Hybrid Open Access
Green Open AccessA Heterogeneous Ant Colony Framework with Semantic Manifold Learning for Dynamic Ore-Flow Blending
Journal
Proceedings of the Genetic and Evolutionary Computation Conference
Date Issued
July 10, 2026
Author(s)
Tang, Hao
Chen, Guoyu
Han, Shoufei
Li, Miqing
The University of Birmingham
Abstract
Ore-flow blending in open pit mining is a complex real-time scheduling decision problem constrained by high dimensional spatiotemporal dynamics and stochastic uncertainties. Traditional methods often struggle to reconcile the need for long term global stability with the requirement for sub second response times to dynamic events. To address this, we propose a heterogeneous ant colony optimization framework with semantic manifold learning. The framework bridges the gap between static offline learning and dynamic online adaptation. In the offline phase, we introduce a semantic manifold learning mechanism combining genetic programming with quality-diversity optimization. By constructing a behaviorally diverse archive of elite heuristics, this method provides the online system with a "warm start" and diverse initial perspectives, significantly enhancing real-time responsiveness. In the online phase, a heterogeneous ant colony system is deployed where different ant sub populations are coupled with distinct elite rules from the offline archive. This architecture enables parallel exploration of the solution space from multiple semantic perspectives, granting the system intrinsic robustness and high adaptability. Extensive experiments on real-world mine demonstrate that this method significantly outperforms state-of-the-art techniques in ore flow stability control and exhibits superior robustness and scalability across varying road network topologies, equipment configurations, and dynamic scenarios.
Funding
National Natural Science Foundation of China
Hubei Provincial Natural Science Foundation of China
Fundamental Research Funds of the AUST
Hubei Provincial Natural Science Foundation of China
Fundamental Research Funds of the AUST
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403d6015e0cc174f254f3093049f0a5a
Scopus Subject Areas
Artificial Intelligence
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Computational Theory and Mathematics
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Computer Science Applications
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Control and Optimization
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Logic