Self-tuning multi-layer optimization algorithm (STML): An innovative parameter-less approach

• Published in: Applied soft computing 2024-11, Vol.165, p.112045, Article 112045
• Main Authors: Zolghadr-Asli, Babak, Latifi, Milad, Beig Zali, Ramiz, Nikoo, Mohammad Reza, Farmani, Raziyeh, Nazari, Rouzbeh, Gandomi, Amir H.
• Format: Article
• Language: English
• Subjects: Computational intelligence; Meta-heuristic algorithms; Optimization; Self-tuning
• ISSN: 1568-4946
• DOI: 10.1016/j.asoc.2024.112045

Computational intelligence (CI)-based methods offer a practical approach to overcoming the significant challenges posed by analytical and enumeration optimization methods when dealing with complex real-world problems. However, a notable drawback of these algorithms is the need for time-consuming and computationally demanding fine-tuning procedures to achieve optimal performance. This paper proposes a novel parameterless auto-tuning meta-heuristic architecture called the self-tuning multi-layer (STML). The fundamental concept behind this architecture involves a multi-layer structure where the inner layer optimizes the main problem. In contrast, the outer layer utilizes information obtained during the search to fine-tune the performance of the inner layer. This feature eliminates manual fine-tuning, as it can autonomously handle this task. A series of mathematical and benchmark problems were employed to demonstrate the computational prowess of the STML. The results indicate its superiority over other meta-heuristic algorithms. Additionally, the STML showcases robustness, as evidenced by the numerical proximity of results obtained from different independent runs on these benchmark problems. [Display omitted] •Self-tuning multi-layer (STML) autonomously carries out the optimization.•STML architecture eliminates manual parameter calibration.•STML removes the need for initial guesses on parameter impact for optimal results.•Preliminary results highlight STML’s robustness and computational efficiency.