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

Cesar J. Montiel Moctezuma Jaime Mora Miguel González Mendoza

*Corresponding author: Cesar J. Montiel Moctezuma, Jaime Mora cmontielmoctezuma@gmail.com;jmora@tec.mx

MBE2020,2,975doi:10.3934/mbe.2020052

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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