Title :
Parallel strength Pareto multiobjective evolutionary algorithm
Author :
Xiong, Shengwu ; Li, Feng
Author_Institution :
Sch. of Comput. Sci. & Technol., Wuhan Univ. of Technol., China
Abstract :
A parallel strength Pareto multiobjective evolutionary algorithm (PSPMEA) is proposed. PSPMEA is a parallel computing model designed for solving Pareto-based multiobjective optimization problems by using an evolutionary procedure. In this procedure, both global parallelization and island parallel evolutionary algorithm models are used. Each subpopulation evolves separately with different crossover and mutation probability, but they exchange individuals in the elitist archive. The benchmark problems numerical experiment results demonstrate that the proposed method can rapidly converge to the Pareto optimal front and spread widely along the front.
Keywords :
Pareto optimisation; genetic algorithms; parallel algorithms; benchmark problem; evolutionary computation; global parallelization; island parallel evolutionary algorithm model; multiobjective optimization; parallel computing model; parallel genetic algorithm; parallel strength Pareto multi-objective evolutionary algorithm; Computer science; Constraint optimization; Design optimization; Evolutionary computation; Frequency; Genetic algorithms; Genetic engineering; Genetic mutations; Parallel processing; Pareto optimization;
Conference_Titel :
Parallel and Distributed Computing, Applications and Technologies, 2003. PDCAT'2003. Proceedings of the Fourth International Conference on
Print_ISBN :
0-7803-7840-7
DOI :
10.1109/PDCAT.2003.1236390
