二次分配问题的粒子群算法求解

文章采用了一种新的算法,即粒子群算法(PSO)去解决二次分配问题(QAP),构造了该问题的粒子表达方法,建立了此问题的粒子群算法模型,并对不同的二次分配问题算例进行了实验,结果表明:粒子群算法可以快速、有效地求得二次分配问题的优化解,是求解二次分配问题的一个较好方案。PSO算法在很多连续优化问题中已经得到较成功的应用,而在离散域上的研究和应用还很少。文章应用PSO算法解决QAP问题是一种崭新的尝试,它对于将PSO算法应用于离散问题,特别是组合优化问题无疑具有启发性,并为进一步深入研究奠定了基础。     

PSO算法在多约束染色工艺优化模型中的应用

针对染色工艺优化设计,存在周期长、成本高、无法精确定量的问题,以生产成本最小化为优化目标,构造染色工艺优化设计的数学模型。从模型可知,染色工艺优化问题是一个具有大量局部极小值、不连续、多变量、多约束的复杂优化问题。粒子群(PSO)算法是一种基于群体智能的启发式算法。它具有简单易行、收敛速度快、优化效率高、对种群规模不十分敏感、鲁棒性好等特点,能方便地被用于求解带离散变量、不连续、多变量、多约束、非线性的复杂优化问题中。因而,提出粒子群算法来求解染色工艺优化模型。考虑到粒子群算法(PSO)易陷入局部最优解的局限性,提出一种基于改进惯性权重粒子群算法。该方法通过引进指数因子改进标准粒子群算法的惯性权重,平衡了其全局和局部搜索能力,在速度和精度上满足了计算要求。仿真结果表明,在满足实际生产要求的条件下,该方法优化后的生产成本节约了25%。证明该优化模型及算法是一种可行而有效的方法,对生产成本的预测以及染色工艺参数的制定具有指导意义。     

动态邻域混合粒子群优化算法

粒子群优化(PSO)算法对于多峰搜索问题一直存在早熟收敛问题。为在增强PSO算法全局搜索能力的同时提高收敛速度,提出一种动态邻域混合粒子群优化算法DNH_PSO,采用PSO局部模型,将随机拓扑和冯诺依曼拓扑相结合形成动态邻域,提高算法的全局搜索能力,为增强算法的局部搜索能力并加快收敛速度,使用粒子邻域全面学习策略,将拟牛顿法引入算法中。与其他PSO实验对比分析表明,该算法对于多峰搜索问题具有较好的全局收敛性。     

粒子群优化的时频联合资源分配算法

针对航空通信环境中正交频分多址系统的资源分配问题,在信道资源有限的约束条件下,以最大化用户节点的效用总和为目标,提出了一种基于粒子群优化(PSO)的时频联合资源分配算法.该算法采用离散变量来编码粒子位置,并针对离散空间构建新的基于概率信息的粒子速度和位置更新算法.仿真结果表明:所提出的资源分配算法在效用总和、公平性等方面优于现有资源分配算法.     

基于粒子群优化的军事物流配送中心选址

针对当前军事物流配送改革中配送中心选址问题,在成本最小的基础上,构建了一个混合整数规划模型,并将粒子群优化算法(PSO)引入到模型的求解中,采用离散PSO解决物流配送中心选择问题,用基本PSO解决货物运输分配问题,通过嵌套调用离散PSO和基本PSO,得到模型最优解.该方法降低了计算复杂度,有效选择了物流配送中心,优化了军事物流网络.实例表明了方法的可行性和有效性.     

多量子粒子群协同优化算法研究

针对标准粒子群优化（PSO）算法及其改进算法存在的局部收敛与收敛速度问题,提出了一种多量子粒子群协同优化（QPSCO）方法。该算法采用双层的多粒子群协同优化结构：用多个量子粒子群在底层独立地搜索解空间,同时引入参数变异策略,以扩大搜索范围;上层用1个量子粒子群追逐当前全局最优解,并对飞离搜索区域粒子的位置用新位置取代,以加快算法收敛。在此基础上,将该算法应用于实际控制系统低阶时滞对象的PID控制器设计中。仿真结果表明,QPSCO是一种有效的参数优化算法,与标准PSO、QPSO等算法相比具有更好的全局收敛性能。     

动态离散粒子群优化算法

为解决现实世界中动态环境下的离散事件优化问题,研究了当前已被广泛应用于动态环境或离散运算优化问题的粒子群优化算法(PSO),据此提出了一种动态离散PSO算法.该算法设计了一种环境绝对值和环境敏感性判定策略来实现动态环境的监测与响应,并通过带变异算子的离散PSO算法公式的重新定义来满足大规模离散运算需求.最后,利用离散时间系统的零状态响应求解评价了该算法的性能,结果表明,该算法在定义域内具有较好的收敛性.     

