Deep Memory Search: A Metaheuristic Approach for Optimizing Heuristic Search
This work addresses optimization challenges for researchers and practitioners in fields requiring heuristic search, but it appears incremental as it builds on existing metaheuristic frameworks.
The paper tackled the problem of optimizing heuristic search in complex optimization challenges by introducing Deep Heuristic Search (DHS), a memory-driven metaheuristic approach, and demonstrated significant improvements in search efficiency and performance across various problems.
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.