期刊
MATHEMATICAL AND COMPUTER MODELLING
卷 38, 期 11-13, 页码 1275-1282出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/S0895-7177(03)90129-4
关键词
parallel genetic algorithm; parameter determination; traveling salesman problem; parallel computer; genetic operators
The parallel genetic algorithms (PGA) have been developed for combinatorial optimization problems, and its parallel efficiencies have been investigated on a specific problem. These investigations were. concerned with how to design a topology and the determination of the optimum setting for parameters (for example, size of subpopulations, migration interval, and so on) rather than the effectiveness of genetic operators. This paper investigates a relation between the parallel efficiency of the coarse-grained PGA and genetic (crossover and selection) operators for the traveling salesman problem on an MIMD parallel computer. The following genetic operators are considered: improved edge recombination (IERX), distance preserving (DPX), and complete subtour exchange (CSEX) crossovers, and two selection operators, which have relatively high selection pressures. Computational results indicate that the parallel efficiency is significantly affected by the difference of crossovers rather than the selections, and the PGA with CSEX gives better properties. (C) 2003 Elsevier Ltd. All rights reserved.
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