基于改进蚁群算法的物流配送优化研究

物流配送是物流管理的重要环节,物流配送关系到物流活动的正常开展,因此,物流配送优化成为学术界和企业界的研究热点。而传统蚁群算法由于存在着缺点,所以采用一种改进蚁群算法以改善传统算法的不足。基于改进蚁群算法,构建物流配送模型,求解最优解。通过算例,求得最优解,实现目标函数最小化。结论表明：改进蚁群算法是求解物流配送优化问题的有效方法。     

基于改进蚁群算法的物流配送路径问题研究

在蚁群算法中采用新的信息素更新方式,对中小规模的物流配送路径问题进行求解．实验证明,该改进算法具有较好的全局寻优能力,在中小规模的物流配送路径问题求解中,取得了比较理想的效果．     

基于混沌蚁群算法的物流配送路径问题仿真研究

将混沌与最大最小蚂蚁算法相融合,在蚁群算法的信息素更新规则中加入混沌扰动量避免了在搜索过程中陷入局部极值.测试结果表明混沌蚁群算法能够有效地提高算法的全局寻优能力,对于物流配送路径问题的求解能够获得满意的结果.     

改进蚁群算法在物流配送路径问题中的应用

将最大最小蚂蚁算法与变异操作相融合,改进物流配送路径问题的求解,其算法首先采用最大最小蚂蚁算法产生较优解,然后使用变异操作对较优解进行优化.测试结果表明：该改进算法可以避免蚁群算法在搜索过程中陷入局部最优,有效地提高算法的全局寻优能力.     

蚁群优化算法在物流配送车辆路径问题中的应用研究

车辆路径问题(VRP)是一类物流配送领域具有广泛应用的组合优化问题,属于NP难题。一种改进的蚁群优化算法可以用于求解VRP。实验结果表明,采用蚁群优化算法能有效求解VRP问题。     

基于多代理蚁群算法的电子商务物流配送优化研究

利用多Agent技术,描述了电子商务物流配送优化路径模型,并分析了此模型的工作原理。针对电子商务物流配送优化路径问题,对蚁群算法进行改进,提高其搜索能力和加快收敛速度。通过仿真,验证了此方法在电子商务物流配送优化路径求解中具有很好的可行性与有效性。     

物流配送车辆路径优化问题的仿真研究

随着物流行业的发展。物流配送成本成为人们关注的问题。为了进一步降低日益复杂的物流配送成本．该文将一种改进的蚁群算法应用于物流配送车辆路径优化问题中,设计了求解物流配送路径优化问题的改进蚁群算法,并应用实例加以仿真计算,实验仿真证明了该改进蚁带算法的有效性。     

物流配送路径中蚁群算法改进策略

蚁群算法是求解物流配送最佳路径的有效仿生模拟方法,但存在早熟、停滞、局部最优等缺陷。文章从蚁群算法的数学本质出发,提出将单纯蚁群算法与爬山算法、混沌理论、K-均值聚类算法相结合等几种有效的改进策略,使得改进后的蚁群算法具有更多优越性。     

CVRP物流配送路径优化及应用研究

车辆行驶路径优化问题是智能安全交通网络的重要组成部分。针对传统车辆路径求解搜索时间过长、得不到最优解、求解质量不高的现况,在研究一般物流配送路径问题处理方法和数学模型的基础上,提出了一种改进的蚁群算法求解问题以提高构建路径的速度和质量,在限量车辆路径问题（CapacitatedVehicleRoutingProblem,CVRP）中用改进的蚁群算法来优化求解车物流的配送路径。通过MATLAB仿真结果表明,蚁群算法搜索速度相对较快,具有良好的全局求优能力,收敛结果表明可以准确求出最优路径,相比传统方案,优化后解的质量得到了提高,速度提高了80%左右,是一种可行性较高的求解物流配送路径优化问题的有效算法。     

基于蚁群算法的城市各区域垃圾回收优化

就垃圾回收优化建立模型,利用Dijkstra算法预处理位置数据,再以蚁群算法实现,使模拟结果最短距离为回收真实距离,所得最优回收次序对确定回收路线有参考意义.设计的2个解决方案中,方案一证明了蚁群算法解决城市小区垃圾回收的可行性,方案二实现城市各区域垃圾回收分区回收.     

