Multiform Differential Evolution With Elite-Guided Knowledge Transfer for Coal Mine Integrated Energy Systems Constrained Dispatch
针对煤矿综合能源系统调度中高维、强耦合和多目标难题，提出一种多形态进化框架，通过约束耦合空间分解和精英引导知识迁移等策略，有效处理强约束并优化多目标，在案例中优于CPLEX和八种先进算法。
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 when solving the dispatch problem. To address this issue, we here develop a multiform EA framework that incorporates the dispatch-correlated domain knowledge to effectively deal with strong constraints and multiobjective optimization. Possible evolutionary multiform 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 multiform 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 a neighborhood-driven dual 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 (IES) by comparing with CPLEX solver and eight state-of-the-art constrained multiobjective EAs.