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高维目标进化算法研究进展 被引量：1

Progress of research on many-objective evolutionary algorithms
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摘要 主流的多目标进化算法在解决目标数目较少的优化问题时具有较好的性能,但当优化目标数目超过4维,即具有高维目标时,算法的性能很快下降,而且搜索的开销快速增长.高维目标进化算法的研究受到了进化计算与工程优化领域的高度关注.鉴于此,对高维目标优化问题的困难进行了分析,并对高维目标进化算法的研究进展进行了综述,总结了各类算法的特点与缺陷,并指出了未来进一步研究的方向. Modern multi-objective evolutionary algorithms can solve two or three-objective optimization problems successfully;but their search abilities and performances will deteriorate badly when the number of objectives exceeds four.So,many-objective evolutionary algorithms are attracting more attention.The difficulties of many-objectives bringing to optimization problems are analyzed.And some many-objective evolutionary algorithms are surveyed systematically by categories;and their characteristics and limitations are also summarized.Finally,the proposed algorithms are evaluated and topics for future research are suggested.
作者 谢承旺 汪慎文 谢大同 郭肇禄
机构地区 华东交通大学软件学院 石家庄经济学院信息工程学院 武汉大学软件工程国家重点实验室
出处 《武汉大学学报（工学版）》 CAS CSCD 北大核心 2012年第5期636-642,共7页 Engineering Journal of Wuhan University
基金 国家自然科学基金项目(编号:61165004) 江西省自然科学基金(编号:20114BAB201025) 福建省自然科学基金(编号:2012J01248) 江西省教育厅科技项目(编号:GJJ12307)
关键词 PARETO支配 高维目标优化 进化算法 收敛性 Pareto dominance many-objective optimization evolutionary algorithm convergence
分类号 TP301 [自动化与计算机技术—计算机系统结构]
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参考文献22

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二级参考文献50

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武汉大学学报（工学版）
2012年 第5期

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