一种融合加权策略及分布估计的蚁群优化算法

通过对蚁群算法、加权策略、分布估算算法等进行研究和分析,首先提出将加权策略应用于蚁群算法的信息素更新,有效地提高了算法的全局收敛速度,然后将蚁群算法与分布估算算法进行融合,从而避免了由于信息素的正反馈机制而陷入局部最优的问题,仿真实验表明该算法在收敛速度及最优路径求解方面有较好的改进。     

基于蚁群优化的多水平图划分算法

Partitioning is a fundamental problem with applications to many areas including data mining, parellel processing and Very-large-scale integration (VLSI) design. An effective multi-level algorithm for bisecting graph is proposed. During its coarsening phase, an improved matching approach based on the global information of the graph core is developed withits guidance function. During the refinement phase, the vertex gain is exploited as ant’s heuristic information and a positive feedback method based on pheromone trails is used to find the global approximate bipartitioning. It is implemented with American National Standards Institute (ANSI) C and compared to MeTiS. The experimental evaluation shows that it performs well and produces encouraging solutions on 18 different graphs benchmarks.     

基于蚁群算法的医用服务机器人路径规划方法

机器人路径规划问题是机器人学的一个重要研究领域，主要研究机器人依据某个或某些优化原则，在其工作空间中找到一条从起始点到目标点的能避开障碍物的最优路径。国内外学者对此作过大量的研究，主要采用的方法有局部的人工势场法(Artificial Potential Field)和遗传算法(cene     

基于遗传机制的蚁群算法求解连续优化问题

A new algorithm is presented by using the ant colony algorithm based on genetic method (ACG) to solve the continuous optimization problem.Each component has a seed set.The seed in the set has the value of component,trail information and fitness.The ant chooses a seed from the seed set with the possibility determined by trail information and fitness of the seed.The genetic method is used to form new solutions from the solutions got by the ants.Best solutions are selected to update the seeds in the sets and trail information of the seeds.In updating the trail information,a diffusion function is used to achieve the diffuseness of trail information.The new algorithm is tested with 8 different benchmark functions.     

Multi-group ant colony algorithm based on simulated annealing method

To overcome the default of single search tendency, the ants in the colony are divided into several sub-groups. The ants in different subgroups have different trail information and expectation coefficients. The simulated annealing method is introduced to the algorithm. Through setting the temperature changing with the iterations, after each turn of tours, the solution set obtained by the ants is taken as the candidate set. The update set is obtained by adding the solutions in the candidate set to the previous update set with the probability determined by the temperature. The solutions in the candidate set are used to update the trail information. In each turn of updating, the current best solution is also used to enhance the trail information on the current best route. The trail information is reset when the algorithm is in stagnation state. The computer experiments demonstrate that the proposed algorithm has higher stability and convergence speed.     

基于蚁群算法的仿生模式识别神经网络构造方法

仿生模式识别神经网络（BPRNN）同传统BP、RBF神经网络相比具有更好的模式识别能力;训练样本库变更后网络的重新训练时间更小,但该网络构造过程中样本覆盖几何体参数的选择对网络识别率和复杂度有很大影响.本文通过引入蚁群算法来构造并优化网络参数,实验证明该算法法能较好的平衡网络性能和复杂度.     

An adaptive ant colony system algorithm for continuous-space optimization problems

Ant colony algorithms comprise a novel category of evolutionary computation methods for optimization problems, especially for sequencing-type combinatorial optimization problems. An adaptive ant colony algorithm is proposed in this paper to tackle continuous-space optimization problems, using a new objective-function-based heuristic pheromone assignment approach for pheromone update to filtrate solution candidates. Global optimal solutions can be reached more rapidly by self-adjusting the path searching behaviors of the ants according to objective values. The performance of the proposed algorithm is compared with a basic ant colony algorithm and a Square Quadratic Programming approach in solving two benchmark problems with multiple extremes. The results indicated that the efficiency and reliability of the proposed algorithm were greatly improved. Project (No. 9845-005) supported by National High-Tech. Research & Development Plan, China     

An adaptive ant colony system algorithm for continuous-space optimization problems

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蚁群算法概述

蚁群算法是一种新型模拟进化算法，广泛应用于求解组合优化问题．对蚁群算法的原理、模型的建立及此算法的发展前景进行了剖析．