南方科技大学知识苑(SUSTech KC): Constrained many-objective evolutionary algorithm based on adaptive infeasible ratio

题名	Constrained many-objective evolutionary algorithm based on adaptive infeasible ratio
作者	Liang，Zhengping 1; Chen，Canran 1; Wang，Xiyu 2; Liu，Ling 1; Zhu，Zexuan 1,3
通讯作者	Zhu，Zexuan
发表日期	2023-09-01
DOI	10.1007/s12293-023-00393-0
发表期刊	Memetic Computing 影响因子和分区
ISSN	1865-9284
EISSN	1865-9292
卷号	15 期号:3页码:281-300
摘要	Constrained many-objective optimization problems (CMaOPs) pose great challenges for evolutionary algorithms to reach an appropriate trade-off of solution feasibility, convergence, and diversity. To deal with this issue, this paper proposes a constrained many-objective evolutionary algorithm based on adaptive infeasible ratio (CMaOEA-AIR). In the evolution process, CMaOEA-AIR adaptively determines the ratio of infeasible solutions to survive into the next generation according to the number and the objective values of the infeasible solutions. The feasible solutions then undergo an exploitation-biased environmental selection based on indicator ranking and diversity maintaining, while the infeasible solutions undergo environmental selection based on adaptive selection criteria, aiming at the enhancement of exploration. In this way, both feasible and infeasible solutions are appropriately used to balance the exploration and exploitation of the search space. The proposed CMaOEA-AIR is compared with the other state-of-the-art constrained many-objective optimization algorithms on three types of CMaOPs of up to 15 objectives. The experimental results show that CMaOEA-AIR is competitive with the compared algorithms considering the overall performance in terms of solution feasibility, convergence, and diversity.
关键词	Adaptive infeasible ratio (AIR) Constraint handing technology (CHT) Environmental selection Many-objective evolutionary algorithm (MaOEA)
相关链接	[Scopus记录]
收录类别	SCI ; EI
语种	英语
学校署名	通讯
资助项目	Innovative Research Group Project of the National Natural Science Foundation of China[61871272];
WOS研究方向	Computer Science ; Operations Research & Management Science
WOS类目	Computer Science, Artificial Intelligence ; Operations Research & Management Science
WOS记录号	WOS:001044211000001
出版者	SPRINGER HEIDELBERG
EI入藏号	20233314524375
EI主题词	Constrained optimization ; Economic and social effects ; Environmental technology
EI分类号	Environmental Engineering:454 ; Systems Science:961 ; Social Sciences:971
Scopus记录号	2-s2.0-85167404924
来源库	Scopus
引用统计	被引频次[WOS]：2
成果类型	期刊论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/559656
专题	南方科技大学
作者单位	1.College of Computer Science and Software Engineering,Shenzhen University,Shenzhen,518060,China 2.Central R &D Institute,ZTE Corporation,Shenzhen,518057,China 3.Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation,Southern University of Science and Technology,Shenzhen,518055,China
通讯作者单位	南方科技大学
推荐引用方式 GB/T 7714	Liang，Zhengping,Chen，Canran,Wang，Xiyu,et al. Constrained many-objective evolutionary algorithm based on adaptive infeasible ratio[J]. Memetic Computing,2023,15(3):281-300.
APA	Liang，Zhengping,Chen，Canran,Wang，Xiyu,Liu，Ling,&Zhu，Zexuan.(2023).Constrained many-objective evolutionary algorithm based on adaptive infeasible ratio.Memetic Computing,15(3),281-300.
MLA	Liang，Zhengping,et al."Constrained many-objective evolutionary algorithm based on adaptive infeasible ratio".Memetic Computing 15.3(2023):281-300.