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.

翻译：煤矿综合能源系统的调度优化因高维度、强耦合约束及多目标特性而极具挑战性。现有约束多目标进化算法难以定位多个小型且不规则的可行域，导致其无法有效应对此类问题。为此，本文提出一种融合调度相关领域知识的多任务进化算法框架，以有效处理强约束与多目标优化问题。首先，基于复杂约束关系分析与处理（包括约束耦合空间分解、约束强度分类及约束处理技术），探讨了可行的进化多任务构建策略。在该多任务进化优化框架内，进一步提出两项策略：一是通过设计特殊拥挤距离机制从各任务中筛选优势个体，实现精英引导的知识传递；二是采用基于自适应邻域技术的变异策略，以有效平衡差分进化算法中各优化任务的多样性与收敛性。通过将所提算法与CPLEX求解器及七种约束多目标进化算法进行对比，以某煤矿综合能源系统为案例，验证了该算法在可行性、收敛性和多样性方面的优越性能。