A Review of Population-Based Metaheuristics for Large-Scale Black-Box Global Optimization-Part I

• Published in: IEEE transactions on evolutionary computation Vol. 26; no. 5; pp. 802 - 822
• Main Authors: Omidvar, Mohammad Nabi, Li, Xiaodong, Yao, Xin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Black-box optimization; Buildings; Combinatorial analysis; Couplings; evolutionary optimization; Genetic algorithms; Global optimization; Heuristic methods; large-scale global optimization; Machine learning; Memetics; metaheuristics; Optimization; Optimization algorithms; Scalability; Search problems
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/TEVC.2021.3130838

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.