Considering radial basis function neural network for effective solution generation in metaheuristic algorithms.

ABSTRACT: In many engineering optimization problems, the number of function evaluations is severely limited by the time or cost constraints. These limitations present a significant challenge in the field of global optimization, because existing metaheuristic methods typically require a substantial number of function evaluations to find optimal solutions. This paper presents a new metaheuristic optimization algorithm that considers the information obtained by a radial basis function neural network (RBFNN) in terms of the objective function for guiding the search process. Initially, the algorithm uses the maximum design approach to strategically distribute a set of solutions across the entire search space. It then enters a cycle in which the RBFNN models the objective function values from the current

SUBMITTER: Cuevas E 

PROVIDER: S-EPMC11263605 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature