Deep Memory Search: A Metaheuristic Approach for Optimizing Heuristic Search

TL;DR

This paper introduces Deep Heuristic Search (DHS), a memory-driven metaheuristic method that improves optimization efficiency by using layered memory mechanisms without probabilistic models.

Contribution

The paper presents DHS, a novel memory-based metaheuristic framework that enhances search performance in complex optimization problems.

Findings

Significant improvements in search efficiency.

Enhanced performance across various optimization problems.

Effective navigation of large, dynamic search spaces.

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.