基于混沌粒子群的资源受限项目调度问题

鉴于基本粒子群算法易陷入局部最优,提出一种将混沌算法嵌入基本粒子群的混沌粒子群算法,并将其用于求解典型的资源受限项目调度问题。采用基于优先值的粒子编码方式和串行调度方案,分别用基本粒子群算法和混沌粒子群算法对实例求解。并且比较了2种算法求解多资源受限项目调度问题的性能。结果表明:混沌粒子群算法在距最优值的平均偏差和达到最优值的次数百分比等性能上要优于基本的粒子群算法,并且混沌粒子群具有更好的收敛性。但是,混沌粒子群算法在计算达到最优工期的平均时间上略比基本粒子群算法逊色。     

基于DMPSO的单元集成设计方法

为避免单元设计中出现独立分步求解造成的解空间丢失,建立一个多目标生产单元集成布局优化模型,对单元构建与单元布局同时进行描述求解.模型考虑了设施的摆放方向,设施与单元之间的安全距离和设施空间尺寸.针对该多目标复杂问题,设计了基于结构化编码的动态多种群粒子群优化算法,避免粒子较快收敛陷入局部极值,提高了解的质量.同时采用个体密度值的外部档案维护策略,保持了解的多样性与均匀性.最后通过求解单元设计问题实例,验证模型和算法的有效性和可行性.     

模拟退火-二进制粒子群优化算法在配电网重构中的应用

将模拟退火算法与二进制粒子群算法相结合应用于配电网重构的优化算法既发挥了粒子群算法收敛速度快的特点,又因为引入的模拟退火算法具有的较强的跳出局部最优解能力,实现了有效地避免粒子群算法易陷入局部极值点的缺点,提高了进化后期算法的收敛速度和精度。实例中应用IEEE16节点系统的算例验证了模拟退火-二进制粒子群混合算法在配电网重构中的可行性和有效性。     

一种改进的雁群扩展粒子群算法研究

针对粒子群算法易陷入局部最优的问题,结合雁群启示粒子群算法和扩展粒子群算法提出了基于雁群启示的扩展粒子群(GeEPSO)算法。该算法在利用雁群飞行方向的多样性同时融合了所有粒子的个体极值信息,提高了种群多样性。为进一步提高改进算法的收敛速度,引入简化粒子群提出了 GeESPSO算法。基准函数的仿真表明:改进算法GeESPSO较好地平衡了收敛速度和局部最优两个矛盾,总体较优。为进一步验证算法在实际应用中的有效性,又分别用两种改进算法优化BP神经网络,并用相关气象数据对PM2.5的值进行预测。     

基于融合禁忌搜索粒子群算法的配电网无功优化

从数学角度分析,配电网无功优化是一个非线性、多变量、多约束的混合规划问题。粒子群优化搜索算法被广泛应用于求解配电网无功优化问题。由于粒子群算法粒子群在进化过程易趋向同一化,失去多样性,从而使算法陷入局部最优解。本文在分析配电网无功优化的特性基础上,提出一种改进的紧融合禁忌搜索-粒子群算法用于配电网无功优化问题的求解。通过将禁忌搜索功能融合到粒子历史最优解和全局最优解寻优过程中,避免了粒子群算法寻优过程中出现的局部最优问题,从而提高粒子群算法的全局搜索能力。通过IEEE14节点系统的仿真计算结果表明,改进的算法能取得良好的效果。     

用混合量子算法求解模糊柔性作业车间调度问题

针对模糊环境下柔性作业车间的调度问题,以最小化最大完工时间、最小化成本和最小化惩罚值为目标,建立调度问题数学模型,提出了混沌量子粒子群算法。针对实际生产交货期模糊的特点,在量子粒子群算法基础上,提出引入混沌机制建立初始群的方法;针对量子个体的更新,提出了改进的量子旋转角计算方法;针对种群可能局部早熟收敛和后期多样性丢失的问题,利用混沌机制的遍历性,提出混沌局部优化策略;通过四个经典的调度算例验证了所提出算法能降低早熟概率和提高迭代搜索效率,与其他算法比较可以获得更多的非支配解。     

基于MPSO的有限缓冲区多产品厂间歇调度问题的研究

研究了以最小化最大完工时间为目标的有限缓冲区多产品厂间歇调度问题,提出了一种基于多种群粒子群优化(MPSO)的间歇调度算法.该算法采用多种群,增加了种群初始粒子的多样性,在每一代子种群并行进化的过程中引入移民粒子,使子种群之间相互影响和促进,避免算法过早地陷入局部最优,提高了算法的全局搜索能力;每代进化后选出子种群中的优秀粒子作为精华种群,并对其进行变邻域搜索(VNS),进一步提高了算法的收敛精度.通过对不同规模调度问题的仿真,以及与其它算法的对比,证明了该算法解决有限缓冲区多产品厂间歇调度问题的有效性和优越性.     

