Computer Engineering and Applications ›› 2019, Vol. 55 ›› Issue (7): 220-225.DOI: 10.3778/j.issn.1002-8331.1712-0227

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Research on Wind Farm Layout Based on Hyper-Heuristic

CHI Zongzheng, DONG Shaozheng, GUO Tong, REN Zhilei, ZHOU Kuanjiu, GUO He   

  1. School of Software, Dalian University ofTechnology, Dalian, Liaoning 116621, China
  • Online:2019-04-01 Published:2019-04-15


迟宗正,董绍正,郭  童,任志磊,周宽久,郭  禾   

  1. 大连理工大学 软件学院,辽宁 大连 116621

Abstract: 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 low-level 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.

Key words: hyper heuristic algorithm, wind farm layout, Differential Evolution(DE) algorithm, Covariance Matrix Adaptation Evolution Strategy(CMA-ES) algorithm

摘要: 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。

关键词: 超启发式算法, 风力发电机布局, 差分进化算法, 适应性协方差矩阵进化策略算法