DE-FPA: A hybrid differential evolution-flower pollination algorithm for function minimization

In this paper, a new hybrid population based algorithm (DE-FPA) is proposed with the combination of differential evolution optimization algorithm and flower pollination algorithm. The main idea is to integrate the natural evolution characteristics of the population in differential evolution algorithm with the pollination behavior of flowering plant in flower pollination algorithm to synthesize the strength and power of both the algorithms. The hybrid algorithm is robust in the sense that the globalization takes place in evolution. Some benchmark test functions are utilized here to compare the hybrid algorithm with the individual DE and FPA algorithms in searching the best solution. The results show the hybrid algorithm possesses a better capability in searching for the sufficiently good solution and to escape from local optima. In addition to that, a novel concept of dynamic adaptive weight is introduced for faster convergence than the individual algorithms, thereby making the hybrid one competent.