基于混合粒子群算法的物流配送路径优化问题研究

针对物流配送路径优化问题,提出了一种融合Powell局部寻优算法和模拟退火算法的混合粒子群算法,以克服单用粒子群算法求解问题早熟收敛的不足,增加算法的开发能力,提高算法的全局搜索能力,并进行了实验计算.计算结果表明,用混合粒子群算法求解物流配送路径优化问题,可以在一定程度上提高粒子群算法在局部搜索能力和搜索全局最优解概率,从而得到质量较高的解.     

基于粒子群优化聚类的汽轮机组振动故障诊断

针对模糊C-均值聚类算法(FCM)容易陷入局部极值和对初始值敏感的不足,提出了一种新的模糊聚类算法(PFCM),新算法利用粒子群优化算法(PSO)全局寻优、快速收敛的特点,代替了FCM算法的基于梯度下降的迭代过程,使算法具有很强的全局搜索能力,很大程度上避免了FCM算法易陷入局部极值的缺陷,同时也降低了FCM算法对初始值的敏感度。将该算法应用于汽轮机组振动故障诊断中,与电厂运行实际故障状态对照,仿真结果表明该算法提高了故障诊断的正确率。为汽轮机振动故障诊断方法的研究提供了一种新的思路。     

免疫粒子群算法在混流装配线排序中的应用

混流装配线上的产品投产排序是影响装配线生产效率的重要因素.建立以最小化装配线总闲置—超载成本为优化目标的装配线排序模型,采用粒子群算法来解决混流装配线的投产排序问题.考虑到基本粒子群算法易陷入局部最优解的问题,引入免疫算法思想对其进行改进,根据抗体亲和性与浓度值的计算,及时进行粒子的替换以维持种群的多样性,防止粒子过早...     

A hybrid particle swarm based method for process planning optimisation

A process planning (PP) problem is defined as to determine a set of operation-methods (machine, tool, and set-up configuration) that can convert the given stock to the designed part. Essentially, the PP problem involves the simultaneous decision making of two tasks: operation-method selection and sequencing. This is a combinatorial optimisation problem and it is difficult to find the best solution in a reasonable amount of time. In this article, an optimisation approach based on particle swarm optimisation (PSO) is proposed to solve the PP problem. Due to the characteristic of discrete process planning solution space and the continuous nature of the original PSO, a novel solution representation scheme is introduced for the application of PSO in solving the PP problem. Moreover, two kinds of local search algorithms are incorporated and interweaved with PSO evolution to improve the best solution in each generation. The numerical experiments and analysis have demonstrated that the proposed algorithm is capable of gaining a good quality solution in an efficient way.     

A cloud theory-based particle swarm optimization for multiple decision maker vehicle routing problems with fuzzy random time windows

This article puts forward a cloud theory-based particle swarm optimization (CTPSO) algorithm for solving a variant of the vehicle routing problem, namely a multiple decision maker vehicle routing problem with fuzzy random time windows (MDVRPFRTW). A new mathematical model is developed for the proposed problem in which fuzzy random theory is used to describe the time windows and bi-level programming is applied to describe the relationship between the multiple decision makers. To solve the problem, a cloud theory-based particle swarm optimization (CTPSO) is proposed. More specifically, this approach makes improvements in initialization, inertia weight and particle updates to overcome the shortcomings of the basic particle swarm optimization (PSO). Parameter tests and results analysis are presented to highlight the performance of the optimization method, and comparison of the algorithm with the basic PSO and the genetic algorithm demonstrates its efficiency.     

基于改进PSO算法的结构损伤检测

结构的损伤检测常转化为求解约束优化问题,针对粒子群算法容易出现早熟问题,增大算法后期的粒子位置的改变量,从而增加粒子位置的差异,因而能够增强其在求解约束优化问题时抵抗局部极小的能力。两层刚架单损伤和多损伤识别的数值结果和收敛曲线表明了改进后的粒子群算法优于传统的带惯性因子的粒子群算法。三层框架结构的4种损伤工况的试验研究进一步说明了该算法应用于结构损伤检测领域的有效性。     

A particle swarm optimisation for the no-wait flow shop problem with due date constraints

This paper considers the no-wait flow shop scheduling problem with due date constraints. In the no-wait flow shop problem, waiting time is not allowed between successive operations of jobs. Moreover, a due date is associated with the completion of each job. The considered objective function is makespan. This problem is proved to be strongly NP-Hard. In this paper, a particle swarm optimisation (PSO) is developed to deal with the problem. Moreover, the effect of some dispatching rules for generating initial solutions are studied. A Taguchi-based design of experience approach has been followed to determine the effect of the different values of the parameters on the performance of the algorithm. To evaluate the performance of the proposed PSO, a large number of benchmark problems are selected from the literature and solved with different due date and penalty settings. Computational results confirm that the proposed PSO is efficient and competitive; the developed framework is able to improve many of the best-known solutions of the test problems available in the literature.     

