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.

翻译：元启发式搜索方法已被证明是解决复杂优化挑战的重要工具，但其全部潜力往往受限于传统算法框架。本文提出了一种称为深度启发式搜索（DHS）的新方法，该方法将元启发式搜索建模为一个记忆驱动的过程。DHS采用多层搜索结构和基于记忆的探索-利用机制，以导航大规模、动态的搜索空间。通过利用无模型的记忆表示，DHS增强了在无需依赖概率转移模型的情况下遍历时间轨迹的能力。所提出的方法在一系列启发式优化问题上展现出搜索效率和性能的显著提升。