Multi-objective decomposition evolutionary algorithm with objective modification-based dominance and external archive

Wang，Zhenkun1,2; Li，Qingyan1; Li，Genghui1; Zhang，Qingfu3,4

2023-12-01

10.1016/j.asoc.2023.111006

Applied Soft Computing   影响因子和分区

1568-4946

In practice, the multi-objective optimization problem (MOP) is typically challenging in two aspects. On the one hand, its Pareto front has imbalanced search difficulties; on the other hand, its search space contains many dominance resistant solutions (DRSs). Decomposing a complicated MOP into several simple MOPs for collaborative optimization (M2M) has been acknowledged to be efficient in coping with the imbalanced search difficulty. Nevertheless, the convergence efficiency of the M2M-based multi-objective evolutionary algorithm (MOEA) is rarely investigated, especially on the MOP with DRSs. This paper reveals two convergence challenges faced by M2M-based MOEAs. Subsequently, a variant called MOEA/D-OMDEA is proposed to achieve better convergence efficiency without sacrificing advantages in diversity preservation. MOEA/D-OMDEA integrates a new relaxed dominance criterion, namely the OM-dominance criterion, into its environmental selection to alleviate the negative influence of inferior solutions (e.g., DRSs) as well as to better balance convergence and diversity. MOEA/D-OMDEA is compared with ten state-of-the-art MOEAs on two sets of MOP benchmarks with different characteristics and a real-world problem. Experimental results indicate that MOEA/D-OMDEA can significantly outperform the other ten competitors on these problems. In addition, this paper provides a thorough analysis of the effectiveness of each new algorithmic component and the sensitivity of each newly-introduced parameter.

External archive MOP with different search difficulties Multi-objective decomposition evolutionary algorithm Objective modification-based dominance

SCI ; EI

英语

第一 ; 通讯

WOS:001111746400001

20234615059592

Decomposition ; Efficiency ; Multiobjective optimization

Chemical Reactions:802.2 ; Production Engineering:913.1 ; Optimization Techniques:921.5

COMPUTER SCIENCE

2-s2.0-85176499141

Scopus

1.School of System Design and Intelligent Manufacturing,Southern University of Science and Technology,Shenzhen,China
2.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,China
3.Department of Computer Science,City University of Hong Kong,Hong Kong
4.City University of Hong Kong Shenzhen Research Institute,China

Wang，Zhenkun,Li，Qingyan,Li，Genghui,et al. Multi-objective decomposition evolutionary algorithm with objective modification-based dominance and external archive[J]. Applied Soft Computing,2023,149.

Wang，Zhenkun,Li，Qingyan,Li，Genghui,&Zhang，Qingfu.(2023).Multi-objective decomposition evolutionary algorithm with objective modification-based dominance and external archive.Applied Soft Computing,149.

Wang，Zhenkun,et al."Multi-objective decomposition evolutionary algorithm with objective modification-based dominance and external archive".Applied Soft Computing 149(2023).