求解风力发电机布局问题的超启发式算法研究

Research on Wind Farm Layout Based on Hyper-Heuristic

针对大规模问题求解效率不高、结果不理想等问题,以影响参数多变的风力发电机布局问题为研究对象,设计并实现了超启发式算法策略,底层算子用差分进化(Differential Evolution,DE)算法和适应性协方差策略(Covariance Matrix Adaptation Evolution Strategy,CMA-ES)算法,高层策略用启发式调用策略选择底层算子求解在不同场景、不同风力参数等多种情况下的风力发电机布局情况。实验将权值选择策略与DE算法、CMA-ES算法和随机调度策略进行比较,最终数据表明该策略求解风力发电布局的效果远高于其他三种。

Aiming at low efficiency and unsatisfactory results while solving the large-scale problems,to take the wind farm layout problem with variable parameters as a study target,the strategy of hyper heuristics is designed and implemented.The paper selects the DE(Differential Evolution)algorithm and CMA-ES(Covariance Matrix Adaptation Evolution Strategy)algorithm as the low-level operators,and at the high level it uses the hyper heuristics algorithm to call the lowlevel operators to solve the wind farm layout problem under different complicated conditions.By contrast,the experiment data imply that the new strategy is more efficient and flexible.