This paper models the calculation of the optimal matching speeds of passenger and freight trains with various stage control methods for speed in mixed operations, presents a algorithm for the solution and justifies ...This paper models the calculation of the optimal matching speeds of passenger and freight trains with various stage control methods for speed in mixed operations, presents a algorithm for the solution and justifies it with a practical example.展开更多
This paper addresses the shortcomings of the Sparrow and Eagle Optimization Algorithm (SBOA) in terms of convergence accuracy, convergence speed, and susceptibility to local optima. To this end, an improved Sparrow an...This paper addresses the shortcomings of the Sparrow and Eagle Optimization Algorithm (SBOA) in terms of convergence accuracy, convergence speed, and susceptibility to local optima. To this end, an improved Sparrow and Eagle Optimization Algorithm (HS-SBOA) is proposed. Initially, the algorithm employs Iterative Mapping to generate an initial sparrow and eagle population, enhancing the diversity of the population during the global search phase. Subsequently, an adaptive weighting strategy is introduced during the exploration phase of the algorithm to achieve a balance between exploration and exploitation. Finally, to avoid the algorithm falling into local optima, a Cauchy mutation operation is applied to the current best individual. To validate the performance of the HS-SBOA algorithm, it was applied to the CEC2021 benchmark function set and three practical engineering problems, and compared with other optimization algorithms such as the Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA) to test the effectiveness of the improved algorithm. The simulation experimental results show that the HS-SBOA algorithm demonstrates significant advantages in terms of convergence speed and accuracy, thereby validating the effectiveness of its improved strategies.展开更多
针对时变速度下的低碳配送需求,本文以配送总成本最小化为目标,构建考虑三维装载和时间窗约束的绿色车辆路径优化模型。模型考虑时变速度和实时载重对车辆燃油消耗量的影响。为准确计算行驶时间和油耗,采用二分K-means聚类算法对时段进...针对时变速度下的低碳配送需求,本文以配送总成本最小化为目标,构建考虑三维装载和时间窗约束的绿色车辆路径优化模型。模型考虑时变速度和实时载重对车辆燃油消耗量的影响。为准确计算行驶时间和油耗,采用二分K-means聚类算法对时段进行合理划分。设计两阶段算法求解模型:第一阶段采用自适应大规模邻域搜索(adaptive large neighborhood search,ALNS)算法以确定车辆配送路径;第二阶段采用遗传算法(genetic algorithm,GA)对货物进行三维装载顺序的可行性校验。算例结果表明,基于二分K-means聚类算法的时段划分方法能更精确地计算总成本,从而验证了本文所构建的模型和所设计的算法具有可行性和有效性。展开更多
文摘This paper models the calculation of the optimal matching speeds of passenger and freight trains with various stage control methods for speed in mixed operations, presents a algorithm for the solution and justifies it with a practical example.
文摘This paper addresses the shortcomings of the Sparrow and Eagle Optimization Algorithm (SBOA) in terms of convergence accuracy, convergence speed, and susceptibility to local optima. To this end, an improved Sparrow and Eagle Optimization Algorithm (HS-SBOA) is proposed. Initially, the algorithm employs Iterative Mapping to generate an initial sparrow and eagle population, enhancing the diversity of the population during the global search phase. Subsequently, an adaptive weighting strategy is introduced during the exploration phase of the algorithm to achieve a balance between exploration and exploitation. Finally, to avoid the algorithm falling into local optima, a Cauchy mutation operation is applied to the current best individual. To validate the performance of the HS-SBOA algorithm, it was applied to the CEC2021 benchmark function set and three practical engineering problems, and compared with other optimization algorithms such as the Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA) to test the effectiveness of the improved algorithm. The simulation experimental results show that the HS-SBOA algorithm demonstrates significant advantages in terms of convergence speed and accuracy, thereby validating the effectiveness of its improved strategies.
文摘针对时变速度下的低碳配送需求,本文以配送总成本最小化为目标,构建考虑三维装载和时间窗约束的绿色车辆路径优化模型。模型考虑时变速度和实时载重对车辆燃油消耗量的影响。为准确计算行驶时间和油耗,采用二分K-means聚类算法对时段进行合理划分。设计两阶段算法求解模型:第一阶段采用自适应大规模邻域搜索(adaptive large neighborhood search,ALNS)算法以确定车辆配送路径;第二阶段采用遗传算法(genetic algorithm,GA)对货物进行三维装载顺序的可行性校验。算例结果表明,基于二分K-means聚类算法的时段划分方法能更精确地计算总成本,从而验证了本文所构建的模型和所设计的算法具有可行性和有效性。
