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

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. [ABSTRACT FROM AUTHOR]