元胞微粒群算法及其在多维背包问题中的应用

针对离散微粒群算法早熟收敛问题,基于元胞自动机的原理和离散微粒群算法,提出一种元胞微粒群算法.将元胞及其邻居引入到算法中来保持种群的多样性,利用元胞的演化规则进行局部优化,避免算法陷入局部极值.通过对典型多维背包问题的仿真实验和与其他算法的比较,表明本算法可行有效,有良好的全局优化能力.     

蚂蚁算法在组合优化中的应用

蚂蚁算法是近年来新出现的一种随机型搜索寻优算法 ,自从在 TSP等著名问题中得到富有成效的应用之后 ,已引起越来越多的关注和重视 .本文进一步将这种新型的生物优化思想扩展到其他一些组合优化难题 ,包括目前尚缺乏有效求解手段的多目标组合优化问题 ,从实验上探索了蚂蚁算法的优化能力 ,获得了满意的效果     

一种差异工件单机批调度问题的蚁群优化算法

由于在利用蚁群算法构建差异工件(即工件有尺寸差异)单机批调度问题的解时,批的加工时间是不确定的.从而不能类似于经典调度问题的蚁群算法把批加工时间的倒数作为蚁群算法中的启发式信息,引入批的利用率和批的负载均衡率作为蚁群算法中的启发式信息,提出了JACO(ant colony optimization based a job sequence)和BACO(ant colony optimization based a batch sequence)两种蚁群优化算法.在算法JACO中,解的编码为工件序列,它对应着用BF(best fit)分批规则生成的调度方案,信息素代表工件间的排列顺序;在算法BACO中,解的编码为批序列,信息素代表工件间的批相关性,由此信息素通过中间信息素量来构造相应的解,并引入特定的局部优化策略,提高了算法的搜索效率.实验表明,与以往文献中的SA(simula-ted annealing)、GA(genetic algorithm)算法以及FFLPT(first-fit longest processing time)、BFLPT (best-fit longest processing time)启发式规则相比,算法JACO和BACO明显优于它们,且BACO算法比JACO算法效果更好.     

基于动态扫描和蚂蚁算法的物流配送网络优化研究

本文在对动态扫描和蚂蚁算法研究的基础上,针对蚂蚁算法在求解大规模物流配送问题中存在的不足,利用动态扫描方法在区域选择方面的实用性和蚂蚁算法在局部优化方面的优点,提出综合两种方法的混合算法,并进行了实验计算.计算结果表明,混合算法获得了较满意的效果.     

蚁群算法研究评述

对蚁群算法的相关研究领域进行了评述,阐述并分析了蚁群算法的研究进展,重点介绍了蚁群算法及其一系列改进算法的基本原理和应用研究成果,最后从发展重点和未来应用等方面探讨了蚁群算法研究的进一步发展方向.     

基于改进蚂蚁算法的拉动式供应链动态调度分析

分析了拉动式供应链调度动态性的产生根源,指出由于存在供应链协作成员之间复杂的协作与竞争关系,导致了供应链动态调度的特殊性;分析了供应链动态调度过程中的两个主要瓶颈.为了合理解决瓶颈问题,在优化供应链动态调度过程中引入蚁群觅食的寻优机理,并对其进行特定的算法设计及改进,提出供应链动态调度的蚂蚁寻优算法.仿真结果验证了算法可行有效.     

求解组合优化法问题的偶合算法综述

偶合算法(Coincidence Algorithm,COIN)是演化算法中最先进的一种。它主要用来解决组合问题。偶合算法属演化算法的一个分支,其主要功能是运用概率模式代替传统突变和交叉算子来解决问题。偶合算法模式是一个相邻事件(最初称符合)的联合概率表,它来源于许多备选的解决办法。偶合算法的独特之处是在学习和优化过程中它运用负相关学习(Neg-ative Correlation Learning,NCL)和传统的正相关学习(Positive Correlation Learning,PCL)相结合的方法。偶合算法在解决组合优化方法中非常有效。它的解释力和与现代其他算法的比较也显而易见。据此,本文旨在介绍偶合算法在解决一些工程问题如货郎担问题、生产线平衡问题、配线排序问题和工人配置问题的实际应用。     

基于自感应蚁群算法的VRPSDP问题研究

同时供货和取货的车辆路径问题是车辆路径问题的重要组成部分之一,问题的复杂性使得目前的主要求解方法局限于各种插入式启发算法。本文引用了近年来出现的蚁群算法,并通过对蚂蚁行为的深入研究,首次提出了感应因子、期望程度因子、距离性比因子以及加速因子的概念,在信息素更新方面融入了当前路径的距离特征,构建了一种全新的自感应蚁群算法。该方法充分利用全局分布的信息素感应信息,并且根据车辆容量支配值以及节点间距和节点-中心点间距性比进行状态转移,利用信息素更新公式中加速因子的动态调节有效地解决了算法快速收敛与陷入局部最优的矛盾。仿真试验证明了自感应蚁群算法的有效性,同时,该算法也拓展了车辆路径问题的算法空间。     

一类Flow Shop排序问题的混合遗传算法

描述了在平行顺序移动方式下Flow Shop排序问题的数学模型,构造了求解该问题的混合遗传算法．用计算机模拟计算的结果表明,该算法对解决这类Flow Shop排序问题是有效的     

Reduced costs propagation in an efficient implicit enumeration for the 01 multidimensional knapsack problem

In a previous work we proposed a variable fixing heuristics for the 0-1 Multidimensional knapsack problem (01MDK). This approach uses fractional optima calculated in hyperplanes which contain the binary optimum. This algorithm obtained best lower bounds on the OR-Library benchmarks. Although it is very attractive in terms of results, this method does not prove the optimality of the solutions found and may fix variables to a non-optimal value. In this paper, we propose an implicit enumeration based on a reduced costs analysis which tends to fix non-basic variables to their exact values. The combination of two specific constraint propagations based on reduced costs and an efficient enumeration framework enable us to fix variables on the one hand and to prune significantly the search tree on the other hand. Experimentally, our work provides two main contributions: (1) we obtain several new optimal solutions on hard instances of the OR-Library and (2) we reduce the bounds of the number of items at the optimum on several harder instances.     

