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The all-time behavior provides a robustness algorithm by allowing it to be interrupted at any point in the process, while ensuring that the best possible response is delivered when the stop request occurs. This work proposes a methodology to identify the relationship of the parameters of an optimization algorithm by colony of ants with its behavior at-all-moment, allowing to implement a technique of automatic improvement of the behavior at-all-moment using a reduction in logarithmic factors of the search space, decreasing the computational demand required for this task.

Aplicación del método de bisección para la mejora automática del comportamiento a-todo-momento del algoritmo ASO