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A self-adaptive mechanism using weibull probability distribution to improve metaheuristic algorithms to solve combinatorial optimization problems in dynamic environments

1 Instituto Mexicano del Transporte, Querétaro
2 Tecnologico de Monterrey

Special Issues: Bio-inspired algorithms and Bio-systems

In last decades, the interest to solve dynamic combinatorial optimization problems has increased. Metaheuristics have been used to find good solutions in a reasonably low time, and the use of self-adaptive strategies has increased considerably due to these kind of mechanism proved to be a good alternative to improve performance in these algorithms. On this research, the performance of a genetic algorithm is improved through a self-adaptive mechanism to solve dynamic combinatorial problems: 3-SAT, One-Max and TSP, using the genotype-phenotype mapping strategy and probabilistic distributions to define parameters in the algorithm. The mechanism demonstrates the capability to adapt algorithms in dynamic environments.
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Keywords genetic algorithm; self-adaptive mechanism; dynamic combinatorial optimization problems

Citation: Cesar J. Montiel Moctezuma, Jaime Mora, Miguel González Mendoza. A self-adaptive mechanism using weibull probability distribution to improve metaheuristic algorithms to solve combinatorial optimization problems in dynamic environments. Mathematical Biosciences and Engineering, 2020, 17(2): 975-997. doi: 10.3934/mbe.2020052

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Copyright Info: © 2020, Cesar J. Montiel Moctezuma, Jaime Mora, licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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