A Glowworm Swarm Optimization algorithm for the Vehicle Routing Problem with Stochastic Demands

The Glowworm Swarm Optimization (GSO) algorithm is a relatively new swarm intelligence algorithm that simulates the movement of the glowworms in a swarm based on the distance between them and on a luminescent quantity called luciferin. This algorithm has been proven very efficient in the problems that has been applied. However, there is no application of this algorithm, at least to our knowledge, in routing type problems. In this paper, this nature inspired algorithm is used in a hybrid scheme (denoted as Combinatorial Neighborhood Topology Glowworm Swarm Optimization (CNTGSO)) with other metaheuristic algorithms (Variable Neighborhood Search (VNS) algorithm and Path Relinking (PR) algorithm) for successfully solving the Vehicle Routing Problem with Stochastic Demands. The major challenge is to prove that the proposed algorithm could efficiently be applied in a difficult combinatorial optimization problem as most of the applications of the GSO algorithm concern solutions of continuous optimization problems. Thus, two different solution vectors are used, the one in the continuous space (which is updated as in the classic GSO algorithm) and the other in the discrete space and it represents the path representation of the route and is updated using Combinatorial Neighborhood Topology technique. A migration (restart) phase is, also, applied in order to replace not promising solutions and to exchange information between solutions that are in different places in the solution space. Finally, a VNS strategy is used in order to improve each glowworm separately. The algorithm is tested in two problems, the Capacitated Vehicle Routing Problem and the Vehicle Routing Problem with Stochastic Demands in a number of sets of benchmark instances giving competitive and in some instances better results compared to other algorithms from the literature.     

求解带时间窗车辆路径问题的改进粒子群算法

通过分析已有粒子群算法对有时间窗约束的车辆路径问题求解质量不高的原因,提出了一种基于粒子交换原理的整数粒子更新方法。采用构造的双层粒子进化算法分别对8个和20个任务点的有时间窗约束的车辆路径问题求解,数值实验结果表明算法的求解精度和耗时均优于已有算法。     

基于量子粒子群优化算法的车辆路径问题

针对粒子群优化算法的搜索空间有限、容易出现早熟现象的缺陷,提出将一种基于量子行为的粒子群优化算法用于求解车辆路径问题.车辆路径问题是组合优化问题中的NP-难问题.将量子粒子群算法用于车辆路径问题求解,用粒子的位置表示车辆路径,建立车辆路径的数学模型.与粒子群算法相比,量子粒子群算法提高了最优路径搜索的成功率,能更有效的求解问题.     

车辆路径问题的改进混合粒子群算法研究

针对各种启发式算法在求车辆路径问题(VRP)中的缺陷,提出了改进的混合粒子群算法(MHPSO)的求解方法.分析了基于速度-位置更新策略传统粒子群算法在解决离散的和组合优化问题的不足.考虑到算法在求解过程中种群多样性的损失过快,引进了种群的多样性测度参数-平均粒距,以保持种群的多样性.同时利用混沌运功的随机性、遍历性和规律性等特性,采用混沌初始化粒子编码.详细讨论了该算法在车辆路径问题中的求解策略.针对同一个实例,将改进的混合粒子群算法与遗传算法从多个角度进行比较.仿真结果表明,论文所提出的算法性能较好,可以快速、有效求得车辆路径问题的优化解或近似优化解.     

基于车辆路径问题的带近邻因子的粒子群算法

提出了一种改进的粒子群算法。该算法通过引入近邻因子,增强了当前粒子的学习功能,克服了基本粒子群算法易陷于局部最优的缺陷,提高了算法进化的收敛精度。将该算法用于解决车辆路径问题,实验结果表明具有较好的性能和很好的应用价值。     

需求随机车辆调度问题的遗传算法研究

文中主要研究了需求随机的车辆调度问题,其中服务需求量满足二项式分布.首先分析了随机分布规律中分布参数与车辆调度服务失败的允许次数之间的关系,并在此基础上建立了需求随机的车辆调度问题数学模型,由于对允许服务失败的次数没有明确限制,使得模型能适应多种实际问题.针对建立的数学模型设计了问题求解的遗传算法,算法中对遗传算子进行了改进.数值计算结果表明:文中所提出的模型和设计的遗传算法能够有效地解决服务需求随机的车辆调度问题.     

