置换流水车间调度问题的中心引力优化算法求解

目前求解置换流水车间调度问题的智能优化算法都是随机型优化方法,存在的一个问题是解的稳定性较差。针对该问题,本文给出一种确定型智能优化算法——中心引力优化算法的求解方法。为处理基本中心引力优化算法对初始解选择要求高的问题,利用低偏差序列生成初始解,提高初始解质量;利用加速度和位置迭代方程更新解的状态;利用两位置交换排序法进行局部搜索,提高算法的优化性能。采用置换流水车间调度问题标准测试算例进行数值实验,并和基本中心引力优化算法、NEH启发式算法、微粒群优化算法和萤火虫算法进行比较。结果表明该算法不仅具有更好的解的稳定性,而且具有更高的计算精度,为置换流水车间调度问题的求解提供了一种可行有效的方法。     

面向靠桥率及道口冲突率的航班-机位指派问题优化模型及其启发式算法研究

在给定航班时刻表条件下,对于进出港航班的机位分配,除了必须满足航班、飞机和机位之间的技术性要求之外,还要考虑尽量提高整个机场的机位利用率,且方便旅客出入港及时、安全和便捷.文章以飞机机型、所属航空公司、客运/货运航班、国内/国际航班等匹配条件为约束条件,以航班-机位分配完成率、靠桥率、道口非冲突率为目标,建立了一个航班-机位指派问题的全局优化模型.基于国内某机场的真实应用场景及其待决策变量维度的超大规模,导致模型求解成为一个NP-COMPLETE的混合整数规划问题.文章提出一种启发式快速求解算法,基于贪婪规则建立若干优先级队列的航班冲突调整方案,按照3个指标重要程度渐次探求近似最优解.而且,对于每一步贪婪规则的改进,文章都进行了算法有效性检验以及计算性能的对比实验.最终多重对比实验的结果表明,新算法的结果在与理论最优解差距不足3%的代价下,可节约超过90%求解时间.     

复杂供水系统优化调度的数学模型与求解方法研究

从供水管网系统整体优化和最小供水费用的角度出发,结合复杂供水系统的生产调度特点,建立了复杂供水系统整体优化调度的数学模型.利用多维编码的遗传算法求解数学模型,对其编码规则、染色体的评估以及遗传操作进行了深入的研究.并给出了详细的求解步骤.采用Matlab软件编写了基于遗传算法的优化调度程序.对某市供水管网系统的生产调度进行了仿真研究.仿真结果表明,应用遗传算法求解管网系统优化调度,可以得到问题的全局最优解,并将调度结果和自来水公司实际生产情况比较,采用优化调度程序进行生产调度可以节省运行费用.     

一种求解延迟工件数最小的混合流水车间调度问题的模拟退火算法

针对延迟工件数最小的混合流水车间调度问题,给出了一种改进的模拟退火求解算法. 该算法首先给出一个启发式算法来获得初始解,然后用模拟退火算法对初始解改进. 通过交换工件在第一阶段的排序来获得一个新的解,采用最先空闲设备分配规则和先到先被加工规则,对工件在剩余各级的工序进行调度. 实验仿真表明算法是可行有效的.     

求解柔性作业车间调度的动态群智能优化算法

针对柔性作业车间调度问题,提出一种新型两阶段动态混合群智能优化算法.算法初始阶段采用动态邻域的协同粒子群进行粗搜索,第二阶段提出了基于混沌算子的蜂群进行细搜索,既增强了种群多样性,又提高了算法搜索精度,实现了全局搜索与局部搜索能力的有效平衡.针对柔性作业车间调度问题特点,采用独特的编码方式和位置更新策略来避免不合法解的产生.最后将此算法在不同规模的实例上进行了仿真测试,并与最近提出的其他几种具有代表性的算法进行了比较,验证了算法的有效性和优越性.     

非线性0-1规划问题的元胞灰狼优化算法

针对非线性0-1规划求解问题,基于元胞自动机原理和改进的灰狼算法,提出一种元胞灰狼优化算法.首先,为了避免基本灰狼算法种群分布的随机性问题,利用佳点集理论对灰狼种群进行初始化,增强算法种群的多样性,提高算法的全局收敛速度;其次,针对基本灰狼算法的开发和探索能力平衡能力差的问题,利用自适应精英学习策略分别对算法中的参数α、灰狼与猎物的距离进行修正,实现灰狼算法的全局搜索速度和开发探索能力的最优均衡性;最后,将元胞自动机的演化规则与次优解β灰狼位置以及第三优解δ灰狼位置进行更新,利用元胞及其邻居增强搜索过程的多样性和分布性,实现灰狼算法的全局优化能力;并选用14个典型的非线性0-1规划问题算例进行仿真解算,并将解算结果与其它算法进行比较,结果表明:该算法对大规模复杂问题求解的平均运行时间少10%左右,且具有较快的收敛速度、较多的最优解集和较好的全局寻优能力.     

