A two-layer architecture for economically optimal process control and operation

A two-layer architecture for dynamic real-time optimization (or nonlinear modelpredictive control (NMPC) with an economic objective) is presented, where the solution of the dynamic optimization problem is computed on two time-scales. On the upper layer, a rigorous optimization problem is solved with an economic objective function at a slow time-scale, which captures slow trends in process uncertainties. On the lower layer, a fast neighboring-extremal controller is tracking the trajectory in order to deal with fast disturbances acting on the process. Compared to a single-layer architecture, the two-layer architecture is able to address control systems with complex models leading to high computational load, since the rigorous optimization problem can be solved at a slower rate than the process sampling time. Furthermore, solving a new rigorous optimization problem is not necessary at each sampling time if the process has rather slow dynamics compared to the disturbance dynamics. The two-layer control strategy is illustrated with a simulated case study of an industrial polymerization process.     

用于约束多目标优化问题的混合粒子群算法

针对约束多目标优化问题,结合Pareto支配思想、锦标赛选择和排挤距离技术,采用双种群搜索策略,引进免疫机制,对传统的粒子更新策略进行改进,提出一种用于求解约束多目标优化问题的混合粒子群算法。通过4个标准约束多目标函数进行测试,测试结果表明,该方法有效可行,相比传统多目标优化算法更优。     

Consistent hierarchical economic NMPC for a class of hybrid systems using neighboring-extremal updates

A hierarchical two-layer control algorithm is developed for a class of hybrid (discrete-continuous dynamic) systems to support economically optimal operation of batch or continuous processes with a predefined production schedule. For this class of hybrid systems, the optimal control moves as well as the controlled switching times between two adjacent modes are determined online. In contrast to closely related schemes for integrated scheduling and control, the sequence of modes is not optimized. On the upper layer, the economic optimal control problem is solved rigorously by a slow hybrid economic model predictive controller at a low sampling rate. On the lower layer, a fast hybrid neighboring-extremal controller is based on the same economic optimal control problem as the slow controller to ensure consistency between both layers. The fast neighboring-extremal controller updates rather than tracks the optimal trajectories from the upper layer to account for disturbances. Consequently, the fast controller steers the process to its operational bounds under disturbances and the economic potential of the process is exploited anytime. The suggested two-layer control algorithm provides fully consistent control action on the fast and slow time-scale and thus avoids performance degradation and even infeasibilities which are commonly encountered if inconsistent optimal control problems are formulated and solved.     

Suboptimization techniques for improving transients in adaptive control†

Two possible optimization techniques for on-line adjustment of the design parameters involved in the adaptation algorithms of adaptive control schemes for minimum phase plants arc discussed. Sensitivity corrections adapled to this particular problem are introduced for correcting inaccuracies in an auxiliary model derived in order to be able to apply classical optimization techniques to the whole scheme. The main objective of such techniques is to improve the adaptation transient performances. The resulting strategies are discussed from the point of view of performance and possible implementation. Simulations illustrate the feasibility of the proposed optimizing procedures which are an extension, using a more general optimization theory and/or a sensitivity approach, of previous results and an alternative to the adaptive sampling approach of De la Sen (1984 c).     

Cellular particle swarm optimization

This paper proposes a cellular particle swarm optimization (CPSO), hybridizing cellular automata (CA) and particle swarm optimization (PSO) for function optimization. In the proposed CPSO, a mechanism of CA is integrated in the velocity update to modify the trajectories of particles to avoid being trapped in the local optimum. With two different ways of integration of CA and PSO, two versions of CPSO, i.e. CPSO-inner and CPSO-outer, have been discussed. For the former, we devised three typical lattice structures of CA used as neighborhood, enabling particles to interact inside the swarm; and for the latter, a novel CA strategy based on “smart-cell” is designed, and particles employ the information from outside the swarm. Theoretical studies are made to analyze the convergence of CPSO, and numerical experiments are conducted to compare the proposed algorithm with different variants of PSO. According to the experimental results, the proposed method performs better than other variants of PSO on benchmark test functions.     

