Orthogonal Learning Firefly Algorithm

Abstract

Abstract The primary aim of this original work is to provide a more in-depth insight into the relations between control parameters adjustments, learning techniques, inner swarm dynamics and possible hybridization strategies for popular swarm metaheuristic Firefly Algorithm (FA). In this paper, a proven method, orthogonal learning, is fused with FA, specifically with its hybrid modification Firefly Particle Swarm Optimization (FFPSO). The parameters of the proposed Orthogonal Learning Firefly Algorithm are also initially thoroughly explored and tuned. The performance of the developed algorithm is examined and compared with canonical FA and above-mentioned FFPSO. Comparisons have been conducted on well-known CEC 2017 benchmark functions, and the results have been evaluated for statistical significance using the Friedman rank test.