用记忆驱动方法提升启发式搜索效率，解决复杂优化难题。
Deep Memory Search: A Metaheuristic Approach for Optimizing Heuristic Search
- 将启发式搜索建模为多层记忆驱动过程，实现动态空间导航。
- 在多种优化问题上显著提升搜索效率与性能表现。
- 无需概率转移模型，适合大规模动态搜索场景使用。
元启发式搜索方法在应对复杂优化挑战中已证明至关重要，但其潜力常受限于传统算法框架。本文提出一种新方法——深度启发式搜索（Deep Heuristic Search, DHS），将元启发式搜索建模为基于记忆的过程。DHS采用多层搜索结构和基于记忆的探索-利用机制，以在大而动态的搜索空间中高效导航。通过使用无模型的记忆表征，DHS能够在不依赖概率转移模型的前提下，有效追踪时间轨迹。所提方法在多种启发式优化问题上展现出显著的搜索效率与性能提升。
原文摘要 · Abstract (English)
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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