基于分层自主学习的改进粒子群优化算法

针对粒子群优化（PSO）算法容易陷入局部最优、收敛精度不高、收敛速度较慢的问题，提出一种基于分层自主学习的改进粒子群优化（HCPSO）算法。首先，根据粒子适应度值和迭代次数将种群动态地划分为三个不同阶层；然后，根据不同阶层粒子特性，分别采用局部学习模型、标准学习模型以及全局学习模型，增加粒子多样性，反映出个体差异的认知对算法性能的影响，提高算法的收敛速度和收敛精度；最后，将HCPSO算法与PSO算法、自适应多子群粒子群优化（PSO-SMS）算法以及动态多子群粒子群优化（DMS-PSO）算法分别在6个典型的测试函数上进行对比仿真实验。仿真结果表明，HCPSO算法的收敛速度和收敛精度相对给出的对比算法均有明显提升，并且算法执行时间和基本PSO算法执行时间差距在0.001量级内，在不增加算法复杂度的情况下算法性能更高。     

求解整数非线性规划结合正交杂交的离散PSO 算法

针对整数非线性规划问题,提出一种结合正交杂交的离散粒子群优化(PSO)算法.首先采用舍入取整方法,为了减少舍入误差,对PSO中的每个粒子到目前为止的最好位置进行随机修正,将基于正交实验设计的正交杂交算子引入离散PSO算法,以增强搜索性能;然后对PSO算法中的惯性权重和收缩因子采用动态调整策略,以提高算法的搜索效率;最后对一些不同维数的整数非线性规划问题进行数值仿真实验,实验结果表明了所提出算法的有效性.     

粒子群优化算法研究

粒子群优化算法是根据鸟群觅食过程中的迁徙和群集模型而提出的,用于解决优化问题的一类新兴的随机优化算法。本文首先介绍PSO算法的基本原理和工作机制;然后介绍粒子群优化算法的优化策略,包括提高收敛速度﹑算法离散化﹑提高总群多样性;最后对其将来的发展进行了展望。     

基于实例模板的产品杂交配置设计

产品杂交配置设计是产品智能与创新设计方法的研究热点。基于产品模板配置设计概念,结合产品杂交配置设计需求,定义了面向杂交配置设计的知识库模型,提出了基于实例模板的机械产品杂交配置总体框架,给出了需求驱动、知识制导的产品杂交配置设计算法。该方法提供了统一的模型和框架,可有效支持各类机械产品杂交配置设计系统的研究和开发。     

预制构件并行生产线资源配置与生产调度集成优化

并行生产线和特定工序生产资源共享模式可以显著改善客户满意度并节约成本.针对预制构件并行生产线资源配置与生产调度集成优化问题,基于分解策略和交替迭代优化思想,提出一种交替式混合果蝇-禁忌搜索算法(AHFOA＿TS)以最小化拖期惩罚费用.首先,通过快速启发式方法产生一较好初始解;然后,固定资源配置方案,为提高算法局部搜索能力,通过集成多种局部搜索方式,设计一种离散果蝇优化算法优化订单指派及调度方案;最后,固定订单指派及调度方案,为减少无效搜索次数,设计一种基于双层变异算子和精英劣解交叉策略的混合禁忌搜索算法以优化资源配置方案,如此两个阶段交替运行直至满足终止条件.此外,设计4种基于交替搜索框架的智能优化算法用于比较.计算结果表明, AHFOA＿TS算法能够更有效求解预制构件生产线资源配置和生产调度集成优化问题.     

An operator allocation optimization model for balancing control of the hybrid assembly lines using Pareto utility discrete differential evolution algorithm

This paper investigates the operator allocation problems (OAP) with jobs sharing and operator revisiting for balance control of a complicated hybrid assembly line which appears in the apparel sewing manufacturing system. Multiple objectives and constraints for the problem are formulated. The utility function is employed to deal with the difficulty of combining several conflicting and incommensurable objectives into one overall measure. An optimization model combining the Pareto utility discrete differential evolution (PUDDE) algorithm and the embedded discrete event simulation (DES) model is proposed to solve the OAPs. The PUDDE algorithm is an improved discrete differential evolution approach used with the Pareto utility selection strategy, which extends the real-value differential evolution to handle the discrete-value vector by introducing two modified operators, namely the subtraction and addition operators. During the optimization process, the embedded DES model is used to evaluate the performance objectives by analyzing the dynamic behaviors of the hybrid assembly lines, which tackles the problem of having no closed-form mathematical expressions for the evaluation of performance objectives owing to the existence of jobs sharing and operator revisiting. Extensive experiments are conducted to validate the proposed optimization model. The experimental results demonstrate that the proposed PUDDE-based optimization model can effectively solve the OAPs for the hybrid assembly lines with the consideration of jobs sharing and operator revisiting. It was also found that the proposed PUDDE algorithm evidently outperforms the general differential evolution algorithm. Compared with the collected industrial results, the solution generated by the proposed optimization model has much better performance objectives for the hybrid assembly lines.     

