisy MOPs, combinatorial and discrete MOPs, benchmark problems, performance indicators, and applications. In addition, some future research issues are also presented. (C) 2011 Elsevier B.V. All rights reserved.