A Parallel Genetic Algorithm in Multi-objective Optimization
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.
Multi-objective optimization NSGA-II Parallel genetic algorithm Individual migration Individual update
WANG Zhi-xin JU Gang
School of Energy and Environment, Southeast University, Nanjing 210096, China
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
3497-3501
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）