Metaheuristic search methods have proven to be essential tools for tackling complex optimization challenges, but their full potential is often constrained by conventional algorithmic frameworks. In this paper, we introduce a novel approach called Deep Heuristic Search (DHS), which models metaheuristic search as a memory-driven process. DHS employs multiple search layers and memory-based exploration-exploitation mechanisms to navigate large, dynamic search spaces. By utilizing model-free memory representations, DHS enhances the ability to traverse temporal trajectories without relying on probabilistic transition models. The proposed method demonstrates significant improvements in search efficiency and performance across a range of heuristic optimization problems.
@article{arxiv.2410.17042,
title = {Deep Memory Search: A Metaheuristic Approach for Optimizing Heuristic Search},
author = {Abdel-Rahman Hedar and Alaa E. Abdel-Hakim and Wael Deabes and Youseef Alotaibi and Kheir Eddine Bouazza},
journal= {arXiv preprint arXiv:2410.17042},
year = {2024}
}