Constrained Mixed Integer Programming Solver Based on the Compact Genetic Algorithm

This paper presents a solver, the PIMAGc, for optimization of constrained mixed integer programming problems based on the Compact Genetic Algorithm (cGA). As the cGA, the PIMAGc works with binary representation of variables. As comparison criteria and to measure the algorithm's performance, the...

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Bibliographic Details
Published in:Revista IEEE América Latina 2018-05, Vol.16 (5), p.1493-1498
Main Authors: Palhares, P. H. S., Brito, L. C.
Format: Article
Language:English
Subjects:
Compact Genetic Algorithm
Computational modeling
constrained optimization
Genetic algorithms
IEEE transactions
Integer programming
Linear programming
Mixed integer
Mixed integer programming
Optimization
Random access memory
Robustness
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Summary:This paper presents a solver, the PIMAGc, for optimization of constrained mixed integer programming problems based on the Compact Genetic Algorithm (cGA). As the cGA, the PIMAGc works with binary representation of variables. As comparison criteria and to measure the algorithm's performance, the PIMAGc was compared with the MI-LXPM, an appropriate algorithm for solving mixed integer programming problems. By using an appropriate number of problems, it was found that the PIMAGc outperformed the MI-LXPM with a greater success rate and a smaller number of evaluations on the majority of the problems, with equal performance on the others.
ISSN:1548-0992
1548-0992
DOI:10.1109/TLA.2018.8408446