An enhanced opposition-based firefly algorithm for solving complex optimization problems

Abstract

Firefl y algorithm is one of the heuristic optimization algorithms which mainly based on the light intensity and the attractiveness of fi refl y. However, fi refl y algorithm has the problem of being trapped in local optimum and slow convergence rates due to its random searching process. This study introduces some methods to enhance the performance of original fi refl y algorithm. The proposed enhanced opposition fi refl y algorithm (EOFA) utilizes opposition-based learning in population initialization and generation jumping while the idea of inertia weight is incorporated in the updating of fi refl y’s position. Fifteen benchmark test functions have been employed to evaluate the performance of EOFA. Besides, comparison has been made with another existing optimization algorithm namely gravitational search algorithm (GSA). Results show that EOFA has the best performance comparatively in terms of convergence rate and the ability of escaping from local optimum point.