An Enhanced Opposition-based Firefly Algorithm for Solving Complex Optimization Problems

Firefly algorithm is one of the heuristic optimization algorithms which mainly based on the light intensity and the attractiveness of firefly. however, firefly 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 firefly algorithm. the proposed enhanced opposition firefly algorithm (eofa) utilizes opposition-based learning in population initialization and generation jumping while the idea of inertia weight is incorporated in the updating of firefly’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.