一种改进的蚁群算法在工艺规划与车间调度集成优化中的应用

改进标准蚁群算法的执行策略,可提高工艺规划和调度集成问题的求解 质量和效率。通过节点集、有向弧/无向弧集、AND/OR 关系,建立了基于AND/OR 图的工 艺规划和调度集成优化模型。提出一种求解工艺规划与车间调度集成问题的改进蚁群优化算 法,采用了信息素动态更新策略避免收敛过慢和局部收敛,利用多目标优化策略提高求解质 量。仿真结果证明了该算法的有效性。     

Composite adaptive control with fast convergence for multilayer neural network

A composite adaptive control (CAC) that combines the benefits of direct and indirect adaptive controls has better parameter adaptation and control response. Multilayer neural networks (NNs) can be employed to enhance a model's representation capacity, but previous composite adaptive approaches cannot easily train the model due to its nonlinearities. A novel CAC is therefore developed in this study to tackle the above limitations. A modified robust version is adopted by focusing on the direct adaptive part to enhance robustness of adaption. Then, the indirect parameter adaptive law is improved by adopting a small learning rate in which a multistep adaption update is executed in one control interval. Moreover, multistep prediction errors are implemented to guarantee the consistency of the approximation errors, and an experience replay technique is adopted to attenuate the requirement of persistent excitation conditions. These improvements not only accelerate the convergence process but also smoothen the updating of NN parameters. Given that a nonlinear plant with MIMO strict‐feedback structure is considered, the proposed CAC is integrated into the backstepping framework. The uniformly bounded property of the tracking errors and the approximation errors is proven by Lyapunov theory. The superiority of the proposed method and the roles of these improvements are demonstrated by comparative simulations.     

MOPSO算法及其在地下水监测网布局优化中的应用

在已有多目标粒子群优化算法（CMOPSO）研究和分析的基础上,为提高算法的聚合性和分布性,设计了一种新的精英档案维护及全局最优值选取策略,同时,使用动态全局最优值设置策略对原有算法的粒子速度更新公式进行扩展,以增强粒子的搜索能力,克服早熟现象。通过对疏勒河项目区地下水监测网空间布局多目标优化计算,表明该算法是求解大规模复杂多目标优化问题的一种有效手段。     

面向健康体检数据的多目标Top-k频繁模式挖掘方法

为解决现有的模式挖掘方法没有充分利用体检数据中检查项的异常程度与特定疾病之间相关性的问题,提出一种面向健康体检数据的多目标Top-k频繁模式挖掘方法.首先,针对体检数据的特点,提出异常度和覆盖率两个指标,在此基础上,将Top-k频繁模式挖掘建模为一个多目标优化问题;其次,针对该问题,提出一种基于偏好的种群初始化策略和一个面向模式和项的双层更新策略,并基于此设计一种高效的进化多目标优化算法进行求解.实验结果表明,所提出方法所获得的Top-k个模式不仅能够有效地反映其与特定疾病之间的关联性,而且能够提供多样化的模式,为健康管理提供重要的参考依据.     

Applying multi-objective ant colony optimization algorithm for solving the unequal area facility layout problems

The unequal area facility layout problem (UA-FLP) which deals with the layout of departments in a facility comprises of a class of extremely difficult and widely applicable multi-objective optimization problems with constraints arising in diverse areas and meeting the requirements for real-world applications. Based on the heuristic strategy, the problem is first converted into an unconstrained optimization problem. Then, we use a modified version of the multi-objective ant colony optimization (MOACO) algorithm which is a heuristic global optimization algorithm and has shown promising performances in solving many optimization problems to solve the multi-objective UA-FLP. In the modified MOACO algorithm, the ACO with heuristic layout updating strategy which is proposed to update the layouts and add the diversity of solutions is a discrete ACO algorithm, with a difference from general ACO algorithms for discrete domains which perform an incremental construction of solutions but the ACO in this paper does not. We propose a novel pheromone update method and combine the Pareto optimization based on the local pheromone communication and the global search based on the niche technology to obtain Pareto-optimal solutions of the problem. In addition, the combination of the local search based on the adaptive gradient method and the heuristic department deformation strategy is applied to deal with the non-overlapping constraint between departments so as to obtain feasible solutions. Ten benchmark instances from the literature are tested. The experimental results show that the proposed MOACO algorithm is an effective method for solving the UA-FLP.     

