基于目标空间分区的稳态高维多目标进化算法

doi:10.12068/j.issn.1005-3026.2018.03.001

东北大学学报:自然科学版 ›› 2018, Vol. 39 ›› Issue (3): 305-310.DOI: 10.12068/j.issn.1005-3026.2018.03.001

• 信息与控制 • 下一篇

基于目标空间分区的稳态高维多目标进化算法

李飞¹，刘建昌¹，朱佳妮¹，李晨曦²

(1. 东北大学信息科学与工程学院，辽宁沈阳110819; 2. 南京航空航天大学自动化学院，江苏南京211106)

收稿日期:2016-10-12 修回日期:2016-10-12 出版日期:2018-03-15 发布日期:2018-03-09
通讯作者: 李飞
作者简介:冯明杰(1971-)，男，河南禹州人，东北大学副教授; 王恩刚(1962-)，男，辽宁沈阳人，东北大学教授，博士生导师.李飞(1988-)，男，安徽太和人，东北大学博士研究生; 刘建昌(1960-)，男，辽宁黑山人，东北大学教授，博士生导师.
基金资助:
国家自然科学基金资助项目(51171041).国家自然科学基金资助项目(61773106，61374137); 流程工业综合自动化国家重点实验室基础科研业务项目(2013ZCX02-03).

Steady-State Many-Objectives Evolutionary Algorithm Based on Objective Space Partition

LI Fei¹， LIU Jian-chang¹， ZHU Jia-ni¹， LI Chen-xi²

1.School of Information Science & Engineering， Northeastern University， Shenyang 110819， China; 2.College of Automation Engineering， Nanjing University of Aeronautics & Astronautics， Nanjing 211106， China.

Received:2016-10-12 Revised:2016-10-12 Online:2018-03-15 Published:2018-03-09
Contact: LI Fei
About author:-
Supported by:
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摘要/Abstract

摘要： 针对高维多目标优化中Pareto非劣候选解所占比例很大，常用的先考虑收敛性再考虑分布性的多目标进化算法面临选择压力衰减的问题，提出一种先考虑分布性再考虑收敛性的高维多目标进化算法——基于目标空间分区的稳态高维多目标进化算法(SS-OSP).该算法先采用目标空间分区策略将种群按照权重向量分为多个子空间，在每个子空间中按照分解方法中的聚合函数选择个体;然后，考虑到常规的PBI聚合函数的罚参数在进化过程中一直保持不变的情况，提出一种自适应PBI聚合函数;最后，仿真实验结果表明所提出的算法与其他三种算法相比，具有更好的收敛性和分布性.

关键词: 高维多目标优化问题, 稳态进化算法, 目标空间分区, 自适应PBI聚合函数, 分解策略

Abstract: Due to the sharp increasing of the proportion of Pareto non-dominated candidate solutions for many-objective optimization problems， the commonly used many-objective evolutionary algorithms encounter the selection pressure deterioration problem considering the convergence-first-and-diversity-second selection approach. This paper proposed a many-objective evolutionary algorithm with the diversity-first-and-convergence-second selection strategy-steady-state many-objective evolutionary algorithm based on objective space partition (SS-OSP). Firstly， it divided objective space into a large number of subspaces using a set of weight vectors. Then， one individual in each subspace was selected via adopting aggregation function. In addition， since the penalty parameter of PBI aggregation function remained constant in evolutionary process， an adaptive PBI aggregation function was proposed. Finally， the experimental results show that better convergence and diversity can be obtained using the proposed algorithm.

Key words: many-objective optimization problems, steady-state evolutionary algorithm, objective space partition, adaptive PBI aggregation function, decomposition strategy

中图分类号:

李飞，刘建昌，朱佳妮，李晨曦. 基于目标空间分区的稳态高维多目标进化算法[J]. 东北大学学报:自然科学版, 2018, 39(3): 305-310.

LI Fei， LIU Jian-chang， ZHU Jia-ni， LI Chen-xi. Steady-State Many-Objectives Evolutionary Algorithm Based on Objective Space Partition[J]. Journal of Northeastern University Natural Science, 2018, 39(3): 305-310.

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基于目标空间分区的稳态高维多目标进化算法

Steady-State Many-Objectives Evolutionary Algorithm Based on Objective Space Partition

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