DocumentCode
2397370
Title
PSFGA: a parallel genetic algorithm for multiobjective optimization
Author
De Toro, Francisco ; Ortega, Julio ; Fernández, Javier ; Díaz, Antonio
Author_Institution
Univ. of Huelva, Spain
fYear
2002
fDate
2002
Firstpage
384
Lastpage
391
Abstract
This paper presents the parallel single front genetic algorithm (PSFGA), a parallel Pareto-based algorithm for multiobjective optimization problems based on an evolutionary procedure. In this procedure, a population of solutions is sorted with respect to the values of the objective functions and partitioned into subpopulations which are distributed among the processors. Each processor applies a sequential multiobjective genetic algorithm that we have devised (called single front genetic algorithm, SFGA) to its subpopulation. Experimental results are provided comparing PSFGA with previously proposed multiobjective evolutionary algorithms
Keywords
Pareto distribution; genetic algorithms; parallel algorithms; PSFGA; Pareto-based algorithm; SFGA; evolutionary procedure; multiobjective optimization; parallel single front genetic algorithm; sequential algorithm; Evolutionary computation; Genetic algorithms; Parallel processing; Pareto optimization;
fLanguage
English
Publisher
ieee
Conference_Titel
Parallel, Distributed and Network-based Processing, 2002. Proceedings. 10th Euromicro Workshop on
Conference_Location
Canary Islands
Print_ISBN
0-7695-1444-8
Type
conf
DOI
10.1109/EMPDP.2002.994315
Filename
994315
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