一种全终端网络可靠性多目标优化模型及求解

分析了全终端网络可靠性设计问题,针对单目标优化存在的不足,建立了一种更加贴近工程实际的极大化可靠度,同时极小化投资成本的多目标优化模型,并利用模拟退火算法对其进行了优化求解。计算机仿真实例表明：模拟退火算法在求解此问题时,无论是在Pareto解的数量上还是在Pareto解的范围上都能得到较好的优化效果。     

多目标资源优化分配问题的Memetic 算法

针对传统算法求解多目标资源优化分配问题收敛慢、Pareto解不能有效分布在Pareto 前沿面的问题, 提出一种新的Memetic 算法. 在遗传算法的交叉算子中引入模拟退火算法, 加强了遗传算法的局部搜索能力, 加快了收敛速度. 为了使Pareto 最优解均匀分布在Pareto 前沿面, 在染色体编码中引入禁忌表, 增加了种群的多样性, 避免了传统遗传算法后期Pareto 解集过于集中的缺点. 通过与已有的遗传算法、蚁群算法、粒子群算法进行比较, 仿真实验表明了所提出算法的有效性, 并分析了禁忌表长度和模拟退火参数对算法收敛性的影响.

     

多目标优化的一类模拟退火算法

多目标优化是运筹学中的重要研究课题,但迄今仍缺少高效的优化技术。通过对搜索操作和参数的合理设置,提出了一类求解多目标优化问题Pareto最优解的高效模拟退火算法。基于典型算例的数值仿真验证了算法的有效性。     

多蚁群分级优化的多目标求解方法*

为提高多目标优化方法的求解性能,在给出了蚁群算法优化函数类问题求解方法的基础上,提出了基于多蚁群分级优化多目标问题的求解方法。构建了子蚁群以自身启发式信息及以其他子群的启发式信息获得准Pareto解以及采用各子群的每一只蚂蚁获得的准Pareto解作支配判断,从而提高Pareto解的多样性;构建了父蚁群以准Pareto解作为空间节点构成TSP类似的组合优化问题,其求解结果以获得多目标优化问题的Pareto解的前沿,从而提高Pareto解的均匀分布性。通过优化实例验证,结果表明,多蚁群分级优化的多目标求解方法     

模糊作业时间的拆卸线平衡Pareto多目标优化

针对实际拆卸作业的复杂性,建立了考虑模糊作业时间的多目标拆卸线平衡问题的数学模型,提出了一种基于Pareto解集的多目标遗传模拟退火算法进行求解。改进了模拟退火操作的Metropolis准则,使其能够求解多目标优化问题。采用拥挤距离评价非劣解的优劣,保留了优秀个体,并通过精英选择策略,将非劣解作为遗传操作的个体,引导算法向最优方向收敛。基于25项拆卸任务算例,通过与现有的单目标人工蜂群算法进行对比,验证了所提算法的有效性和优越性。最后将该算法应用于某打印机拆卸线实例中,求得8种可选平衡方案,实现了求解结果的多样性。     

多目标相关性指派问题的模拟退火算法

讨论了一种较复杂的指派问题—多目标相关性指派问题的模型,通过对搜索操作和参数的合理设置,提出一类求解多目标相关性指派问题Pareto最优解的模拟退火算法,并通过实际运算证明该算法是有效的。     

遗传算法在多目标决策中的应用研究

1 引言多目标优化是实践中广泛存在的一种优化问题,其理论研究已经取得了一些进展,但是在算法上还不太成熟。一种求解方法是将各个目标加权,从而将多目标问题转化为单目标问题。另一种方法是求多目标规划问题的非劣解(Pareto意义下的最优解),供决策者从中选择。本文主要讨论如何求解多目标优化问题的Pareto有效解。现在,考虑如下带有等式约束和不等式约束的多目标优化问题。     

求解多目标流水车间调度Pareto最优解的遗传强化算法

针对多目标流水车间调度Pareto最优问题, 本文建立了以最大完工时间和最大拖延时间为优化目标的多目标流水车间调度问题模型, 并设计了一种基于Q-learning的遗传强化学习算法求解该问题的Pareto最优解. 该算法引入状态变量和动作变量, 通过Q-learning算法获得初始种群, 以提高初始解质量. 在算法进化过程中, 利用Q表指导变异操作, 扩大局部搜索范围. 采用Pareto快速非支配排序以及拥挤度计算提高解的质量以及多样性, 逐步获得Pareto最优解. 通过与遗传算法、NSGA-II算法和Q-learning算法进行对比实验, 验证了改进后的遗传强化算法在求解多目标流水车间调度问题Pareto最优解的有效性.     

