A self-adaptive mechanism using weibull probability distribution to improve metaheuristic algorithms to solve combinatorial optimization problems in dynamic environments
| Autor: | Jaime Mora, Cesar J. Montiel Moctezuma, Miguel González-Mendoza |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Mathematical optimization
Computer science Self adaptive 02 engineering and technology 0502 economics and business Genetic algorithm 0202 electrical engineering electronic engineering information engineering genetic algorithm QA1-939 Metaheuristic Weibull distribution dynamic combinatorial optimization problems Mechanism (biology) Applied Mathematics 05 social sciences Combinatorial optimization problem Probabilistic logic General Medicine Computational Mathematics Modeling and Simulation Probability distribution 020201 artificial intelligence & image processing General Agricultural and Biological Sciences 050203 business & management TP248.13-248.65 Mathematics self-adaptive mechanism Biotechnology |
| Zdroj: | Mathematical Biosciences and Engineering, Vol 17, Iss 2, Pp 975-997 (2020) |
| ISSN: | 1551-0018 |
| DOI: | 10.3934/mbe.2020052?viewType=HTML |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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