A Weight Vector Adjustment Method for Decomposition-Based Multi-Objective Evolutionary Algorithms

被引：10

作者：

Cheng, Haibing ^{[1
,2
]}

Li, Lin ^{[2
]}

You, Ling ^{[2
]}

机构：

[1] PLA Strateg Support Force Informat Engn Univ, Zhengzhou 450001, Peoples R China

[2] Natl Key Lab Sci & Technol Blind Signal Proc, Chengdu 610041, Peoples R China

来源：

IEEE ACCESS | 2023年 / 11卷

关键词：

MOEA/D; discontinuous PF; search direction; weight vector; adjustment; OPTIMIZATION ALGORITHM; MOEA/D;

D O I：

10.1109/ACCESS.2023.3270806

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Multi-objective evolutionary algorithm based on decomposition (MOEA/D) is effective to solve most multi-objective optimization problems (MOPs) in the past 20 years. However, the algorithm MOEA/D with constant weight vectors has bad performance in solving several MOPs with discontinuous Pareto front (PF). This paper analyses the limitations of the constant weight vectors in MOEA/D and explains the necessity of adjusting the weight vectors in the processing. This paper proposes a weight vector adjustment method for MOEA/D (MOEA/D-WVA). It deletes the weight vectors which have bad search direction, and adds some new weight vectors in the processing. Experimental studies are conducted on several MOPs with discontinuous PF to compare the MOEA/D-WVA with other state-of-the-art multi-objective optimization algorithms in solving those MOPs with complex PF. The results show MOEA/D-WVA performs better than other algorithms on those MOPs with discontinuous PF.

引用

页码：42324 / 42330

页数：7

共 24 条

[1] An Enhanced Decomposition-Based Evolutionary Algorithm With Adaptive Reference Vectors [J].

Asafuddoula, Md ;

Singh, Hemant Kumar ;

Ray, Tapabrata .

IEEE TRANSACTIONS ON CYBERNETICS, 2018, 48 (08) :2321-2334

[2] The balance between proximity and diversity in multiobjective evolutionary algorithms [J].

Bosman, PAN ;

Thierens, D .

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2003, 7 (02) :174-188

[3] A Decomposition-Based Many-Objective Evolutionary Algorithm With Two Types of Adjustments for Direction Vectors [J].

Cai, Xinye ;

Mei, Zhiwei ;

Fan, Zhun .

IEEE TRANSACTIONS ON CYBERNETICS, 2018, 48 (08) :2335-2348

[4] A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization [J].

Cheng, Ran ;

Jin, Yaochu ;

Olhofer, Markus ;

Sendhoff, Bernhard .

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2016, 20 (05) :773-791

[5]

Deb K, 2002, IEEE C EVOL COMPUTAT, P825, DOI 10.1109/CEC.2002.1007032

[6] A fast and elitist multiobjective genetic algorithm: NSGA-II [J].

Deb, K ;

Pratap, A ;

Agarwal, S ;

Meyarivan, T .

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2002, 6 (02) :182-197

[7]

Deb K., 2001, PROC INT C EVOL MULT, P7

[8] A new multi-objective particle swarm optimizer using empirical movement and diversified search strategies [J].

Fan, Shu-Kai S. ;

Chang, Ju-Ming ;

Chuang, Yu-Chiang .

ENGINEERING OPTIMIZATION, 2015, 47 (06) :750-770

[9] A parallel particle swarm optimization algorithm for multi-objective optimization problems [J].

Fan, Shu-Kai S. ;

Chang, Ju-Ming .

ENGINEERING OPTIMIZATION, 2009, 41 (07) :673-697

[10] An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach [J].

Jain, Himanshu ;

Deb, Kalyanmoy .

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2014, 18 (04) :602-622

← 1 2 3 →