Metropolis–Hastings (MH): An Innovative Approach to Population Initialization
DOI:
https://doi.org/10.32870/recibe.v13i1.335Keywords:
Métodos de inicialización, Algoritmos metaheurísticos, OptimizaciónAbstract
In this article, a novel population initialization method for metaheuristic algorithms is proposed. In this approach, the initial set of candidate solutions is generated by sampling the objective function using the Metropolis–Hastings (MH) technique. Under this method, the initial solutions tend to be located near the most significant values of the objective function being optimized. Unlike most initialization methods, which only consider the spatial distribution of solutions, the proposed method generates initial points that represent promising regions of the search space, deserving further exploration to identify the global optimal solution more efficiently. This approach provides the algorithm with faster convergence and improves the quality of the solutions obtained. To demonstrate the effectiveness of the proposed initialization method in metaheuristic algorithms, it was embedded into the classical Differential Evolution (DE) algorithm. The resulting system was evaluated using a representative set of benchmark functions extracted from different datasets. The experimental results show that the proposed approach achieves faster convergence rates and higher-quality solutions when compared with other similar initialization methods.References
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