Dynamic Temperature Control of Simulated Annealing using Hyper-Heuristics

This paper explores the potential for dynamically adapting the temperature of Simulated Annealing (SA) in a problem-independent manner, eliminating the need for extensive tuning or prior knowledge of instance-specific features. Our goals are to bypass expensive tuning procedures and to ensure a balanced interplay between exploration and exploitation at appropriate stages of the search process. To achieve this, we developed a framework called HHSA that employs Hyper-Heuristics (HHs) and makes use of fixed-temperature SA as their low-level heuristics. The proposed approach is evaluated across three state-of-the-art HHs and four problem domains (i.e., k-Graph Coloring, Permutation Flowshop, Traveling Salesperson, and Facility Location). Comparative results against a fine-tuned SA reveal that HHSA consistently achieves comparable or superior results in three out of the four studied problems. The findings reinforce the broader applicability of hyper-heuristics, demonstrating their potential to generalize across different problem domains without relying on instance-specific configurations.