A Granular Sieving Algorithm for Deterministic Global Optimization
This provides a new deterministic optimization method for researchers and practitioners working with Lipschitz continuous functions in various domains.
The authors developed a gradient-free deterministic method called granular sieving to solve global optimization problems for Lipschitz continuous functions, locating the global minimum value and all global minimizers through decreasing sequences of compact sets. The algorithm was tested on extensive benchmark functions and showed remarkable effectiveness and applicability.
A gradient-free deterministic method is developed to solve global optimization problems for Lipschitz continuous functions defined in arbitrary path-wise connected compact sets in Euclidean spaces. The method can be regarded as granular sieving with synchronous analysis in both the domain and range of the objective function. With straightforward mathematical formulation applicable to both univariate and multivariate objective functions, the global minimum value and all the global minimizers are located through two decreasing sequences of compact sets in, respectively, the domain and range spaces. The algorithm is easy to implement with moderate computational cost. The method is tested against extensive benchmark functions in the literature. The experimental results show remarkable effectiveness and applicability of the algorithm.