Title :
A parallel genetic algorithm in multi-objective optimization
Author :
Wang Zhi-xin ; Ju, Gang
Author_Institution :
Sch. of Energy & Environ., Southeast Univ., Nanjing, China
Abstract :
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.
Keywords :
Pareto optimisation; genetic algorithms; parallel algorithms; set theory; NSGA-II algorithm; Pareto optimal set; multiobjective optimization; parallel genetic algorithm; parallel searching speed; Convergence; Diversity reception; Genetic algorithms; Genetic engineering; Parallel algorithms; Parallel processing; Pareto optimization; Performance evaluation; Scattering; Sorting; Individual migration; Individual update; Multi-objective optimization; NSGA-II; Parallel genetic algorithm;
Conference_Titel :
Control and Decision Conference, 2009. CCDC '09. Chinese
Conference_Location :
Guilin
Print_ISBN :
978-1-4244-2722-2
Electronic_ISBN :
978-1-4244-2723-9
DOI :
10.1109/CCDC.2009.5192490
