Yin-Yang firefly algorithm based on dimensionally Cauchy mutation. (15th July 2020)

• Record Type: Journal Article
• Authors: Wang, Wen-chuan; Xu, Lei; Chau, Kwok-wing; Xu, Dong-mei
• Is Part Of: Expert systems with applications. Volume 150(2020)
• Publication Date: 2020-07-15
• Subjects: Yin-Yang firefly algorithm -- Cauchy mutation -- GNS strategy -- Random attraction model -- CEC 2013 benchmark functions -- Engineering optimization problems; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.113216
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Propose a new Ying-Yang firefly algorithm (YYFA) based on dimensionally Cauchy mutation. Initialize the fireflies by good nodes set (GNS) strategy. A designed randomly attraction model is used to help convergence. Ying-Yang firefly self-learning strategy is employed to reduce the time complexity. The YYFA algorithm has a competitive performance on CEC 2013 and constrained problems. Abstract: Firefly algorithm (FA) is a classical and efficient swarm intelligence optimization method and has a natural capability to address multimodal optimization. However, it suffers from premature convergence and low stability in the solution quality. In this paper, a Yin-Yang firefly algorithm (YYFA) based on dimensionally Cauchy mutation is proposed for performance improvement of FA. An initial position of fireflies is specified by the good nodes set (GNS) strategy to ensure the spatial representativeness of the firefly population. A designed random attraction model is then used in the proposed work to reduce the time complexity of the algorithm. Besides, a key self-learning procedure on the brightest firefly is undertaken to strike a balance between exploration and exploitation. The performance of the proposed algorithm is verified by a set of CEC 2013 benchmark functions used for the single objective real parameter algorithm competition. Experimental results are compared with those of other the state-of-the-art variants of FA. Nonparametric statistical tests on the results demonstrate that YYFA provides highly competitive performance in terms of the tested algorithms. In addition, the application in constrained engineering optimization problems shows the practicability of YYFA algorithm.