An improved firefly algorithm for global continuous optimization problems. (1st July 2020)

• Record Type: Journal Article
• Authors: Wu, Jinran; Wang, You-Gan; Burrage, Kevin; Tian, Yu-Chu; Lawson, Brodie; Ding, Zhe
• Is Part Of: Expert systems with applications. Volume 149(2020)
• Publication Date: 2020-07-01
• Subjects: Adaptive switch -- Logarithmic spiral -- Firefly algorithm -- Global continuous optimization; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.113340
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Logarithmic spiral path enhances search agents' exploitation. The exploration rate is made dynamic to improve the convergence. The new procedure is tested by minimizing the 29 well known functions. The proposed optimizer is validated using the 6 real cases. Abstract: Global continuous optimization is populated by its implementation in many real-world applications. Such optimization problems are often solved by nature-inspired and meta-heuristic algorithms, including the firefly algorithm (FA), which offers fast exploration and exploitation. To further strengthen FA's search for global optimum, a Levy-flight FA (LF-FA) has been developed through sampling from a Levy distribution instead of the traditional uniform one. However, due to its poor exploitation in local areas, the LF-FA does not guarantee fast convergence. To address this problem, this paper provides an adaptive logarithmic spiral-Levy FA (AD-IFA) that strengthens the LF-FA's local exploitation and accelerates its convergence. Our AD-IFA is integrated with logarithmic-spiral guidance to its fireflies' paths, and adaptive switching between exploration and exploitation modes during the search process. Experimental results show that the AD-IFA presented in this paper consistently outperforms the standard FA and LF-FA for 29 test functions and 6 real cases of global optimization problems in terms of both computation speed and derived optimum.