Enhancing evolutionary multifactorial optimization based on particle swarm optimization

Multifactorial evolutionary algorithm is used to deal with multifactorial optimization problem which simultaneously optimizes multiple tasks. In this paper, we introduce particle swarm optimization operation into the multifactorial evolutionary algorithm, and propose a hybrid algorithm for multifactorial optimization. The major aim is to utilize particle swarm optimization operation to accelerate the convergence and improve the accuracy of solutions. Experimental comparisons between the proposed hybrid algorithm and the original multi-factorial evolutionary algorithm show that the particle swarm update operators can effectively accelerate the convergence on some benchmark problems.