基于新模型的多目标Memetic算法及收敛分析

将多目标函数优化问题转化成单目标约束优化问题.对转化后的问题提出了基于约束主导原理的选择方法,克服了多数方法只使用Pareto优胜关系作为选择策略而没有采用偏好信息这一缺陷;Memetic算法是求解多目标优化问题最有效的方法之一,它融合了局部搜索和进化计算.新的多目标Memetic算法引进C-metric,将模拟退火算法与遗传算法结合起米,改善了全局搜索能力.用概率论的有关知识证明了算法的收敛性.仿真结果表明该方法对不同的试验函数均可求出一组沿着Pareto前沿分布均匀且散布广泛的非劣解.     

目标0-1背包问题的元胞竞争决策算法*

为求解多目标0-1背包问题,基于竞争决策算法原理和多目标优化问题的特性,提出了一种求解多目标0-1背包问题的元胞竞争决策算法。将元胞自动机演化规则引入竞争决策算法,给出了算法的具体描述,并使用Delphi 7.0实现了算法的具体步骤。为了提高多目标非劣解(Pareto解)的分布性和多样性,利用全局经验作为指导,在最稀疏的Pareto解附近进行邻域搜索。经过大量数据测试和验证,该算法具有真实的Pareto前沿逼近效果,是一种多目标优化问题的有效方法。     

Melt index prediction by RBF neural network optimized with an MPSO-SA hybrid algorithm

Melt index (MI) is considered as an important quality variable which determines the product specifications, so reliable estimation of MI is crucial in the quality control of practical polypropylene (PP) polymerization processes. A novel MPSO-SA-RNN (modified PSO-SA algorithm and RBF neural network) MI prediction model based on radial basis function (RBF) neural network and artificial intelligent algorithms particle swarm optimization (PSO), and simulated annealing (SA) is presented, where the traditional PSO is modified first and then combined with SA to overcome the inherent defects in PSO and SA, and to achieve better optimization performance. The proposed optimization algorithm, MPSO-SA algorithm, is then used to optimize the parameters of the RBF neural network. Then the network is employed to build the MI prediction model, and the MPSO-SA-RNN model is thereby developed. Based on the data from a real plant, the approach presented above is evaluated and the research results confirm the validity of the proposed model, as well as the advantage of MPSO-SA algorithm to the traditional PSO and SA algorithms in handling optimization problems.     

Applications of hybrid optimization techniques for model updating of rotor shafts

This paper presents an approach by combining the genetic algorithm (GA) with simulated annealing (SA) algorithm for enhancing finite element (FE) model updating. The proposed algorithm has been applied to two typical rotor shafts to test the superiority of the technique. It also gives a detailed comparison of the natural frequencies and frequency response functions (FRFs) obtained from experimental modal testing, the initial FE model and FE models updated by GA, SA, and combination of GA and SA (GA–SA). The results concluded that the GA, SA, and GA–SA are powerful optimization techniques which can be successfully applied to FE model updating, but the appropriate choice of the updating parameters and objective function is of great importance in the iterative process. Generally, the natural frequencies and FRFs obtained from FE model updated by GA–SA show the best agreement with experiments than those obtained from the initial FE model and FE models updated by GA and SA independently.     

基于可靠度的计算机系统优化问题的研究

分析了计算机系统硬件优化配置问题,给出了该问题的优化模型,并利用模拟退火算法和遗传退火算法对求解该问题进行了具体设计。通过实例仿真比较,遗传退火算法能得到比文献中算法和模拟退火算法更好的结果,表明算法是非常有效的。     

A heuristic optimization algorithm for HMM based on SA and EM in machinery diagnosis

This paper proposes a novel hidden Markov model (HMM) based on simulated annealing (SA) algorithm and expectation maximization (EM) algorithm for machinery diagnosis. As traditional HMM is sensitive to initial values and EM is easy to trap into a local optimization, SA is combined to improve HMM which can overcome local optimization searching problem. The proposed HMM has strong ability of global convergence, and optimizes the process of parameters estimation. Finally, through a case study, the computation results illustrate this SAEM-HMM has high efficiency and accuracy, which could help machinery diagnosis in practical.     

