具有模糊约束的大工业过程的关联平衡法

研究稳态大工业过程的递阶优化控制算法时,针对模型－实际存在差异以及实际约束条件有一定伸缩性的问题,将子过程模型作为等式约束,通过引入模糊系数使其转化为模型等式约束,同时对子过程原有的不等式约束进行模糊化处理,提出具有模糊约束的关联平衡法,进而了两种情形（开环和有全局反馈）的关联平衡法。仿真结果表明,有全局反馈的模糊关联法得到的最终解十分接近实际过程的真实最优解。     

具有模糊参数的大工业过程的关联平衡法

考虑到模型-实际差异,将子过程的数学模型中的系数处理为模糊数,提出了基于模糊 模型的大工业过程的稳态关联平衡法.论文研究了关联平衡法的三种情况:开环的、有全局反馈 的以及双迭代的关联平衡法.仿真结果验证了基于模糊模型的关联平衡法的有效性.同时表明 模糊双迭代法是迄今为止在线迭代次数最少的稳态协调算法.     

大工业过程稳态优化的模糊双迭代法

在研究大工业过程稳态优化控制算法时, 针对模型–实际存在差异, 将子过程模型作为等式约束, 通过引入模糊系数使其转化为模糊等式约束, 同时对子过程的不等式约束进行模糊化处理, 提出具有模糊不等式约束的模糊双迭代法, 通过实际例子研究了模糊双迭代法. 仿真结果表明, 模糊双迭代法目标函数非常接近实际目标函数值、算法迭代次数较精确双迭代法有明显改善. 这对实际生产非常重要.     

具有模糊参数的大工业过程的关联预测法

针对模型－实际差异问题,提出了基于模糊模型的大工业过程稳态关联预测协调法。研究了关联预测协调法的两种情形：开环的关联预测协调法和有全局反馈的关联预测协调法。仿真结果表明,该算法收敛更快,目标函数更好,并且在线迭代次数有一定程度的减少。     

基于模糊模型的大系统关联平衡法的收敛性

结合模糊规划与非线性规划的收敛性分析方法,给出了基于模糊模型的关联平衡法的 收敛性分析.首先证明了经去模糊处理后形成的约束集合与子过程原有的约束集合必有交集. 并且此交集是凸集.在此基础上,分析和证明了基于模糊模型的关联平衡法可用于求解基于模 糊模型的稳态大工业过程递阶优化问题.继而通过定义迭代序列的A-内积,证明了基于模糊模 型的关联平衡法是收敛的.同时给出了保证迭代收敛的迭代系数取值范围.     

模糊需求和模糊能力约束的集约生产计划

通过对模糊需求量和模糊等式及模糊需求环境下生产-库存平衡方程两种等价的描述方法的基础上,建立了具有模糊需求量和模糊能力约束集约生产计划问题的最佳平衡模型(BAPP)、交互式集约计划模型(IAPP)和交互式求解方法.     

具有模糊参数的大工业过程的混合协调法

在研究大工业过程的递阶优化控制算法时, 针对模型-实际差异问题, 将子过程的数学模型中的系数处理为模糊数, 提出了基于模糊模型的大工业过程的稳态混合协调算法. 研究了两种具有模糊参数的混合算法: 开环的和有全局反馈的混合协调算法. 与基于精确系数模型的混合算法相比基于模糊模型的混合算法收敛更快, 目标函数更好.     

基于模糊动态模型的多变量系统模糊控制

采用模糊动态模型对多变量复杂非线性系统进行模糊控制.首先针对局部线性动态模 型设计状态反馈控制器,然后利用模糊推理确定整个系统的控制;在一系列局部模型通过模糊 隶属函数连接得到的连续的全局模型中,全面考虑其它关联子系统对标称线性系统的摄动,并 利用大系统分散控制关联稳定性的概念和方法,得到了闭环模糊系统稳定的充分条件.仿真例 子验证了该设计方法的正确性.     

集群式供应链中基于约束层次和模糊偏好的供应商评价选择模型

针对集群式供应链中采购商选择供应商过程中出现的过约束问题,提出一种基于约束层次和模糊偏好的供应商评价选择模型.该模型基于约束层次理论,依据采购商对供应商约束的必需性程度,将约束分为强制性约束层和非强制性约束层;同时采用模糊偏好的量化方法来确定采购商对供应商的模糊偏好,简化了供应商的选择过程;另外,该模型改进了约束层次理论中的全局评价算子,基于线性加权法构造了基于强制性约束层评价模型和非强制性约束层评价模型的供应商综合评价模型,提高了供应商选择的成功率.最后通过算例验证该模型是有效的.     

