A clustering and vector angle-based adaptive evolutionary algorithm for multi-objective optimization with irregular Pareto fronts

被引:0
作者
He, Maowei [1 ]
Zheng, Hongxia [2 ]
Chen, Hanning [1 ,3 ]
Wang, Zhixue [4 ]
Liu, Xingguo [5 ,6 ]
Xia, Yelin [3 ]
Wang, Haoyue [3 ]
机构
[1] Tiangong Univ, Sch Comp Sci & Technol, Tianjin 300387, Peoples R China
[2] Tiangong Univ, Sch Software Engn, Tianjin 300387, Peoples R China
[3] Tianjin Univ Sci & Technol, Sch Artificial Intelligence, Tianjin 300457, Peoples R China
[4] Tiangong Univ, Sch Control Sci & Engn, Tianjin 300387, Peoples R China
[5] Open Univ China, Beijing 100039, Peoples R China
[6] Minist Educ, Engn Res Ctr Integrat & Applicat Digital Learning, Beijing 100039, Peoples R China
关键词
Multi-objective optimization; Irregular Pareto fronts; Hierarchical clustering; Vector angle-based selection; MANY-OBJECTIVE OPTIMIZATION; NONDOMINATED SORTING APPROACH; REFERENCE-POINT; MOEA/D;
D O I
10.1007/s11227-024-06496-w
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
In recent years, multi-objective optimization evolutionary algorithms (MOEAs) have been proven to be effective methods for solving multi-objective optimization problems (MOPs). However, most existing MOEAs that are limited by the shape of the Pareto fronts (PFs) are only suitable for solving a certain type of problem. Therefore, in order to ensure the generality of the algorithm in practical applications and overcome the constraints brought by the shapes of PFs, a new adaptive MOEA (CAVA-MOEA) based on hierarchical clustering and vector angle to solve various MOPs with irregular PFs is proposed in this article. Firstly, a set of adaptively generated clustering centers is used to guide the population to converge quickly in many search directions. Secondly, the vector angle-based selection further exploits the potential of the clustering algorithm, which keeps a good balance between diversity and convergence. The proposed CAVA-MOEA is tested and analyzed on 24 MOPs with regular PFs and 18 MOPs with irregular PFs. The results show that CAVA-MOEA has certain competitive advantages compared with the other six advanced algorithms in solving MOPs with irregular PFs.
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页数:46
相关论文
共 60 条
[41]   MOEA/D with Adaptive Weight Adjustment [J].
Qi, Yutao ;
Ma, Xiaoliang ;
Liu, Fang ;
Jiao, Licheng ;
Sun, Jianyong ;
Wu, Jianshe .
EVOLUTIONARY COMPUTATION, 2014, 22 (02) :231-264
[42]   A Cooperative Multistep Mutation Strategy for Multiobjective Optimization Problems With Deceptive Constraints [J].
Qiao, Kangjia ;
Yu, Kunjie ;
Yue, Caitong ;
Qu, Boyang ;
Liu, Mengnan ;
Liang, Jing .
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2024, 54 (11) :6670-6682
[43]  
Qiao Kangjia, 2024, IEEE J Biomed Health Inform, VPP, DOI 10.1109/JBHI.2024.3435418
[44]   Constraints Separation Based Evolutionary Multitasking for Constrained Multi-Objective Optimization Problems [J].
Qiao, Kangjia ;
Liang, Jing ;
Yu, Kunjie ;
Ban, Xuanxuan ;
Yue, Caitong ;
Qu, Boyang ;
Suganthan, Ponnuthurai Nagaratnam .
IEEE-CAA JOURNAL OF AUTOMATICA SINICA, 2024, 11 (08) :1819-1835
[45]   Benchmark problems for large-scale constrained multi-objective optimization with baseline results [J].
Qiao, Kangjia ;
Liang, Jing ;
Yu, Kunjie ;
Guo, Weifeng ;
Yue, Caitong ;
Qu, Boyang ;
Suganthan, P. N. .
SWARM AND EVOLUTIONARY COMPUTATION, 2024, 86
[46]   Evolutionary Constrained Multiobjective Optimization: Scalable High-Dimensional Constraint Benchmarks and Algorithm [J].
Qiao, Kangjia ;
Liang, Jing ;
Yu, Kunjie ;
Yue, Caitong ;
Lin, Hongyu ;
Zhang, Dezheng ;
Qu, Boyang .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2024, 28 (04) :965-979
[47]   A Multistage Evolutionary Algorithm for Better Diversity Preservation in Multiobjective Optimization [J].
Tian, Ye ;
He, Cheng ;
Cheng, Ran ;
Zhang, Xingyi .
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2021, 51 (09) :5880-5894
[48]   PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [J].
Tian, Ye ;
Cheng, Ran ;
Zhang, Xingyi ;
Jin, Yaochu .
IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE, 2017, 12 (04) :73-87
[49]   A faster algorithm for calculating hypervolume [J].
While, L ;
Hingston, P ;
Barone, L ;
Huband, S .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2006, 10 (01) :29-38
[50]   A Vector Angle-Based Evolutionary Algorithm for Unconstrained Many-Objective Optimization [J].
Xiang, Yi ;
Zhou, Yuren ;
Li, Miqing ;
Chen, Zefeng .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2017, 21 (01) :131-152