基于模拟退火的并行粒子群优化研究

针对粒子群优化(PSO)容易陷入局部极小,提出将模拟退火(SA)引入并行PSO算法．这种模拟退火并行粒子群算法,结合了并行粒子群算法的快速寻优能力和SA的概率突跳特性,保持了群体多样性,从而避免了种群退化．针对转炉提钒过程是一个复杂非线性反应过程而难以建立终点控制模型的问题,提出了基于模拟退火的并行粒子群RBF网络的辨识模型,优化了RBF核中心个数,从而克服了随机性选择．将该模型用于预测提钒吹氧时间,仿真结果表明预测误差不超过真实值的20％．     

Micromachined III-V multimorph actuators for MOEMS applications - concept, design, and model

A multimorph configuration of III-V materials that is based on the global optimization method using the simulated annealing (SA) algorithm is proposed to enhance small intrinsic piezoelectric effect of such materials. In this paper, a novel piezoelectric multimorph microactuator design such as a five-layer multimorph is proposed and analyzed using both numerical and analytical methods for potential microoptoelectromechanical systems (MOEMS) applications. Previously published analytical multimorph models for MEMS, which have been shown to reduce to equivalent expressions, are also presented and used for design optimization. By imposing a set of "real-world" constraints on the designs, optimal device geometries, which are more realistic and visible, have been determined using the SA method. Design tradeoffs are also discussed in terms of potential utility for the multimorph actuator in MOEMS applications. Finally, the proposed multimorph models are verified by finite element simulations results.     

Moving object detection and tracking by using annealed background subtraction method in videos: Performance optimization

In computer vision, moving object detection and tracking methods are the most important preliminary steps for higher-level video analysis applications. In this frame, background subtraction (BS) method is a well-known method in video processing and it is based on frame differencing. The basic idea is to subtract the current frame from a background image and to classify each pixel either as foreground or background by comparing the difference with a threshold. Therefore, the moving object is detected and tracked by using frame differencing and by learning an updated background model. In addition, simulated annealing (SA) is an optimization technique for soft computing in the artificial intelligence area. The p-median problem is a basic model of discrete location theory of operational research (OR) area. It is a NP-hard combinatorial optimization problem. The main aim in the p-median problem is to find p number facility locations, minimize the total weighted distance between demand points (nodes) and the closest facilities to demand points. The SA method is used to solve the p-median problem as a probabilistic metaheuristic. In this paper, an SA-based hybrid method called entropy-based SA (EbSA) is developed for performance optimization of BS, which is used to detect and track object(s) in videos. The SA modification to the BS method (SA-BS) is proposed in this study to determine the optimal threshold for the foreground-background (i.e., bi-level) segmentation and to learn background model for object detection. At these segmentation and learning stages, all of the optimization problems considered in this study are taken as p-median problems. Performances of SA-BS and regular BS methods are measured using four videoclips. Therefore, these results are evaluated quantitatively as the overall results of the given method. The obtained performance results and statistical analysis (i.e., Wilcoxon median test) show that our proposed method is more preferable than regular BS method. Meanwhile, the contribution of this study is discussed.     

基于混合策略的QoS选播流路由优化算法

分析了QoS选播流交错服务问题的产生过程及其对网络资源的浪费，提出了一种基于混合策略的全局路由优化解决方法．在构建路由优化问题模型的基础上，得出了选播流路由端到端时延、服务器负载、网络流量、路径调整等多个优化目标和QoS约束的表达式．给出了GA、SA、TS三种算法有机结合形成的一种混合优化算法，并说明了运用该算法求解的关键步骤和实现过程．实验结果表明，本算法能够在满足QoS约束的前提下实现选播路由的多目标组合优化，与基于GA或者SA的求解算法相比具有更强的稳定性和更高的精确度．     

基于GM(1,1)模型和模拟退火算法的中长期负荷预测

分析了灰色模型(GM)和模拟退火模型(SA),GM(1,1)学习参数的计算采用最小二乘法,而最小二乘法是基于残差平方和最小寻优,容易陷入局部最小,对于非线性较强的负荷,会产生很大的偏差。提出了一种GM(1,1)与SA相结合的方法,根据模拟退火原理,结合概率突跳特性在解空间中随机寻找目标函数的全局最优解,自动优化GM(1,1)的参数,在负荷预测的实例中取得良好效果。