新能源汽车电池回收网点竞争选址模型及算法

Competitive location model and algorithm of new energy vehicle battery recycling outlets

针对考虑排队论的新能源汽车电池回收网点竞争设施选址问题,提出一种改进的人类学习优化(IHLO)算法。首先,构建包含排队时间约束、容量约束和门槛约束等条件的新能源汽车电池回收网点竞争设施选址模型;然后,考虑到该问题属于NP-hard问题,针对人类学习优化(HLO)算法前期收敛速度较慢、寻优精度不够高、求解稳定性不够高的不足,通过引入精英种群反向学习策略、团队互助学习算子和调和参数自适应策略提出IHLO算法;最后,以上海市和长江三角洲为例进行数值实验,并将IHLO算法和改进二进制灰狼(IBGWO)算法、改进二进制粒子群(IBPSO)算法、HLO算法和融合学习心理学的人类学习优化(LPHLO)算法进行比较。大、中、小三种不同规模的实验结果表明,IHLO算法在15个指标中的14个指标上表现最优,IHLO算法比IBGWO算法求解精度至少提高了0.13%,求解稳定性至少提高了10.05%,求解速度至少提高了17.48%。所提算法具有较高的计算精度和优化速度,可有效解决竞争设施选址问题。

To solve the competitive facility location problem of new energy vehicle battery recycling outlets considering queuing theory,an Improved Human Learning Optimization(IHLO)algorithm was proposed.First,the competitive facility location model of new energy vehicle battery recycling outlets was constructed,which included queuing time constraints,capacity constraints,threshold constraints and other constraints.Then,considering that this problem belongs to NP-hard problem,in view of the shortcomings of Human Learning Optimization(HLO)algorithm,such as low convergence speed,optimization accuracy and solving stability in the early stage,IHLO algorithm was proposed by adopting elite population reverse learning strategy,group mutual learning operator and adaptive strategy of harmonic parameter.Finally,taking Shanghai and the Yangtze River Delta as examples for numerical experiments,IHLO was compared with Improved Binary Grey Wolf Optimization(IBGWO)algorithm,Improved Binary Particle Swarm Optimization(IBPSO)algorithm,HLO and Human Learning Optimization based on Learning Psychology(LPHLO)algorithm.For large,medium and small scales,the experimental results show that IHLO algorithm has the best performance in 14 of the 15 indicators;compared with IBGWO algorithm,the solution accuracy of IHLO algorithm is improved by at least 0.13%,the solution stability is improved by at least 10.05%,and the solution speed is improved by at least 17.48%.The results show that the proposed algorithm has high computational accuracy and fast optimization speed,which can effectively solve the competitive facility location problem.