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ACOmi (Ant Colony Optization for mixed-integer problems) [ 30] and fSSm [ 31] are robust extensions of metaheuristics (Ant Colony optimization and Scatter Search, respectively) that enable the handling of mixed-integer variable search domains; therefore, they are ideal for solving the MINLP problem introduced in this work.
The Sequential Search mathematical method has been observed to be the only variable search method capable of selection of appropriate model parameters (molecular descriptors) regarding this large data set.
variable search window.
Besides, variable search was performed using the ANFIS network.
Variable search speed was also associated with increased accuracy, rather than a consistent pace, as predicted.
The multivariable search problem is reduced to a single variable search problem.
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Variable searching using the ANFIS network was performed to determine how the ANS branches affect the most relevant HRV parameters.
The classical approach is an exact method but because it involves a two-variable search technique takes a much longer computation time to achieve accurate results.
These dynamic-variable search rules are effective to improve the performance of the local search, and different from variable neighborhood search.
The most common metaheuristics are genetic algorithms, simulated annealing, variable neighborhood search, tabu search, and neural networks.
We also present some results for other meta-heuristic algorithms including the variable neighborhood search, the Tabu search, and the scatter search.
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