Centroid opposition with a two-point full crossover for the partially attracted firefly algorithm

The firefly algorithm (FA) is a powerful optimization tool. However, the existing FA and its variants seldom take advantage of intermediate data generated during algorithm iteration. In this paper, the centroid opposition-based learning with a two-point full crossover (CCOBL) is proposed to make full use of the favor information of the candidate solutions. It adopts a centroid opposition computing for considering the search information of population and a two-point full crossover for using the favor information in the candidate solution and its opposite. Then, the CCOBL is incorporated into the partially attracted firefly algorithm. The proposed algorithm is tested on the CEC' 2013 benchmark suite and a real-world optimization problem and is compared with some state-of-the-art FA algorithms and other up-to-date opposition-based evolutionary algorithms. The experimental results demonstrate the effectiveness of the CCOBL and the better performance of the proposed algorithm.