Elite opposition-based flower pollination algorithm

Abstract

Flower pollination algorithm (FPA) is a novel metaheuristic optimization algorithm with quick convergence, but its population diversity and convergence precision can be limited in some applications. In order to enhance its exploitation and exploration abilities, in this paper, an elite opposition-based flower pollination algorithm (EOFPA) has been applied to functions optimization and structure engineering design problems. The improvement involves two major optimization strategies. Global elite opposition-based learning enhances the diversity of the population, and the local self-adaptive greedy strategy enhances its exploitation ability. An elite opposition-based flower pollination algorithm is validated by 18 benchmark functions and two structure engineering design problems. The results show that the proposed algorithm is able to obtained accurate solution, and it also has a fast convergence speed and a high degree of stability.