改进型PSO算法在VRP中的应用

运用罚函数法将约束优化问题转化为无约束优化问题,同时采用实数编码方案,将离散的车辆路径问题转化成准连续优化问题,在此基础上,用改进的粒子群优化算法求解最优值.改进的粒子群算法引入了杂交PSO模型和变异算子.仿真实验结果表明,该算法在保持粒子种群多样性、提高收敛速度和搜索精度、扩大搜索范围、避免过早收敛于局部极值点等方面...     

多车种随机需求车辆路由问题的遗传算法

该文给出了多种类型车辆随机节点需求车辆路由问题(VRP)的遗传算法。将各个节点的需求作为一个随机数,同一条路径上车辆允许路由失败(routingfailure)的次数作为一个可控制的参数,对于节点需求服从给定的已知分布的VRP问题,给出了一些更简单实用的递推公式,并在此基础上通过巧妙地设计交配规则,得到遗传算法,并经实例运算对比,效果很好。     

多组播路由问题的粒子群优化算法

具有带宽和时延约束的多组播路由优化问题比组播路由问题更加复杂.为了快速求得多组播路由问题的最优解,提出一种基于树结构演化的粒子群优化算法.粒子由以组播树为分量的向量构成,表示问题的一个可行解,粒子飞行通过树的演化实现.通过在粒子群的环状社会结构中引入粒子视觉半径提高粒子的邻域学习能力;采用树结构变异方法对粒子进行变异提高算法跳出局部解的可能性;根据不满足约束条件的状况对非可行解采取分别惩罚粒子和粒子分量的策略.在随机产生的具有26,50和100个节点的网络拓扑上进行了仿真实验,实验结果表明,提出的算法具有更好的求解质量和较快的收敛速度.     

随机装卸工问题的粒子群算法

在装卸工问题的基础上提出了随机装卸工问题及其求解策略。根据问题的特点设计了相应的粒子群优化算法,并通过数值算例就其求解精度和速度与标准遗传算法进行了对比分析。     

求解车辆路径问题的离散粒子群算法

考虑车辆行驶时间和顾客服务时间的不确定性,建立了以车辆配送总费用最小为目标的机会约束规划模型,将其进行清晰化处理,使之转化为一类确定性数学模型,并构造了求解该问题的一种离散粒子群算法。算法重新定义了粒子的运动方程及其相关离散量运算法则,并设计了排斥算子来维持群体的多样性。与标准遗传算法和粒子群算法比较,该算法能够有效避免算法陷入局部最优,取得了满意的结果。     

基于交叉熵法解决随机需求车辆路径问题

提出一种解决随机需求车辆路径问题(VRPSD)新方法。首先,采用预防性补救措施,建立了VRPSD模型,其次,为提高标准交叉熵(SCE)法性能,对用于更新Markov转移矩阵的路径,设计了根据分位值改变大小的自适应调整方法。仿真结果验证了该算法解决VRPSD的有效性。     

联盟运输调度问题的粒子群算法研究

联盟运输调度问题是在基本运输调度问题基础上所发展起来的、具有重要实用价值的一类组合优化难题.粒子群算法(PSO)是一种新兴的基于群智能的演化计算技术,该算法与传统方法相比有着较高的收敛速度和计算精度,可以在解空间内高效地寻找到全局最优解.将其应用于联盟运输调度问题,并针对联盟运输调度问题中最优解的分布特点,对标准粒子群算法进行了改进,克服了标准粒子群算法收敛速度过快且易收敛于局部最优的缺点.对比实验结果表明,改进后的粒子群算法可以快速、有效求得最优解.     

A New Multi-objective Competitive Open Vehicle Routing Problem Solved by Particle Swarm Optimization

This paper presents a new variant of an open vehicle routing problem (OVRP), in which competition exists between distributors. In the OVRP with competitive time windows (OVRPCTW), the reaching time to customers affects the sales amount. Therefore, distributors intend to service customers earlier than rivals, to obtain the maximum sales. Moreover, a part of a driver??s benefit is related to the amount of sales; thus, the balance of goods carried in each vehicle is important in view of the limited vehicle capacities. In this paper, a new, multi-objective mathematical model of the homogeneous and competitive OVRP is presented, to minimize the travel cost of routes and to maximize the obtained sales while concurrently balancing the goods distributed among vehicles. This model is solved by the use of a multi-objective particle swarm optimization (MOPSO) algorithm, and the related results are compared with the results of NSGA-II, which is a well-known multi-objective evolutionary algorithm. A comparison of our results with three performance metrics confirms that the proposed MOPSO is an efficient algorithm for solving the competitive OVRP with a reasonable computational time and cost.     