A successive approximation algorithm for the multiple knapsack problem

It is well-known that the multiple knapsack problem is NP-hard, and does not admit an FPTAS even for the case of two identical knapsacks. Whereas the 0-1 knapsack problem with only one knapsack has been intensively studied, and some effective exact or approximation algorithms exist. A natural approach for the multiple knapsack problem is to pack the knapsacks successively by using an effective algorithm for the 0-1 knapsack problem. This paper considers such an approximation algorithm that packs the knapsacks in the nondecreasing order of their capacities. We analyze this algorithm for 2 and 3 knapsack problems by the worst-case analysis method and give all their error bounds.     

车辆路径问题的混合蚁群算法设计与实现

蚁群算法是一种新型的模拟进化算法,具有许多优良的性质,可以很好地解决TSP问题.在分析车辆路径问题(VRP)与TSP区别的基础上,论文将蚁群算法应用于VRP的求解,针对VRP的具体特点,构造了具有自适应功能的混合蚁群算法.该算法对基本规则作了进一步改进,并有机结合了爬山法、节约法等方法,以减少计算时间,避免算法停滞.指出可行解问题是蚁群算法的关键问题,提出了大蚂蚁数、近似解可行化等四个解决策略.计算机仿真结果表明,自适应混合蚁群算法性能优良,能够有效地求解VRP.     

Approximate and Exact Algorithms for Constrained (Un) Weighted Two-dimensional Two-staged Cutting Stock Problems

In this paper we propose two algorithms for solving both unweighted and weighted constrained two-dimensional two-staged cutting stock problems. The problem is called two-staged cutting problem because each produced (sub)optimal cutting pattern is realized by using two cut-phases. In the first cut-phase, the current stock rectangle is slit down its width (resp. length) into a set of vertical (resp. horizontal) strips and, in the second cut-phase, each of these strips is taken individually and chopped across its length (resp. width).First, we develop an approximate algorithm for the problem. The original problem is reduced to a series of single bounded knapsack problems and solved by applying a dynamic programming procedure. Second, we propose an exact algorithm tailored especially for the constrained two-staged cutting problem. The algorithm starts with an initial (feasible) lower bound computed by applying the proposed approximate algorithm. Then, by exploiting dynamic programming properties, we obtain good lower and upper bounds which lead to significant branching cuts. Extensive computational testing on problem instances from the literature shows the effectiveness of the proposed approximate and exact approaches.     

On the number of local minima for the multidimensional assignment problem

The Multidimensional Assignment Problem (MAP) is an NP-hard combinatorial optimization problem occurring in many applications, such as data association, target tracking, and resource planning. As many solution approaches to this problem rely, at least partly, on local neighborhood search algorithms, the number of local minima affects solution difficulty for these algorithms. This paper investigates the expected number of local minima in randomly generated instances of the MAP. Lower and upper bounds are developed for the expected number of local minima, E[M], in an MAP with iid standard normal coefficients. In a special case of the MAP, a closed-form expression for E[M] is obtained when costs are iid continuous random variables. These results imply that the expected number of local minima is exponential in the number of dimensions of the MAP. Our numerical experiments indicate that larger numbers of local minima have a statistically significant negative effect on the quality of solutions produced by several heuristic algorithms that involve local neighborhood search.Partially supported by the NSF grant DMI-0457473.     

Ant Colony System for a Dynamic Vehicle Routing Problem

An aboundant literature on vehicle routing problems is available. However, most of the work deals with static problems, where all data are known in advance, i.e. before the optimization has started. The technological advances of the last few years give rise to a new class of problems, namely the dynamic vehicle routing problems, where new orders are received as time progresses and must be dynamically incorporated into an evolving schedule. In this paper a dynamic vehicle routing problem is examined and a solving strategy, based on the Ant Colony System paradigm, is proposed. Some new public domain benchmark problems are defined, and the algorithm we propose is tested on them. Finally, the method we present is applied to a realistic case study, set up in the city of Lugano (Switzerland).     

面向虚拟企业的蚁群劳动分工建模与仿真

针对虚拟企业的复杂性特征,提出了以群集智能这种新颖的复杂系统建模手段研究虚拟企业的组建和运行过程.在将虚拟企业运行特点与蚁群劳动分工模型进行对比分析的基础上,将企业单元定义为人工蚂蚁,根据虚拟企业运行的要求重新设计蚂蚁的属性特征、任务执行和学习规则、活动环境等模块,建立了扩展的蚁群劳动分工模型.采用该模型对虚拟制造式、供应链式、组织虚拟式等3种具有代表性的虚拟企业组织进行建模和仿真,实验表明仿真结果与实际情况比较吻合,运行过程能够体现出虚拟企业运行的自组织行为特征,该模型可为类似虚拟企业的组建、任务规划和利益分配提供依据.最后分析了扩展蚁群劳动分工模型的内在特性和适用范围,并对今后研究的方向进行了展望.