摘要
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.
作者
王生生
张伟
董如意
李文辉
WANG Sheng-sheng;ZHANG Wei;DONG Ru-yi;LI Wen-hui(College of Computer Science and Technology,Jilin University,Changchun 130012,China;College of Software,Jilin University,Changchun 130012,China;College of Information and Control Engineering,Jilin Institute of Chemical Technology,Jilin 132022,China)
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2020年第2期170-175,共6页
Journal of Northeastern University(Natural Science)
基金
吉林省科技发展计划项目(20190302117GX,20180101334JC,20170204020GX)
吉林省发展改革委员会创新能力建设(高技术产业部分)项目(2019C053-3)
关键词
电动汽车
充换电站
优化调度
群智能
蚱蜢算法
electric vehicle
battery swapping station
optimal dispatch
swarm intelligence
grasshopper optimization algorithm
