A new approach for dynamic parametrization of ant system algorithms
Автор: Tawfik Masrour, Mohamed Rhazzaf
Статья в выпуске: 6 vol.10, 2018 года.
This paper proposes a learning approach for dynamic parameterization of ant colony optimization algorithms. In fact, the specific optimal configuration for each optimization problem using these algorithms, whether at the level of preferences, the level of evaporation of the pheromone, or the number of ants, makes the dynamic approach an interested one. The new idea suggests the addition of a knowledge center shared by the colony members, combining the optimal evaluation of the configuration parameters proposed by the colony members during the experiments. This evaluation is based on qualitative criteria explained in detail in the article. Our approach indicates an evolution in the quality of the results over the course of the experiments and consequently the approval of the concept of machine learning.
Swarm Intelligence, Machine Learning, Ant Colony System, Pheromone, Combinatorial Optimization, Meta-heuristic, Traveling Salesman Problems
Короткий адрес: https://readera.ru/15016493
IDR: 15016493 | DOI: 10.5815/ijisa.2018.06.01
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