Deep Memory Search: A Metaheuristic Approach for Optimizing Heuristic Search

• URL: http://arxiv.org/abs/2410.17042v1
• Date: Tue, 22 Oct 2024 14:16:49 GMT
• Title: Deep Memory Search: A Metaheuristic Approach for Optimizing Heuristic Search
• Authors: Abdel-Rahman Hedar, Alaa E. Abdel-Hakim, Wael Deabes, Youseef Alotaibi, Kheir Eddine Bouazza,
• Abstract summary: 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.
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• Abstract: 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.

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