混合粒子群算法求解交通路网中的车辆调度问题

为使路网中的车辆调度问题更加符合实际交通状况．提出了改进的车辆调度模型;针对这个模型,将粒子群算法和模拟退火算法相结合,设计了混合粒子群算法求其有效近似解;最后结合西安市实际交通调查数据．编程实现混合粒子群算法对模型进行计算与仿真,仿真结果表明了此方法的有效性．     

A Novel Model for the Time Dependent Competitive Vehicle Routing Problem: Modified Random Topology Particle Swarm Optimization

This paper presents a novel model for a time dependent vehicle routing problem when there is a competition between distribution companies for obtaining more sales. In a real-world situation many factors cause the time dependency of travel times, for example traffic condition on peak hours plays an essential role in outcomes of the planned schedule in urban areas. This problem is named as “Time dependent competitive vehicle routing problem” (TDVRPC) which a model is presented to satisfy the “non-passing” property. The main objectives are to minimize the travel cost and maximize the sale in order to serve customers before other rival distributors. To solve the problem, a Modified Random Topology Particle Swarm Optimization algorithm (RT-PSO) is proposed and the results are compared with branch and bound algorithm in small size problems. In large scales, comparison is done with original PSO. The results show the capability of the proposed RT-PSO method for handling this problem.     

随机交叉粒子群优化算法

针对粒子群优化算法容易陷入局部极值点、进化后期收敛慢和优化精度较差等缺点,设计了一种随机交叉算子,提出了随机交叉粒子群优化算法。该算法在每次迭代中,对当前粒子和整个粒子群的最优粒子进行随机交叉,产生新的较优粒子并代替原来的粒子,从而加快了算法的收敛速度,增强了算法的寻优能力。仿真结果表明,该算法具有较高的优化性能。     

结合SCE法的粒子群优化QoS路由算法

QoS (Quality of Service) 路由问题是一个非线性的组合优化问题,理论上已证明了该问题是NP完全问题.粒子群优化算法是一种基于群智能演化计算技术,PSO在求解连续性优化问题上得到了较好的应用,而把PSO算法用于求解路由算法等离散性问题还比较少见,同时,PSO算法在收敛过程中还存在随机性,某些情况下会出现停滞现象.为此本文提出了一种结合SCE(Shuffled Complex Evolution)法的粒子群优化方法用于求解QoS路由问题.该算法通过引入插入算子、删除算子、算子系列和基本算子序列等概念,对基本的粒子群优化算法进行改进;通过采用SCE法,使算法跳出局部最优解的限制.仿真结果显示,该算法取得了满意的效果,在寻优速度上优于遗传算法,也提高了算法收敛到最优解的能力.     

随机微粒群优化算法

微粒群优化算法是继蚁群算法之后又一种新的基于群体智能的启发式全局优化算法,其概念简单、易于实现,而且具有良好的优化性能,目前已在许多领域得到应用。但在求解高维多峰函数寻优问题时,算法易陷入局部最优。该文结合模拟退火算法的思想,提出了一种改进的微粒群优化算法——随机微粒群优化算法,该算法在运行初期具有更强的探索能力,可以避免群体过早陷入局部极值点。基于典型高维复杂函数的仿真结果表明,与基本微粒群优化算法相比,该混合算法具有更好的优化性能。     

粒子群算法在LRP问题中的应用

研究了带软时间窗的定位路径问题(LRPSTW)的改进粒子群算法(PSO),在考虑了LRPSTW的整体性基础上,基于一阶段的求解思路设计了带交换子和交换序及贪婪变异算子的改进粒子群算法来求解LRPSTW问题,从而有效地提高了算法的求解效率和解质量.最后通过实验证明了算法的可行性和有效性.