Title :
A selective migration parallel multi-objective genetic algorithm
Author :
Qiu, Tengfei ; Ju, Gang
Author_Institution :
Sch. of Energy & Environ., Southeast Univ., Nanjing, China
Abstract :
A new multi-objective evolutionary algorithm, called selective migration parallel genetic algorithm (SMPGA) was presented in this paper, which designs a new migration strategy and qualification based on the adaptive grid. In SMPGA, a searching population and a elite population evolve at the same time; unique migration strategy and qualification are used to keep and improve the convergence and diversity of the Pareto optimal set. Besides, according to their different purposes, the two populations adopt different crossover strength. Simulation results show that SMPGA can find accurate and uniform Pareto optimal solutions on different multi-objective problems.
Keywords :
Pareto optimisation; genetic algorithms; parallel algorithms; search problems; Pareto optimal set; adaptive grid; crossover strength; elite population; migration strategy; multiobjective evolutionary algorithm; searching population; selective migration parallel multiobjective genetic algorithm; Algorithm design and analysis; Convergence; Evolutionary computation; Genetic algorithms; Genetic engineering; Qualifications; Adaptive grid; Migration strategy and qualification; Multi-objective optimization; Parallel genetic algorithm;
Conference_Titel :
Control and Decision Conference (CCDC), 2010 Chinese
Conference_Location :
Xuzhou
Print_ISBN :
978-1-4244-5181-4
Electronic_ISBN :
978-1-4244-5182-1
DOI :
10.1109/CCDC.2010.5499013