基于模糊约束规划的自动协商

给出了一个基于模糊约束规划模型的自动协商系统。建立了模糊约束规划模型并利用模糊模拟、神经网络和遗传算法给出了求解Pareto最优解的混合智能算法;协商过程中卖方智能体根据神经网络拟合的效用函数并运行混合智能体算法得到当前协商步的Pareto最优解,避免了对大型商品数据库的反复搜索,为系统推向实际应用奠定了基础;协商模型仿真实验表明了协商系统返回的解与实际调查得到的用户偏好相一致。     

An augmented Lagrangian method for VLSI global placement

By ignoring some cell overlaps, global placement computes the best position for each cell to minimize the wirelength. It is an important stage in very large scale integration (VLSI) physical design, since circuit performance heavily depends on the placement results. In this paper, we propose an augmented Lagrangian method to solve the VLSI global placement problem. In the proposed method, a cautious dynamic density weight strategy is used to balance the wirelength objective and the density constraints, and an adaptive step size is used to obtain a trade-off between runtime and solution quality. The proposed method is tested on the IBM mixed-size benchmarks and the International Symposium on Physical Design 2006 placement contest benchmarks. Experimental results show that our global placement method outperforms the state-of-the-art placement approaches in terms of solution quality on most of the benchmarks.     

On applicability of interaction balance method to global identification of interconnected steady-state systems

A decentralized two-level identification scheme using the interaction balance as a coordination principle is proposed to find the model of a large-scale interconnected steady-state system with the structure described by an interconnection matrix. The applicability conditions of the interaction balance method (IBM) to determine the best, in the least squares sense, model of the overall system are derived.     

Quadratic approximation based differential evolution with valuable trade off approach for bi-objective short-term hydrothermal scheduling

Short-term combined economic emission hydrothermal scheduling (CEES) is a bi-objective problem: (i) minimizing fuel cost and (ii) minimizing pollutant emission. In this paper, quadratic approximation based differential evolution with valuable trade off approach (QADEVT) has been developed to solve the bi-objective hydrothermal scheduling problem. The practical hydrothermal system possesses various constraints which make the problem of finding global optimum difficult. In this paper, heuristic rules are proposed to handle the water dynamic balance constraints and heuristic strategies based on priority list are employed to handle active power balance constraints. A feasibility-based selection technique is also introduced to satisfy the reservoir storage volumes constraints. To demonstrate the superiority of the proposed approach, simulation results have been compared with those obtained by differential evolution (DE) and particle swarm optimization (PSO) with same heuristic strategies and the earlier reported methods available in literature. The simulation results reveal that the proposed approach is capable of efficiently providing superior solutions.     

Dynamic execution planning for reliable collaborative business processes

Collaborative business processes, implemented and carried out via web services and enabling dynamic interaction among organizations, are becoming more indispensable to competitiveness in the global market. As dynamic collaboration prevails, the quality of service (QoS) of collaborative processes becomes more important. A critical requirement in cases where processes involve long-term activities is to guarantee successful completion within time constraints. In this study, we developed a methodology for dynamic planning of web service execution that imparts reliability to collaborative business processes. In order to ensure that processes successfully execute within time constraints and at minimum cost, the proposed method dynamically modifies execution plans at run-time by means of fault-tolerance techniques. Since generation of an execution plan of minimum cost while guaranteeing successful completion is classified as an NP-hard problem, a heuristic algorithm was developed. Additionally, to compare the proposed algorithm’s performance with those of the branch-and-bound method and the genetic algorithm (GA), a set of experiments was conducted.     

Integrated layout and topology optimization design of multi-frame and multi-component fuselage structure systems

The purpose of this paper is to present an extended integrated layout and topology optimization method dealing with the multi-frame and multi-component fuselage structure systems design. Considering an aircraft or aerospace fuselage system including main structure, numbers of frames and featured components located on the frames, a simultaneous optimization procedure is proposed here including geometrical design variables of components and frames as well as topological design variables of main structure and frame structures. The multi-point constraints (MPC) scheme is used to simulate the rivets or bolts connecting the components, frames and structures. The finite circle method (FCM) is implemented to avoid the overlaps among different components and frames. Furthermore, to deal with the difficulties of large numbers of non-overlapping constraints, a penalty method is used here to compose the global strain energy and non-overlapping constraints into a single objective function. To guarantee the fuselage system’s balance, the constraint on the system centroid is also introduced into the optimization. Different numerical examples are tested and the optimized solutions have demonstrated the validity and effectiveness of the proposed formulation.     

带自适应感知能力的粒子群优化算法

提出一种求解约束优化问题的改进粒子群优化算法。它利用可行性判断规则处理约束条件,更新个体最优解和全局最优解。通过为粒子赋予自适应感知能力,算法能较好地平衡全局和局部搜索,且有能力跳出局部极值,防止早熟。边界附近粒子的感知结果被用来修正其飞行速度以加强算法对约束边界的搜索。实验结果表明,新算法收敛速度快,寻优能力强,能很好地求解约束优化问题。     

无线传感器网络任务分配的遗传优化算法

为了延长网络生命周期,减少网络能量消耗和均衡网络负载,构造了无线传感器网络任务分配的模型,提出与小生境技术相结合自适应选择概率、三—三交叉算子和目标位变异算子,改进GA算法在很好地保持种群收敛性的同时,提高了算法的局部和全局搜索能力。仿真实验结果表明,该分配算法在局部求解与全局探索之间取得了较好的平衡,能有效减少无线传感器网络的计算时间和网络能耗,并有效地均衡网络负载。