可重构混合任务调度算法

隐藏和减少配置时间是可重构任务调度的关键问题。针对同时存在相关联的软、硬件任务的可重构混合任务,提出一种可重构混合任务调度算法。通过预配置策略和优先级算法确定需要预先配置的任务及其预配置顺序,将后继任务的配置过程隐藏在前驱任务的运行时间中,并采用配置重用策略,减少相同任务的配置次数。实验仿真结果表明,同已有的算法相比,该调度算法调度效果明显,减少了可重构任务调度的整体开销。     

A hybrid fuzzy-GA algorithm for the integrated machine allocation problem with fuzzy demands

The integrated machine allocation and facility layout problem (IMALP) is a branch of the general facility layout problem in which, besides selecting machine locations, the processing route of each product is determined. Most research in this area suppose that the flow of material is certain and exact, which is an unrealistic assumption in today's dynamic and uncertain business environment. Therefore, in this paper the demand volume has been assumed as fuzzy numbers with different membership functions. To solve this problem, the deterministic model is first integrated with a fuzzy implication via the expected value model, and thereafter an intelligent hybrid algorithm, including a genetic algorithm and a fuzzy simulation approach has been applied. Finally, the efficiency of the proposed algorithm is evaluated with a set of numerical examples. The results show the effectiveness of the hybrid algorithm in finding the IMALP solutions.     

Hybrid Fuzzy Modelling for Model Predictive Control

Model predictive control (MPC) has become an important area of research and is also an approach that has been successfully used in many industrial applications. In order to implement a MPC algorithm, a model of the process we are dealing with is needed. Due to the complex hybrid and nonlinear nature of many industrial processes, obtaining a suitable model is often a difficult task. In this paper a hybrid fuzzy modelling approach with a compact formulation is introduced. The hybrid system hierarchy is explained and the Takagi–Sugeno fuzzy formulation for the hybrid fuzzy modelling purposes is presented. An efficient method for identifying the hybrid fuzzy model is also proposed. A MPC algorithm suitable for systems with discrete inputs is treated. The benefits of the MPC algorithm employing the hybrid fuzzy model are verified on a batch-reactor simulation example: a comparison between the proposed modern intelligent (fuzzy) approach and a classic (linear) approach was made. It was established that the MPC algorithm employing the proposed hybrid fuzzy model clearly outperforms the approach where a hybrid linear model is used, which justifies the usability of the hybrid fuzzy model. The hybrid fuzzy formulation introduces a powerful model that can faithfully represent hybrid and nonlinear dynamics of systems met in industrial practice, therefore, this approach demonstrates a significant advantage for MPC resulting in a better control performance.     

Utilizing variant differentiation to mitigate manufacturing complexity in mixed-model assembly systems

A measure of product variety induced complexity has been proposed for mixed-model assembly systems with serial, parallel and hybrid configurations. The complexity model was built based on the assumption of identical parallel stations, i.e., same product variants are produced at all parallel stations in the same volume and with the same mix ratio. In this paper, the existing complexity model is extended to general mixed-model assembly systems with non-identical parallel stations in the presence of product variety. Then it is discussed that how to reduce the system complexity using the variant differentiation, based on which a mathematical formulation is developed to minimize the complexity of a mixed-model assembly system. The formulated problem is a non-linear programming problem and then solved by genetic algorithm. Last the developed complexity mitigation model is applied to the configuration selection of assembly systems, i.e., to identify the system configuration with the minimum complexity.     

Fuzzy multilevel programming with a hybrid intelligent algorithm

In order to model fuzzy decentralized decision-making problem, fuzzy expected value multilevel programming and chance-constrained multilevel programming are introduced. Furthermore, fuzzy simulation, neural network, and genetic algorithm are integrated to produce a hybrid intelligent algorithm for finding the Stackelberg-Nash equilibrium. Finally, two numerical examples are provided to illustrate the effectiveness of the hybrid intelligent algorithm.     

基于混合离散免疫算法的轧制计划编排

为了保证轧制调度计划的可行性，提高排程的效率，根据热轧生产模式和轧制计划的特点，利用车辆路径问题模型来建模轧制调度问题，并用一种基于离散人工免疫算法的混合优化算法来解决这一问题。该方法利用离散人工免疫算法的全局搜索能力来寻找全局最优解，利用模拟退火方法来避免陷入局部最优．对某钢厂实际生产数据仿真结果表明，所提出的模型和算法对于求解热轧调度问题具有可行性和高效性。     

基于混合离散二进制粒子群-遗传算法的测试配置方法研究

针对目前测试性建模工作中尚无具体方法指导测试配置这一问题,通过对系统内故障传播关系进行分析,提出了一种混合离散二进制粒子群-遗传算法用于求解测试配置的最优方案,使系统的测试性模型在满足规定测试性指标下使用的测试数量最少。将系统测试配置方案进行二进制粒子编码,并在粒子群算法中引入遗传算子,使混合算法具有较快的搜索速度的同时避免陷入局部最优。最后通过实例计算与仿真,证明所提出算法计算结果正确且对于指导复杂系统测试性建模工作具有实际应用价值。