A Hybrid Firefly Algorithm Based on Orthogonal Opposition

Firefly Algorithm (FA) may suffer from lower convergence accuracy when solving high-dimensional and complex optimization problems. To solve this problem, a completely new strategy named Hybrid Firefly Algorithm Based on Orthogonal Opposition (OHFA) is proposed. In OHFA, we perform differential evolution (DE) on brighter fireflies (j) and orthogonal opposition-based learning (OOBL) on globally optimal firefly to improve the search ability of the population. Besides, in high-dimensional and large-scale search space, there is an obvious long Euclidean distance between fireflies, which reduces attraction in movement. Therefore, OHFA adopts a new movement to improve the application of the firefly algorithm in high-dimensional space. Computational results show the effectiveness of OOBL and DE. Our findings suggest that OHFA achieves better solutions than other proposed algorithms on most of the test functions.