多维背包问题的二进制蚂蚁算法

针对著名的多维背包问题(MKP), 在蚁群优化系统高维立方体结构的基础上,提出了一种二进制蚂蚁算法(BAS).与其他求解MKP问题的蚂蚁算法不同,BAS根据二进制解的结构设计了特殊的信息素放置方式,同时在算法的迭代过程中允许非可行解的产生,并通过基于问题特征信息的修改算子修复每次迭代所产生的非可行解.BAS算法采用了特殊的信息素更新规则,使得各个选择路径上的信息素可以直接作为选择概率,同时,为了避免算法陷入早熟,BAS设计了简单的局部搜索法,并根据算法所处的不同收敛状况,采用了不同的信息素更新规划和信息素重新初始化的方法.针对MKP基准问题的实验结果表明,BAS具有超越其他蚂蚁算法的求解结果,其求解不同基准测试问题的能力表明了BAS具有解决超大规模MKP问题的潜力.     

粒子群算法及其在调度中的应用综述

粒子群算法是通过对鸟群捕食行为进行的观察和研究而提出的一种群智能优化算法,通过群体中个体之间的协作和信息共享来寻找最优解。本文在介绍粒子群算法的基本原理基础上总结了目前主要的粒于群改进方法以及在调度中的应用,为未来的研究和企业调度工作提供了有力的依据。     

基于微粒群算法的单机不同尺寸工件批调度问题求解

提出了一种改进的具有全局搜索能力的微粒群算法,对工件尺寸有差异的单机批调度问题的制造跨度进行优化。针对问题中工件尺寸不同且分批加工的特点,设计了微粒的编码方式;对进化过程中产生的极优解,采用了混沌优化策略进行改进,避免早熟收敛的问题。仿真实验结果表明,本文算法的时间性能和近似解质量均优于现有的其他方法。     

双层规划问题的粒子群算法研究

提出一种求解一般双层规划问题的层次粒子群算法.和传统的针对特定类型的问题或者基于特定假定假设条件所设计的算法不同,所提出的算法是一个层次算法框架,它通过模拟双层规划的决策过程来直接求解一般双层规划问题.层次粒子群算法将求解一般双层规划问题转化为通过两个变形粒子群算法的交互迭代来求解上下两层规划问题.同其它算法的实验结果比较表明层次粒子群算法是一个有效的求解一般双层规划问题的方法.     

粒子群优化算法及其在电力系统中的应用

粒子群优化算法是一种新兴的群体智能优化技术,适用于目前科学领域、工程领域和经济领域中很多复杂的、非线性的甚至非凸形式的最优化问题.本文介绍了PSO算法的基本原理及其在负荷经济分配、无功优化、最优潮流计算等方面的应用.     

基于约束的离散粒子群算法求解大规模指派问题

针对现有进化算法在求解传统指派问题时因取整而影响优化效果的问题,采用了一种基于AllDifferent约束的置换离散粒子群优化算法,该算法针对指派问题中各变量不能重复取值的特点,改进了算法的迭代方式,并引入了模拟退火的差解接受准则以提高优化效果,仿真算例表明改进后的算法在质量上和时间上更具有效性.     

粒子群算法在VRP中的应用

物流配送车辆路径问题(VRP)属于NP—hard问题。谈文针对粒子群算法的局限性,引入了一种动态改变惯性权重的粒子群算法,在优化迭代过程中,惯性权重随粒子的位置和目标函数的性质而变化。实验结果表明,改进后的算法能使收敛速度显著加快,而且不容易陷入局部最优。     

改进蚁群算法在海洋工程群项目资源调度中的应用

当今海洋工程群项目管理中的瓶颈之一是人力、资金、设备及材料等资源的合理、动态调度问题。针对此问题,引入了基于信息熵的改进蚁群算法。该方法将资源需在各个分项目中占用的时间与资源的急需程度与之比作为算法中的启发式信息进行处理。与传统调度方法的比较及海洋工程群项目资源动态调度工程实例表明,该方法可实现资源的合理、动态调度,为海洋工程及其他工程领域群项目管理提供了一较为有效的资源调度算法。     

粒子群算法求解无能力约束生产批量计划问题

经典的粒子群优化算法是一个在连续的定义域内搜索数值函数极值的有效方法.目前,粒子群算法(particle swarm optimization,PS0)已经成为优化领域中的一个重要的优化工具,其应用在很多优化问题中都可以见到.虽然粒子群算法的应用范围已经十分广泛,但是关于应用其求解多级生产批量计划问题(multilevel lot-sizing problem,MLLs)的文章并不多见.文章提出结合遗传算法(genetic algorithm,GA)变异算子的混合粒子群优化算法(hybrid particle swarm optimizatjon,HPSO)求解无能力约束装配结构MLLS问题.通过实验验证了算法的可行性和有效性.     

改进粒子群优化算法在电源规划中的应用

电源规划是一类复杂、非线性组合优化问题.传统的方法随着规划期的延长,考虑因素的增多,难以有效的进行优化,在实际应用中作用有限.首先,对电源规划优化问题进行了建模.然后,对于粒子群(PSO)的迭代策略进行改进,在此基础上,运用遗传粒子群(GPHA)混合优化算法进行了优化尝试.考虑到电源规划中相关参数众多,在优化过程中引入了虚拟变量对电源规划中的问题进行了简化描述;GHPA算法的适应度评价函数设计中,运用了罚函数的思想,以提高算法优化的效果.最后本文使用某省实际负荷预测和系统负荷实际数据,进行了电源规划方案优化,得到了优化后的电源规划方案,并与普通的遗传算法、粒子群算法以及传统的动态规划算法得到的结果进行了比较.比较的结果显示出了本文提出的算法在优化结果和速度方面具有明显效果.     

