改进蚱蜢算法在电动汽车充换电站调度中的应用

Modified Grasshopper Optimization Algorithm and Applications in Optimal Dispatch of Electric Vehicle Battery Swapping Station

电动汽车充换电站调度优化问题一般采用群智能优化算法求解,但现有算法存在陷入局部最优、早熟收敛等缺陷,因此提出一种改进的蚱蜢算法:采用边界反弹机制,提高算法效率;引入正余弦搜索机制,加强算法的全局搜索能力;采用Lévy飞行对粒子进行随机扰动,防止种群陷入局部最优;采用非线性收敛策略加快算法后期的收敛速度.实验结果表明,该算法在电动汽车充换电站调度优化问题上,性能优于原始蚱蜢算法以及其他现有群智能算法.

The dispatch of electric vehicle battery swapping station is usually optimized by swarm intelligence algorithms.However,the existing algorithms are easily trapped in local optimum and premature convergence.Thus,an improved grasshopper optimization algorithm(IGOA)is proposed to achieve optimal dispatch.In the IGOA,the boundary bounce strategy is adopted to improve the efficiency;the sine/cosine algorithm is introduced to enhance the global searching ability;the Lévy flight is applied to perturb the particles randomly to keep the algorithm from being trapped in local optimum;the nonlinear operation is used to accelerate the convergence rate at the later stage of the algorithm.The simulation results show that the IGOA outperforms GOA and several other swarm intelligence algorithms as to the optimal dispatch of electric vehicle battery swapping station.