A particle swarm‐optimized support vector machine for reliability prediction

System reliability depends on inherent mechanical and structural aging factors as well as on operational and environmental conditions, which could enhance (or smoothen) such factors. In practice, the involved dependences may burden the modeling of the reliability behavior over time, in which traditional stochastic modeling approaches may likely fail. Empirical prediction methods, such as support vector machines (SVMs), become a valid alternative whenever reliable time series data are available. However, the prediction performance of SVMs depends on the setting of a number of parameters that influence the effectiveness of the training stage during which the SVMs are constructed based on the available data set. The problem of choosing the most suitable values for the SVM parameters can be framed in terms of an optimization problem aimed at minimizing a prediction error. In this work, this problem is solved by particle swarm optimization (PSO), a probabilistic approach based on an analogy with the collective motion of biological organisms. SVM in liaison with PSO is then applied to tackle reliability prediction problems based on time series data of engineered components. Comparisons of the obtained results with those given by other time series techniques indicate that the PSO + SVM model is able to provide reliability predictions with comparable or great accuracy. Copyright © 2011 John Wiley & Sons, Ltd.     

工程项目多目标协同优化研究

将微粒群算法(particle swarm optimization,PSO)引入工程项目多目标协同优化领域,研究工程项目的质量、费用、资源和工期的协同优化问题。文章首先系统介绍微粒群算法原理、流程以及算法的改进发展,然后研究了工程项目质量、费用、工期和资源的协调功效系数,并建立了质量、费用、工期和资源的多目标协同优化模型,接下来介绍了应用微粒群算法编码解决工程项目多目标优化的方法步骤。最后,通过一个应用实例,计算表明微粒群算法可以准确快速地解决工程项目多目标协同优化问题。     

Automatic clustering for generalised cell formation using a hybrid particle swarm optimisation

This paper considers the cell formation (CF) problem in which parts have alternative process routings and the number of machine cells is not known a priori. Very few studies address these two practical issues at the same time. This paper proposes an automatic clustering approach based on a hybrid particle swarm optimisation (PSO) algorithm that can automatically evolve the number and cluster centres of machine cells for a generalised CF problem. In the proposed approach, a solution representation, comprising an integer number and a set of real numbers, is adopted to encode the number of cells and machine cluster centres, respectively. Besides, a discrete PSO algorithm is utilised to search for the number of machine cells, and a continuous PSO algorithm is employed to perform machine clustering. Effectiveness of the proposed approach has been demonstrated for test problems selected from the literature and those generated in this study. The experimental results indicate that the proposed approach is capable of solving the generalised machine CF problem without predetermination of the number of cells.     

Application of the inverse elasticity problem to identify irregular interfacial configurations

An inverse elasticity problem is solved to identify the irregular boundary between the components of a multiple connected domain using displacement measurements obtained from an uniaxial tension test. The boundary elements method (BEM) coupled with the particle swarm optimization (PSO) and conjugate gradient method (CGM) are employed. Due to the ill-posed nature of this inverse elasticity problem, and the need for an initial guess of the unknown interfacial boundary when local optimization methods are implemented, a Meta heuristic procedure based on the PSO algorithm is presented. The CGM is then employed using the best initial guess obtained by the PSO to reach convergence. This procedure is highly effective, since the computational time reduces considerably and accuracy of the results is reasonable. Several example problems are solved and the accuracy of obtained results is discussed. The influence of material properties and the effect of measurement errors on the estimation process are also addressed.     

Parallel particle swarm optimization on a graphics processing unit with application to trajectory optimization

In order to reduce the computational time, a fully parallel implementation of the particle swarm optimization (PSO) algorithm on a graphics processing unit (GPU) is presented. Instead of being executed on the central processing unit (CPU) sequentially, PSO is executed in parallel via the GPU on the compute unified device architecture (CUDA) platform. The processes of fitness evaluation, updating of velocity and position of all particles are all parallelized and introduced in detail. Comparative studies on the optimization of four benchmark functions and a trajectory optimization problem are conducted by running PSO on the GPU (GPU-PSO) and CPU (CPU-PSO). The impact of design dimension, number of particles and size of the thread-block in the GPU and their interactions on the computational time is investigated. The results show that the computational time of the developed GPU-PSO is much shorter than that of CPU-PSO, with comparable accuracy, which demonstrates the remarkable speed-up capability of GPU-PSO.