Search Direction Learning Accelerates Evolutionary Optimization in Dispatching Large-scale Coal Mine Integrated Energy Systems

The dispatch optimization of Coal Mine Integrated Energy Systems (CMIES) is critical for energy efficiency and decarbonization in the mining industry, yet it poses challenges due to large-scale decision variables, strong multiple constraints, and a narrow feasible region. Existing constrained multi-objective evolutionary algorithms (CMOEAs) often fail to rapidly locate feasible solutions and suffer from slow convergence, making them impractical for real-time CMIES dispatch. To overcome these limitations, this paper proposes a Search Direction Learningbased Evolutionary Optimization (SDLEO) algorithm that prioritizes feasibility and convergence acceleration. A dual-direction learning mechanism via principal component analysis is proposed to guide the population toward high-quality Pareto regions, while tributary directions derived from constraint violation rankings steer the search directly into the union of feasible regions. An adaptive variable importance strategy selectively activates decision variables based on their sensitivity to objectives and constraints, further enhancing search efficiency. Three realistic dispatch optimization models for CMIES under typical scenarios are developed to faithfully characterize the complex constraints and trade-offs. Experimental results on benchmark problems and real-world CMIES cases demonstrate that SDLEO significantly outperforms 11 state-of-the-art CMOEAs, providing efficient solutions for large-scale constrained dispatch in CMIES.