Abstract
This paper describes a parallel genetic algorithm developed for the solution of the set partitioning problem—a difficult combinatorial optimization problem used by many airlines as a mathematical model for flight crew scheduling. Tests on forty real-world set partitioning problems were carried out on an IBM SP parallel computer. We found that performance, as measured by the quality of the solution found and the iteration on which it was found, improved as additional subpopulations were added to the computation. With larger numbers of subpopulations the genetic algorithm was regularly able to find the optimal solution to problems having up to a few thousand integer variables. A notable limitation was the difficulty solving problems with many constraints.
This work was supported by the Mathematical, Information, and Computational Science Division subprogram of the Office of Computational and Technology Research, U.S. Department of Energy, under Contract W-31-109-Eng-38
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© 1996 Kluwer Academic Publishers
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Levine, D. (1996). A Parallel Genetic Algorithm for the Set Partitioning Problem. In: Osman, I.H., Kelly, J.P. (eds) Meta-Heuristics. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1361-8_2
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DOI: https://doi.org/10.1007/978-1-4613-1361-8_2
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