Complex-valued encoding metaheuristic optimization algorithm: A comprehensive survey

• Published in: Neurocomputing (Amsterdam) Vol. 407; pp. 313 - 342
• Main Authors: Wang, Pengchuan, Zhou, Yongquan, Luo, Qifang, Han, Cao, Niu, Yanbiao, Lei, Mengyi
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 24.09.2020
• Subjects: Combination optimization; Complex-valued encoding; Complex-valued encoding Metaheuristic algorithm; Engineering optimization design; Function optimization; Complex-valued encoding Metaheuristic algorithm; Combination optimization; Function optimization; Engineering optimization design; Complex-valued encoding
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2019.06.112

The number of publications related to complex-valued encoding metaheuristic optimization research is increasing the area of metaheuristic optimization is gaining in popularity. In this paper, we aim to provide researchers with a comprehensive and extensive overview of complex-valued encoding metaheuristic algorithms and applications for function optimization, engineering optimization design, and combination optimization. Compared with the basic metaheuristic algorithm, which are based on real-valued encoding or binary encoding, the complex-valued encoding metaheuristic algorithm expands the dimension of the search region and efficiently avoids the problem of falling into the local minimum. Finally, eight complex-valued encoding metaheuristic algorithms were used for 29 benchmark test functions and five engineering optimization design problems. Through the analysis and comparison of the results with statistical significance, the superiority of complex-value encoding was proved, and the complex-value encoding metaheuristic algorithm with the best performance was obtained. The purpose of this review is to present a relatively comprehensive list of all the complex-value encoding metaheuristic algorithms in the literature to inspire further research.