多因素指派模型全局优化问题研究

基于多因素资源优化分配问题的不确定性,建立基于区间数型下的不确定多因素指派模型,给出模型建立的理论依据与全局优化算法,拓展区间数型多因素指派模型,解决了不确定条件下多因素资源优化分配问题.考虑多因素影响,基于任务完成效率,以5类任务多因素分配问题为例,获得了指派模型全局优化的解.为不确定条件下资源优化分配问题的研究拓宽了决策途径.     

基于自适应子种群和动态反向学习的改进鸡群算法

针对鸡群算法(Chicken swarm optimization,CSO)求解复杂高维问题收敛精度低、容易陷入局部极值等问题,提出了一种基于自适应子种群和动态反向学习的改进鸡群(ICSO)算法.根据鸡群算法迭代进化进程,自适应确定公鸡种群规模大小,并据此将母鸡种群和小鸡分成若干个子种群;设计进化停滞判定机制,并引入动态反向学习因子以改进算法个体更新方式,有效保持鸡群样本多样性和算法全局深度搜索能力.典型测试函数仿真实验结果表明,与SFLA算法、PSO等智能优化算法相比,ICSO算法具有更高的收敛精度和更优的复杂函数优化能力.     

兼顾公平与效率的云计算并行机资源调度研究

研究在云计算中服务资源优化管理背景下,基于时间窗口的非等同并行机服务资源调度问题.为达到最大任务处理数,选取任务延误时间作为目标函数建立数学模型,并利用蚁群算法为模型求解.设计了算法的各项参数,而且进一步探讨了如何将资源分配的公平性引入到算法中来.还通过仿真算例对比了考虑公平性要素前后的调度结果.从结果来看,提出的模型和算法能够较好的用于解决云计算中的并行机资源调度问题,并以较快的收敛速度找到满足约束条件的较优解.     

多目标0-1规划问题的元胞狼群优化算法研究

针对多目标0-1规划问题,首先基于元胞自动机原理和人工狼群智能算法,提出一种元胞狼群优化算法,该算法将元胞机的演化规则与嚎叫信息素更新规则、人工狼群更新规则进行组合,采用元胞及其邻居来增强搜索过程的多样性和分布性,使人工头狼在元胞空间搜索的过程中,增强了人工狼群算法的全局搜索能力,并获得更多的全局非劣解;其次结合多目标0-1规划模型对元胞狼群算法进行了详细的数学描述,定义了人工狼群搜索空间、移动算子、元胞演化规则和非劣解集更新规则,并给出了元胞狼群算法的具体实现步骤;最后通过MATLAB软件对3个典型的多目标0-1规划问题算例进行解算,并将解算结果与其它人工智能算法的结果进行比较,结果表明:元胞狼群算法在多目标0-1规划问题求解方面可获得更多的非劣解集和更优的非劣解,并具有较快的收敛速度和较好的全局寻优能力。     

求解多维0-1背包问题的人工鱼群算法

对于多维0-1背包问题,国内外学者提出了诸如模拟退火、遗传算法、蚁群算法以及其他启发式算法.给出一种新的智能寻优方法——人工鱼群算法.算法通过各人工鱼的局部寻优,从而在群体中体现出全局最优.描述了人工鱼群算法的具体步骤并编程实现,通过多维背包算例进行了求解测试,获得了满意的效果.     

Subset simulation for unconstrained global optimization

Global optimization problem is known to be challenging, for which it is difficult to have an algorithm that performs uniformly efficient for all problems. Stochastic optimization algorithms are suitable for these problems, which are inspired by natural phenomena, such as metal annealing, social behavior of animals, etc. In this paper, subset simulation, which is originally a reliability analysis method, is modified to solve unconstrained global optimization problems by introducing artificial probabilistic assumptions on design variables. The basic idea is to deal with the global optimization problems in the context of reliability analysis. By randomizing the design variables, the objective function maps the multi-dimensional design variable space into a one-dimensional random variable. Although the objective function itself may have many local optima, its cumulative distribution function has only one maximum at its tail, as it is a monotonic, non-decreasing, right-continuous function. It turns out that the searching process of optimal solution(s) of a global optimization problem is equivalent to exploring the process of the tail distribution in a reliability problem. The proposed algorithm is illustrated by two groups of benchmark test problems. The first group is carried out for parametric study and the second group focuses on the statistical performance.     

Solving air traffic conflict problems via local continuous optimization

This paper first introduces an original trajectory model using B-splines and a new semi-infinite programming formulation of the separation constraint involved in air traffic conflict problems. A new continuous optimization formulation of the tactical conflict-resolution problem is then proposed. It involves very few optimization variables in that one needs only one optimization variable to determine each aircraft trajectory. Encouraging numerical experiments show that this approach is viable on realistic test problems. Not only does one not need to rely on the traditional, discretized, combinatorial optimization approaches to this problem, but, moreover, local continuous optimization methods, which require relatively fewer iterations and thereby fewer costly function evaluations, are shown to improve the performance of the overall global optimization of this non-convex problem.     

