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A genetic algorithm (GA) is utilized as an optimization technique to optimize the hyperparameters of the LS-SVM model.
Evolution strategies (ESs) was utilized as an optimization tool.
In this study, a suspended floor slab-isolated structure is utilized as an optimization test system subjected to wind and seismic demands.
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In order to elaborate the proposed optimization methodology, the XW3-type cycloid speed reducer is chosen and utilized as an example for optimization.
Finally, the nonlinear Ant Colony Optimization technique was utilized as an intelligent tool to generate a seismic section of maturity index.
We derive the largest set of prices in which both the uniqueness of the NE and concurrent transmission are guaranteed, which is then utilized as a constraint in the optimization problem.
Genetic algorithms (GAs) have been classically utilized as a search method for optimization of objective functions that are discontinuous, non-differentiable, stochastic, or highly non-linear.
The developed models of different quality characteristics have been utilized as a quality function for single objective optimization by using particle swarm optimization (PSO) method.
The calculated sets of ESLs are utilized as a multiple loading condition in the optimization process.
The well-known benchmark instance, New York City Tunnel (NYCT) problem, is utilized as a case study to evaluate the optimization performance of IA and mIA.
Therein, the curve of manual optimization is utilized as a reference to compare achieved gains.
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