A Hybrid Flower Pollination and Genetic Algorithm for Minimizing the Non-Convex Potential Energy of Molecular Structure

Abstract

Minimizing the molecular potential energy function is a real-life problem which can help to predict the threedimensional structure of the protein by knowing its steady state of the molecules. In this paper, we propose a new hybrid algorithm by combining the flower pollination algorithm and the genetic algorithm in order to minimize a simplified model of the energy function of the molecule. The proposed algorithm is called Hybrid Flower Pollination and Genetic Algorithm. We use the flower pollination algorithm to balance between the exploration and the exploitation process in the proposed algorithm. Also, we divide the population into subpopulations and applying the arithmetical crossover operator in each sub-population in order to increase the diversity of the search in the algorithm. Further, we apply the genetic mutation operator in the whole population in order to avoid the premature convergence and avoid trapping in local minima. Moreover, we compare the proposed algorithm against 3 benchmark algorithms, in order to investigate its efficiency. The numerical experiment results show that the proposed algorithm is a promising and efficient algorithm and can obtain the global minimum or near global minimum of the molecular energy function of the simplified model with up to 200 degrees of freedom faster than the other comparative algorithms.