The dispatch optimization of coal mine integrated energy system is challenging due to high dimensionality, strong coupling constraints, and multiobjective. Existing constrained multiobjective evolutionary algorithms struggle with locating multiple small and irregular feasible regions, making them inaplicable to this problem. To address this issue, we here develop a multitask evolutionary algorithm framework that incorporates the dispatch correlated domain knowledge to effectively deal with strong constraints and multiobjective optimization. Possible evolutionary multitask construction strategy based on complex constraint relationship analysis and handling, i.e., constraint coupled spatial decomposition, constraint strength classification and constraint handling technique, is first explored. Within the multitask evolutionary optimization framework, two strategies, i.e., an elite guided knowledge transfer by designing a special crowding distance mechanism to select dominant individuals from each task, and an adaptive neighborhood technology based mutation to effectively balance the diversity and convergence of each optimized task for the differential evolution algorithm, are further developed. The performance of the proposed algorithm in feasibility, convergence, and diversity is demonstrated in a case study of a coal mine integrated energy system by comparing with CPLEX solver and seven constrained multiobjective evolutionary algorithms.

煤矿综合能源系统的调度优化是一个具有高维度、强耦合约束和多目标的挑战性问题。针对此问题，我们开发了一个多任务进化算法框架，利用调度相关领域知识来有效处理强约束和多目标优化。通过复杂约束关系分析和处理，我们首先探索了一种可能的进化多任务构建策略，即约束耦合空间分解、约束强度分类和约束处理技术。在多任务进化优化框架中，进一步开发了两种策略，即通过设计特殊的拥挤距离机制从每个任务中选择主导个体的精英引导知识传递以及基于自适应邻域技术的突变，以有效平衡每个优化任务的多样性和收敛性。通过与CPLEX求解器和七种约束多目标进化算法的比较，在一个煤矿综合能源系统的案例研究中展示了所提出算法在可行性、收敛性和多样性方面的性能。

多任务多约束差分进化算法中基于优秀导向的知识传递在煤矿综合能源系统调度中的应用