A hybrid particle swarm optimization and genetic algorithm with population partitioning for large scale optimization problems

In this paper, a new hybrid particle swarm optimization and genetic algorithm is proposed to minimize a simplified model of the energy function of the molecule. The proposed algorithm is called Hybrid Particle Swarm Optimization and Genetic Algorithm (HPSOGA). The HPSOGA is based on three mechanisms. The first mechanism is applying the particle swarm optimization to balance between the exploration and the exploitation process in the proposed algorithm. The second mechanism is the dimensionality reduction process and the population partitioning process by dividing the population into sub-populations and applying the arithmetical crossover operator in each sub-population in order to increase the diversity of the search in the algorithm. The last mechanism is applied in order to avoid the premature convergence and avoid trapping in local minima by using the genetic mutation operator in the whole population. Before applying the proposed HPSOGA to minimize the potential energy function of the molecule size, we test it on 13 unconstrained large scale global optimization problems with size up to 1000 dimensions in order to investigate the general performance of the proposed algorithm for solving large scale global optimization problems then we test the proposed algorithm with different molecule sizes with up to 200 dimensions. The proposed algorithm is compared against the standard particle swarm optimization to solve large scale global optimization problems and 9 benchmark algorithms, in order to verify the efficiency of the proposed algorithm for solving molecules potential energy function. 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 faster than the other comparative algorithms.