A parallel genetic algorithm in multi-objective optimization
Zhixin Wang, Ju Gang · 2009
Based on the combination of NSGA-II algorithm and parallel genetic algorithm, this paper presents a parallel genetic algorithm for multi-objective optimization (PNSGA). At the evolving process of this new algorithm, an individual migration to improve the parallel searching speed is applied to improve the efficiency of this algorithm and the accuracy of Pareto optimal set; at the same time, an individual update strategy is introduced to keep the diversity of Pareto optimal set. Data show that the Pareto optimal solutions or the solution candidates output by PNSGA that are scattered extensively and uniformly.