Multi-objective evolutionary algorithm with evolutionary-status-driven environmental selection

Lin，Kangnian1; Li，Genghui3; Li，Qingyan1; Wang，Zhenkun1,2; Ishibuchi，Hisao2; Zhang，Hu4

2024-05-01

10.1016/j.ins.2024.120551

Information Sciences   影响因子和分区

0020-0255

The hardly dominated boundary (HDB) is a common feature of multi-objective optimization problems (MOPs). Previous studies have proposed several multi-objective evolutionary algorithms (MOEAs) to deal with the problem characterized by HDBs. Nevertheless, these methods lack consideration of the evolutionary status, potentially resulting in low efficiency when addressing problems that entail a confluence of HDB and other intricate characteristics. This paper proposes an MOEA with evolutionary-status-driven environmental selection (MOEA-ESD) to address such an issue. Specifically, in each generation, the current and historical information are used to evaluate the evolutionary status. Based on the estimated evolutionary status, the proposed MOEA-ESD algorithm adaptively uses three types of environmental selection: the traditional environmental selection (TES) based on Pareto-dominance and crowding distance, a convergence-first environmental selection (CFES) based on Gaussian mixture model clustering, and a diversity-first environmental selection (DFES) based on outlier detection and extreme solutions. In this way, inferior solutions situated on the HDB can be effectively eliminated, thereby facilitating the convergence of the population while concurrently preserving a commendable degree of diversity. Moreover, a set of new benchmark problems with different objective magnitudes and complicated Pareto sets is developed to enrich the features of HDB-MOPs and to verify the algorithm's performance. Our experimental results on 22 HDB-MOPs show the promising performance of the proposed algorithm. The source code of MOEA-ESD is available at https://github.com/CIAM-Group/EvolutionaryAlgorithm_Codes/tree/main/MOEA-ESD.

Convergence-first environmental selection Diversity-first environmental selection Gaussian mixture model clustering Hardly dominated boundaries (HDBs) Population's evolutionary status

SCI ; EI

英语

第一 ; 通讯

COMPUTER SCIENCE

2-s2.0-85190285319

Scopus

1.School of System Design and Intelligent Manufacturing,Southern University of Science and Technology,Shenzhen,518055,China
2.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
3.College of Computer Science and Software Engineering,Shenzhen University,Shenzhen,518060,China
4.CASIC Research Institute of Intelligent Decision Engineering,Wuhan,430000,China

Lin，Kangnian,Li，Genghui,Li，Qingyan,et al. Multi-objective evolutionary algorithm with evolutionary-status-driven environmental selection[J]. Information Sciences,2024,669.

Lin，Kangnian,Li，Genghui,Li，Qingyan,Wang，Zhenkun,Ishibuchi，Hisao,&Zhang，Hu.(2024).Multi-objective evolutionary algorithm with evolutionary-status-driven environmental selection.Information Sciences,669.

Lin，Kangnian,et al."Multi-objective evolutionary algorithm with evolutionary-status-driven environmental selection".Information Sciences 669(2024).