Alexandru-Razvan Manescu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Alexandru-Razvan Manescu has a total of 1 co-authors. Here are the top co-authors by number of shared publications:
- Bogdan Dumitrescu
Faculty of Automatic Control and Computers, National University of Science and Technology Politehnica Bucharest, Bucharest, Romania
1 shared publication
Latest Publications
- Open Access
- 20 September 2023
- Published by MDPI AG in Algorithms
- Vol. 16 (9) , 451
HyperDE: An Adaptive Hyper-Heuristic for Global Optimization
In this paper, a novel global optimization approach in the form of an adaptive hyper-heuristic, namely HyperDE, is proposed. As the naming suggests, the method is based on the Differential Evolution (DE) heuristic, which is a well-established optimization approach inspired by the theory of evolution. Additionally, two other similar approaches are introduced for comparison and validation, HyperSSA and HyperBES, based on Sparrow Search Algorithm (SSA) and Bald Eagle Search (BES), respectively. The method consists of a genetic algorithm that is adopted as a high-level online learning mechanism, in order to adjust the hyper-parameters and facilitate the collaboration of a homogeneous set of low-level heuristics with the intent of maximizing the performance of the search for global optima. Comparison with the heuristics that the proposed methodologies are based on, along with other state-of-the-art methods, is favorable.
- 20 September 2023
- Published by MDPI AG in Algorithms
- Vol. 16 (9) , 451
Open AccessHyperDE: An Adaptive Hyper-Heuristic for Global Optimization
In this paper, a novel global optimization approach in the form of an adaptive hyper-heuristic, namely HyperDE, is proposed. As the naming suggests, the method is based on the Differential Evolution (DE) heuristic, which is a well-established optimization approach inspired by the theory of evolution. Additionally, two other similar approaches are introduced for comparison and validation, HyperSSA and HyperBES, based on Sparrow Search Algorithm (SSA) and Bald Eagle Search (BES), respectively. The method consists of a genetic algorithm that is adopted as a high-level online learning mechanism, in order to adjust the hyper-parameters and facilitate the collaboration of a homogeneous set of low-level heuristics with the intent of maximizing the performance of the search for global optima. Comparison with the heuristics that the proposed methodologies are based on, along with other state-of-the-art methods, is favorable.