Multiobjective evolutionary algorithm based on the Pareto Archive and individual migration

A multiobjective evolutionary algorithm based on the parallel evolution of multiple single objective populations and Pareto archive population is proposed. For each single objective population, single objective evolutionary algorithm is applied to optimize separately each of multiobjective functions, where individuals generated by tournament selection from the union of single objective and Pareto archive population form the single objective population of next generation. At each evolving iteration, based on the concept of Pareto dominance, a finite-sized Pareto archive population is iteratively updated and trimmed by a new crowded-comparison operation. Especially, individuals in Pareto archive population also join evolutionary operations to increase the converging speed and improve quality of nondominated solutions. Simulations manifest that the proposed method can realize the search from multiple directions to obtain the nondominated solutions scattered more uniformly over the Pareto frontier with better convergence metric compared to well-known NSGA-II algorithm. Individuals migrating from Pareto archive population by tournament selection is also proved to have the advantage in improving the converging speed and converging precision.