Primal and dual algorithms for optimization over the efficient set

ABSTRACT

Optimization over the efficient set of a multi-objective optimization problem is a mathematical model for the problem of selecting a most preferred solution that arises in multiple criteria decision-making to account for trade-offs between objectives within the set of efficient solutions. In this paper, we consider a particular case of this problem, namely that of optimizing a linear function over the image of the efficient set in objective space of a convex multi-objective optimization problem. We present both primal and dual algorithms for this task. The algorithms are based on recent algorithms for solving convex multi-objective optimization problems in objective space with suitable modifications to exploit specific properties of the problem of optimization over the efficient set. We first present the algorithms for the case that the underlying problem is a multi-objective linear programme. We then extend them to be able to solve problems with an underlying convex multi-objective optimization problem. We compare the new algorithms with several state of the art algorithms from the literature on a set of randomly generated instances to demonstrate that they are considerably faster than the competitors.     

No-arbitrage conditions,scenario trees,and multi-asset financial optimization

Many numerical optimization methods use scenario trees as a discrete approximation for the true (multi-dimensional) probability distributions of the problem’s random variables. Realistic specifications in financial optimization models can lead to tree sizes that quickly become computationally intractable. In this paper we focus on the two main approaches proposed in the literature to deal with this problem: scenario reduction and state aggregation. We first state necessary conditions for the node structure of a tree to rule out arbitrage. However, currently available scenario reduction algorithms do not take these conditions explicitly into account. State aggregation excludes arbitrage opportunities by relying on the risk-neutral measure. This is, however, only appropriate for pricing purposes but not for optimization. Both limitations are illustrated by numerical examples. We conclude that neither of these methods is suitable to solve financial optimization models in asset–liability or portfolio management.     

The statement and numerical solution of an optimization problem in X-ray tomography

A nonclassical problem is considered for the transport equation with coefficients depending on the energy of radiation. The task is to find the discontinuity surfaces for the coefficients of the equation from measurements of the radiation flux leaving the medium. For this tomography problem, an optimization problem is stated and numerically analyzed. The latter consists in determining the radiation energy that ensures the best reconstruction of the unknown medium. A simplified optimization problem is solved analytically.     

Annealing-tabu PAES: a multi-objective hybrid meta-heuristic

Most real-life optimization problems require taking into account not one, but multiple objectives simultaneously. In most cases these objectives are in conflict, i.e. the improvement of some objectives implies the deterioration of others. In single-objective optimization there exists a global optimum, while in the multi-objective case no optimal solution is clearly defined, but rather a set of solutions. In the last decade most papers dealing with multi-objective optimization use the concept of Pareto-optimality. The goal of Pareto-based multi-objective strategies is to generate a front (set) of non-dominated solutions as an approximation to the true Pareto-optimal front. However, this front is unknown for problems with large and highly complex search spaces, which is why meta-heuristic methods have become important tools for solving this kind of problem. Hybridization in the multi-objective context is nowadays an open research area. This article presents a novel extension of the well-known Pareto archived evolution strategy (PAES) which combines simulated annealing and tabu search. Experiments on several mathematical problems show that this hybridization allows an improvement in the quality of the non-dominated solutions in comparison with PAES, and also with its extension M-PAES.     

基于动力系统的线性不等式组的解法

本文提出了一种新的求解线性不等式组可行解的方法-基于动力系统的方法．假设线性不等式组的可行域为非空,在可行域的相对内域上建立一个非线性关系表达式,进而得到一个结构简单的动力系统模型．同时,定义了穿越方向。文章最后的数值实验结果表明此算法是有效的．     

基于时间约束的生产过程优化问题研究

本文提出一个实际的生产过程优化问题:基于时间约束的生产过程优化问题。客户要求企业在规定时间内完成指定批量工件的生产任务,该问题便是从中引出的。该问题的目标是在满足生产时间的条件下最小化总生产成本。本文为该问题建立了整数规划模型。然后以某厂工作缸生产过程为例,采用数学规划软件Cplex9.0求解模型。     

Parameter estimation of chaotic system with time-delay: A differential evolution approach

It is an important issue to estimate parameters of chaotic system in nonlinear science. In this paper, parameter estimation problem of chaotic system with time-delay is considered. Parameters and time-delay are estimated together by treating time-delay as an additional parameter. The parameter estimation problem is converted to an multi-dimensional optimization problem. A differential evolution (DE) algorithm, which possess a powerful searching capability for finding the solutions for a given optimization problem, is applied to solve this optimization problem. Two illustrative examples are given to verify the effectiveness of the proposed method.