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February 2, 2026SymmetryOpen Access

Robust Multi-Objective Optimization of Ore-Drawing Process Using the OGOOSE Algorithm Under an ε-Constraint Framework

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Authors

CCChuanchuan CaiJCJ.-S. ChenCRChunfang Ren

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Overview

This analysis demonstrates improved optimization of cost and risk in sublevel caving, suggesting a robust framework for mining operations.

Key Points

  • To optimize the ore-drawing process by addressing cost, risk, recovery, and dilution under uncertainty using the OGOOSE algorithm.
  • Developed the OGOOSE algorithm with three mechanisms: Opposition-Based Learning, Adaptive Inertia Weight, Boundary Reflection Mechanism.
  • Utilized an ellipsoid-plane geometric surrogate for risk modeling.
  • Applied the ε-constraint method to decompose the four-objective optimization problem.
  • Achieved the lowest Friedman rank on CEC2017 benchmarks compared to GOOSE, WOA, and HHO.
  • Demonstrated a 28.95% average dilution rate, the lowest among comparators, without increasing costs.
  • Identified an optimal 'knee-point' for managing risk control and dilution limits through sensitivity analysis.

Cite This Study

Cai et al. (2026) studied this question.

synapsesocial.com/papers/6980ff08c1c9540dea811a7bhttps://doi.org/10.3390/sym18020254
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