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.     

Particle Swarm Optimization for the Vehicle Routing Problem with Stochastic Demands

This paper introduces a new hybrid algorithmic approach based on Particle Swarm Optimization (PSO) for successfully solving one of the most popular supply chain management problems, the Vehicle Routing Problem with Stochastic Demands (VRPSD). The VRPSD is a well known NP-hard problem in which a vehicle with finite capacity leaves from the depot with full load and has to serve a set of customers whose demands are known only when the vehicle arrives to them. A number of different variants of the PSO are tested and the one that performs better is used for solving benchmark instances from the literature.     

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.     

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。     

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.     

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。     

时相关车辆路径规划问题的改进A*算法

时相关车辆路径问题是研究时变路网环境下的车辆路径优化问题.首先,分别采用阶跃函数和分段连续函数描述不同路径上的跨时段行驶速度和威胁度,将路径时间指标和路径威胁指标表示成时相关函数;其次,为提高搜索效率,对传统A*算法进行改进,在启发函数中增加了最短路径中当前结点的父结点信息,构造了包含里程指标、时间指标和威胁指标的时相关启发函数;最后,构造了包含100个结点、190条路径的车辆机动保障路网模型,通过仿真验证了该算法的有效性.     

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。     

一种基于加权有向拓扑的改进粒子群算法

研究粒子群优化算法(PSO)的拓扑结构和信息流动,以提高算法性能是PSO的一个有意义的研究方向。RuiMendes等人提出的全联通型算法(FIPSO),其拓扑结构本质上是加权无向图,两个邻接点之间的相互影响是对等的,与社会人际网络的真实情况不符。提出了一种改进型算法,重新构造了加权函数,体现了粒子之间影响的不平衡性。仿真结果显示:该改进算法对收敛速度和稳定性均有非常好的改善。     

一种基于加权有向拓扑的改进粒子群算法

研究粒子群优化算法（PSO）的拓扑结构和信息流动，以提高算法性能是PSO的一个有意义的研究方向。RuiMendes等人提出的全联通型算法（FIPSO），其拓扑结构本质上是加权无向图，两个邻接点之间的相互影响是对等的，与社会人际网络的真实情况不符。提出了一种改进型算法，重新构造了加权函数，体现了粒子之间影响的不平衡性。仿真结果显示：该改进算法对收敛速度和稳定性均有非常好的改善。     

一种新的求解置换Flowshop问题的粒子群算法

提出了一种通用的基于位置排序的粒子群算法（PSMPSO）并应用于置换Flowshop问题。采用三维粒子表示法,通过对粒子位置排序生成调度方案,将实数编码的粒子位置映射到自然数序列,采用基于粒子位置互换的局部搜索策略来提高算法收敛精度。仿真结果显示了该算法的可行性和有效性。     

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.     

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．     

基于并行粒子群算法的带时间窗车辆路径问题

提出求解带时间窗车辆路径问题的多群并行的粒子群算法。为了提高算法的收敛速度,在每个粒子群中嵌入了记忆功能。针对基本粒子群算法在求解有时间窗车辆路径问题时初始解的单一性导致局部收敛的问题,对两个种群采用了两种不同的初始化方法,并在进化过程中,两个种群相互用记忆粒子替换对方种群中的较差粒子。最后将该算法的运行结果与其他算法进行比较,表明该算法的有效性。     

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.     

一种新的进化粒子群算法及其在TSP中的应用

基于协同进化的思想,针对离散组合优化的NP难问题,提出一种新的混合粒子群进化算法。该算法采用了有效的编码方式;定义了两个粒子间的位置加法操作以实现个体之间的信息交换;引入变异算子保持种群多样性。该算法应用于TSP优化计算,能用较小的计算代价得到比传统方法更满意的解,实验结果表明该算法是有效的。     

A Particle Swarm Optimization Based Deep Learning Model for Vehicle Classification

Image classification is a core field in the research area of image processing and computer vision in which vehicle classification is a critical domain. The purpose of vehicle categorization is to formulate a compact system to assist in real-world problems and applications such as security, traffic analysis, and self-driving and autonomous vehicles. The recent revolution in the field of machine learning and artificial intelligence has provided an immense amount of support for image processing related problems and has overtaken the conventional, and handcrafted means of solving image analysis problems. In this paper, a combination of pre-trained CNN GoogleNet and a nature-inspired problem optimization scheme, particle swarm optimization (PSO), was employed for autonomous vehicle classification. The model was trained on a vehicle image dataset obtained from Kaggle that has been suitably augmented. The trained model was classified using several classifiers; however, the Cubic SVM (CSVM) classifier was found to outperform the others in both time consumption and accuracy (94.8%). The results obtained from empirical evaluations and statistical tests reveal that the model itself has shown to outperform the other related models not only in terms of accuracy (94.8%) but also in terms of training time (82.7 s) and speed prediction (380 obs/sec).     

改进的粒子群算法求解蛋白质结构预测问题

蛋白质的生物学功能是由其空间结构决定的,因此,蛋白质结构预测就成为生物信息学领域中极具挑战性的问题之一.粒子群算法是一种新的群智能算法,优势在于简单容易实现,又有深刻的智能背景.在优化领域,粒子群算法适用 于求解连续优化问题,而基于HP格点模型的蛋白质结构预测问题是一个离散问题.因此,文中通过借鉴单点调整算法的思...