A review of population-based metaheuristics for large-scale black-box global optimization: Part A

Mohammad Nabi Omidvar1; Xiaodong Li2; Xin Yao3

2021

10.1109/TEVC.2021.3130838

IEEE Transactions on Evolutionary Computation   Impact Factor & Quartile

1941-0026

1941-0026

Scalability of optimization algorithms is a major challenge in coping with the ever-growing size of optimization problems in a wide range of application areas from high-dimensional machine learning to complex large-scale engineering problems. The field of large-scale global optimization is concerned with improving the scalability of global optimization algorithms, particularly, population-based metaheuristics. Such metaheuristics have been successfully applied to continuous, discrete, or combinatorial problems ranging from several thousand dimensions to billions of decision variables. In this two-part survey, we review recent studies in the field of large-scale black-box global optimization to help researchers and practitioners gain a bird's-eye view of the field, learn about its major trends, and the state-of-the-art algorithms. Part I of the series covers two major algorithmic approaches to large-scale global optimization: 1) problem decomposition and 2) memetic algorithms. Part II of the series covers a range of other algorithmic approaches to large-scale global optimization, describes a wide range of problem areas, and finally, touches upon the pitfalls and challenges of current research and identifies several potential areas for future research.

large-scale global optimization black-box optimization metaheuristics evolutionary optimization.

SCI

English

Others

ARC (Australian Research Council)["DP180101170","DP190101271"] ; Shenzhen Science and Technology Program[KQTD2016112514355531] ; Program for Guangdong Introducing Innovative and Entrepreneurial Teams[2017ZT07X386] ; Program for University Key Laboratory of Guangdong Province[2017KSYS008]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods

WOS:000862385200005

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Web of Science

1.School of Computing, University of Leeds, and Leeds University Business School, Leeds LS2 9JT, UK, and also with the current chair of the IEEE Taskforce on Large-Scale Global Optimization. (e-mail: mn.omidvar@gmail.com)
2.School of Computing Technologies, RMIT University, Melbourne, Australia.
3.Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China, and also with the School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK.

Mohammad Nabi Omidvar,Xiaodong Li,Xin Yao. A review of population-based metaheuristics for large-scale black-box global optimization: Part A[J]. IEEE Transactions on Evolutionary Computation,2021,PP(99):1-1.

Mohammad Nabi Omidvar,Xiaodong Li,&Xin Yao.(2021).A review of population-based metaheuristics for large-scale black-box global optimization: Part A.IEEE Transactions on Evolutionary Computation,PP(99),1-1.

Mohammad Nabi Omidvar,et al."A review of population-based metaheuristics for large-scale black-box global optimization: Part A".IEEE Transactions on Evolutionary Computation PP.99(2021):1-1.