Current Trends and Challenges in Applying Metaheuristics to the Innovative Area of Weight and Structure Determination Neuronets

• Published in: Inventions (Basel) Vol. 10; no. 4; p. 62
• Main Authors: Mourtas, Spyridon D., Li, Shuai, Cao, Xinwei, Liao, Bolin, Katsikis, Vasilios N.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2025
• Subjects: Algorithms; Approximation; Artificial intelligence; Classification; Cognitive tasks; Forecasts and trends; Heuristic methods; Machine learning; Moore–Penrose inverse; Neural networks; Neurons; Polynomials; Real time; single-hidden-layer feedforward neuronet; Trends; weights and structure determination; United States; New Jersey; Germany
• ISSN: 2411-5134; 2411-5134
• DOI: 10.3390/inventions10040062

The weights and structure determination (WASD) neuronet (or neural network) is a single-hidden-layer feedforward neuronet that exhibits an excellent approximation ability, despite its simple structure. Thanks to its strong generalization, fast speed, and ease of implementation, the WASD neuronet has been the subject of many modifications, including metaheuristics, and applications in a wide range of scientific fields. As it has garnered significant attention in the last decade, the aim of this study is to provide an extensive overview of the WASD framework. Furthermore, the WASD has been effectively used in numerous real-time learning tasks like regression, multiclass classification, and binary classification due to its exceptional performance. In addition, we present WASD’s applications in social science, business, engineering, economics, and medicine. We aim to report these developments and provide some avenues for further research.