针对波浪能转换器(WEC)阵列发电效率提升问题,提出一种基于改进蜜獾算法的三系WEC阵列优化方法.首先,为克服原始蜜獾算法(HBA)收敛速度慢、收敛精度低等缺陷,引入佳点集初始化、混沌机制和蜜獾种群变异3种策略对原始HBA进行改进.此外,为了验证改进蜜獾算法(IHBA)的先进性和有效性,开展2个浮标、10个浮标和20个浮标3个不同规模的WEC阵列优化实验.2浮标阵列仿真结果表明,WEC阵列优化存在多组最优解,且IHBA、HBA、遗传算法和粒子群优化算法都能以不同速度找到最优解.然而,随着WEC阵列规模的增大,3种对比算法都会陷入局部最优解.相反地,IHBA依然表现出较强寻优能力并能搜寻到全局最优解.最后,IHBA所获10浮标和20浮标的阵列q因子分别高达1.059和0.968,远优于其他3种算法.

In order to enhance the generation efficiency of wave energy converter (WEC) arrays, an optimization method for three-tether WEC array based on an improved honey badger algorithm is proposed. First, to overcome the shortcomings of the primal honey badger algorithm (HBA), such as slow convergence speed and low convergence accuracy, three improvement strategies are introduced, i.e., good point set initialization, chaos mechanism, and honey badger population mutation. Then, three wave farms including 2-buoy, 10-buoy, and 20-buoy are tested to verify the advancement and effectiveness of the improved honey badger algorithm (IHBA). The simulation results of the 2-buoy array demonstrate that there are multiple groups of optimal solutions in WEC array optimization. Furthermore, IHBA, HBA, genetic algorithm, and particle swarm optimization can find these optimal solutions at different speeds. Nevertheless, with increasing size of the WEC array, three comparative algorithms fall into local optima solutions. On the contrary, IHBA still exhibits a strong optimization ability and can seek global optima solutions. Finally, the q-factor values obtained by IHBA in 10-buoy and 20-buoy arrays reach 1.059 and 0.968, respectively, which are dramatically larger than those of other algorithms.