Dynamic-objective particle swarm optimization for constrained optimization problems

This paper firstly presents a novel constraint-handling technique, called dynamic-objective method (DOM), based on the search mechanism of the particles of particle swarm optimization (PSO). DOM converts the constrained optimization problem into a bi-objective optimization problem, and then enables each particle to dynamically adjust its objectives according to its current position in the search space. Neither Pareto ranking nor user-defined parameters are involved in DOM. Secondly, a new PSO-based algorithm—restricted velocity PSO (RVPSO)—is proposed to specialize in solving constrained optimization problems. The performances of DOM and RVPSO are evaluated on 13 well-known benchmark functions, and comparisons with some other PSO algorithms are carried out. Experimental results show that DOM is remarkably efficient and effective, and RVPSO enhanced with DOM exhibits greater performance. In addition, besides the commonly used measures, we use histogram of the test results to evaluate the performance of the algorithms.This research is supported by National Natural Science Foundation of China (No. 10371028) and Scientific Research Fund of Southern Yangtze University (No. 0003182).     

基于改进PSO的综合运输网络管理多目标优化

首先,基于层次网络原则对综合运输网络管理优化问题进行了分析,以运输距离区分不同的网络层次,通过引入衔接时间,将同一层次上各种运输方式子网络的竞争关系,以及不同层次间各种运输方式子网络之间的协作关系模型化;其次,以网络运输强度和单位运量能源消耗最小化为目标,建立了综合运输网络管理的多目标优化数学模型;最后,提出了一种改进的多目标粒子群优化算法,得出了简化综合运输网络的Pareto最优前沿.案例计算结果表明,该算法能有效地找到分布均匀的多目标优化问题的Pareto前沿.     

竞争决策算法及其在车辆路径问题中的应用

在分析自然界各种竞争机制和人类社会决策原理的基础上,利用竞争造就优化和决策左右结果的特性,提出了一种能广泛应用于组合优化难题的新型算法———竞争决策算法(CDA),并给出了CDA的通用模型.车辆路径问题(VRP)是一个著名的NP难题,也是物流领域内一个重要的调度问题,利用CDA的通用模型设计了一个针对VRP的快速求解算法,并用该算法求解了VRP标准测试库中的实例,经过大量数据测试和验证,获得了令人满意的效果,其中部分问题的解优于目前公布的最好解.     

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.     

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.     

基于离散粒子群优化的轧辊热处理调度方法

以某轧辊企业铸钢分厂的轧辊热处理调度问题为实际背景,研究了两阶段及三阶段无等待混合流水车间调度问题.针对问题中工件加工无等待特点,设计了分阶段实现的无等待算法;在此基础上,结合离散粒子群优化算法对建立的整数规划模型进行优化求解.通过对真实数据仿真实验所得结果的比较与分析,验证了算法的可行性和有效性,并给出了具有实际参考价值的设备改进策略,对生产决策者合理安排生产具有一定的指导意义.     

A multi-objective particle swarm for a flow shop scheduling problem

Flow shop problems as a typical manufacturing challenge have gained wide attention in academic fields. In this paper, we consider a bi-criteria permutation flow shop scheduling problem, where weighted mean completion time and weighted mean tardiness are to be minimized simultaneously. Since a flow shop scheduling problem has been proved to be NP-hard in strong sense, an effective multi-objective particle swarm (MOPS), exploiting a new concept of the Ideal Point and a new approach to specify the superior particle's position vector in the swarm, is designed and used for finding locally Pareto-optimal frontier of the problem. To prove the efficiency of the proposed algorithm, various test problems are solved and the reliability of the proposed algorithm, based on some comparison metrics, is compared with a distinguished multi-objective genetic algorithm, i.e. SPEA-II. The computational results show that the proposed MOPS performs better than the genetic algorithm, especially for the large-sized problems.     

Algorithmic approaches to the multiple knapsack assignment problem

We consider a variant of the multiple knapsack problem in which some assignment-type side constraints have to be satisfied. The problem finds applications in logistics sectors related, e.g., to transportation and maritime shipping. We derive upper bounds from Lagrangian and surrogate relaxations of a mathematical model of the problem. We introduce a constructive heuristic and a metaheuristic refinement. We study the computational complexity of the proposed methods and evaluate their practical performance through extensive computational experiments on benchmarks from the literature and on new sets of randomly generated instances.     

Exact solution method to solve large scale integer quadratic multidimensional knapsack problems

In this paper we develop a branch-and-bound algorithm for solving a particular integer quadratic multi-knapsack problem. The problem we study is defined as the maximization of a concave separable quadratic objective function over a convex set of linear constraints and bounded integer variables. Our exact solution method is based on the computation of an upper bound and also includes pre-procedure techniques in order to reduce the problem size before starting the branch-and-bound process. We lead a numerical comparison between our method and three other existing algorithms. The approach we propose outperforms other procedures for large-scaled instances (up to 2000 variables and constraints). A extended abstract of this paper appeared in LNCS 4362, pp. 456–464, 2007.