基于超球形模糊支配的高维多目标粒子群优化算法

doi:10.11772/j.issn.1001-9081.2019040710

计算机应用 ›› 2019, Vol. 39 ›› Issue (11): 3233-3241.DOI: 10.11772/j.issn.1001-9081.2019040710

基于超球形模糊支配的高维多目标粒子群优化算法

谭阳^1,2, 唐德权^1,3, 曹守富^1,2

1. 湖南师范大学数学与统计学院, 长沙 410081;
2. 湖南广播电视大学网络技术系, 长沙 410004;
3. 湖南警察学院信息技术系, 长沙 410138

收稿日期:2019-04-26 修回日期:2019-07-05 发布日期:2020-12-11 出版日期:2019-11-10
通讯作者: 谭阳
作者简介:谭阳(1979-),男,湖南望城人,副教授,硕士,主要研究方向:智能计算、信息安全;唐德权(1979-),男,湖南永州人,副教授,博士,主要研究方向:数据挖掘、人工智能;曹守富(1981-),男,内蒙古通辽人,讲师,硕士,主要研究方向:信息系统工程、多媒体信息处理。
基金资助:
国家自然科学基金资助项目（61471169）；湖南省自然科学基金资助项目（2018JJ2104）；湖南省教育厅科学研究基金资助项目（15C0928）。

Many-objective particle swarm optimization algorithm based on hyper-spherical fuzzy dominance

TAN Yang^1,2, TANG Dequan^1,3, CAO Shoufu^1,2

1. College of Mathematics and Statistics, Hunan Normal University, Changsha Hunan 410081, China;
2. Department of Network Technology, Hunan Radio and Television University, Changsha Hunan 410004, China;
3. Department of information technology, Hunan Police Academy, Changsha Hunan 410138, China

Received:2019-04-26 Revised:2019-07-05 Online:2020-12-11 Published:2019-11-10
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61471169), the Natural Science Foundation of Hunan Province (2018JJ2104), the Scientific Research Fund of Hunan Department of Education (15C0928).

摘要/Abstract

摘要： 高维多目标优化问题（MAOP）会随着待优化问题维度的增加形成巨大的目标空间，导致在目标空间中非支配解的比例急剧增加，削弱了进化算法的选择压力，降低了进化算法对MAOP的求解效率。针对这一问题，提出一种以超球型支配关系降低种群中非支配解数量的粒子群优化（PSO）算法。算法以模糊支配策略来维持种群对MAOP的选择压力，并通过全局极值的选择和外部档案的维护来保持种群个体在目标空间中的分布。在标准测试集DTLZ和WFG上的仿真结果表明，所提算法在求解MAOP时具备较优的收敛性和分布性。

关键词: 高维多目标优化问题, Pareto支配, 粒子群, 多样性

Abstract: With the increase of the dimension of the problem to be optimized, Many-objective Optimization Problem (MAOP) will form a huge target space, resulting in a sharp increase of the proportion of non-dominant solutions. And the selection pressure of evolutionary algorithms is weakened and the efficiency of evolutionary algorithms for solving MAOP is reduced. To solve this problem, a Particle Swarm Optimization (PSO) algorithm using hyper-spherical dominance relationship to reduce the number of non-dominant solutions was proposed. The fuzzy dominance strategy was used to maintain the selection pressure of the population to MAOP. And the distribution of individuals in the target space was maintained by the selection of global extremum and the maintenance of external files. The simulation results on standard test sets DTLZ and WFG show that the proposed algorithm has better convergence and distribution when solving MAOP.

Key words: Many-objective Optimization Problem (MAOP), Pareto dominance, particle swarm, diversity

中图分类号:

TP301.6

谭阳, 唐德权, 曹守富. 基于超球形模糊支配的高维多目标粒子群优化算法[J]. 计算机应用, 2019, 39(11): 3233-3241.

TAN Yang, TANG Dequan, CAO Shoufu. Many-objective particle swarm optimization algorithm based on hyper-spherical fuzzy dominance[J]. Journal of Computer Applications, 2019, 39(11): 3233-3241.

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基于超球形模糊支配的高维多目标粒子群优化算法

Many-objective particle swarm optimization algorithm based on hyper-spherical fuzzy dominance

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