  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.