改进灰狼优化算法求解模糊车间调度问题

模糊车间调度问题是复杂调度的经典体现,针对此问题设计优秀的调度方案能提高生产效率。目前对于模糊车间调度问题的研究主要集中在单目标上,因此提出一种改进的灰狼优化算法（improved grey wolf optimization,IGWO）求解以最小化模糊完成时间和最小化模糊机器总负载的双目标模糊柔性作业车间调度问题。该算法首先采用双层编码将IGWO离散化,设计一种基于HV贡献度的策略提高种群多样性;然后使用强化学习方法确定全局和局部的搜索参数,改进两种交叉算子协助个体在不同更新模式下的进化;接着使用两级变邻域和四种替换策略提高局部搜索能力;最后在多个测例上进行多组实验分析验证改进策略的有效性。在多数测例上,IGWO的性能要优于对比算法,具有良好的收敛性和分布性。     

A new hybrid fuzzy-goal programming scheme for multi-objective topological optimization of static and dynamic structures under multiple loading conditions

This work presents a hybrid fuzzy-goal multi-objective programming scheme for topological optimization of continuum structures, in which both static and dynamic loadings are considered. The proposed methodology fortopological optimization first employs a fuzzy-goal programming scheme at the top level for multi-objective problems with static and dynamic objectives. For the static objective with multi-stiffness cases in the fuzzy-goal formulation, a hybrid approach, involving a hierarchical sequence approach or a hierarchical sequence approach coupled with a compromise programming method, is especially suggested for the statically loaded multi-stiffness structure at the sublevel. Concerning dynamic optimization problems of freevibration cases, nonstructural mass, oscillation of the objective function, and repeated eigenvalues are also discussed. Solid Isotropic Material with Penalization density–stiffness interpolation scheme is used to indicate the dependence ofmaterial modulus upon regularized element densities. The globally convergent version of the method of moving asymptotes and the sequential linear programming method areboth employed as optimizers. Several applications have been applied to demonstrate the validation of the presented methodologies.     

Efficient learning from adaptive control under sufficient excitation

Parameter convergence is desirable in adaptive control as it enhances the overall stability and robustness properties of the closed‐loop system. In existing online historical data (OHD)–driven parameter learning schemes, all OHD are exploited to update parameter estimates such that parameter convergence is guaranteed under a sufficient excitation (SE) condition which is strictly weaker than the classical persistent excitation condition. Nevertheless, the exploitation of all OHD not only results in possible unbounded adaptation but also loses the flexibility of handling slowly time‐varying uncertainties. This paper presents an efficient OHD‐driven parameter learning scheme for adaptive control, where a variable forgetting factor is specifically designed and is equipped with an estimation error feedback such that exponential parameter convergence is achieved under the SE condition without the aforesaid drawbacks. The proposed parameter learning scheme is incorporated with direct adaptive control to construct an OHD‐based composite learning control strategy. Numerical results have verified the effectiveness of the proposed approach.     

求解VLSI布图规划问题的多目标粒子群优化算法

布图规划在超大规模集成电路(VLSI)物理设计过程中具有重要作用,它是一个多目标组合优化问题且被证明是一个NP问题。为了有效解决布图规划问题,本文提出一个多目标粒子群优化(PSO)算法。该算法采用序列对表示法对粒子进行编码,根据遗传算法交叉算子的思想对粒子更新公式进行了修改;引入Pareto最优解的概念和精英保留策略,并设计了一个基于表现型共享的适应值函数以维护种群的多样性。仿真实验通过对MCNC标准问题的测试表明了本文算法是可行且有效的。     

渐进式分组狩猎的灰狼优化算法及其工程应用

针对灰狼优化（GWO）算法在求解复杂优化问题时存在后期收敛速度慢、易陷入局部最优的不足,提出了一种渐进式分组狩猎的灰狼优化（PGGWO）算法。首先,设计了非线性多收敛因子以增强全局勘探能力、避免局部最优;其次,提出了渐进式位置更新策略,该策略引入长鼻浣熊的包围策略和动态权重因子,前者在提高收敛精度和速度的同时避免局部最优,后者则动态地提升算法的收敛速度及全局寻优性能。最后,通过与标准GWO、4个GWO先进变体以及4个竞争力较强的新型进化算法对比,验证了PGGWO算法的有效性和先进性。在24个Benchmark函数和3个实际工程优化问题上的实验结果表明,PGGWO算法在收敛精度和收敛速度上具有明显优势,并且对约束优化问题也是有效的。     

强动态Ad Hoc网的拥塞控制:价格协作和滚动优化

Ad Hoc网络存在着无线多跳连接、节点移动这两个本质的特点.前者引起了与固定网络截然不同的信息流竞争新特点,后者导致了网络状态不断发生变化.首先,在采用链路干扰集描述信息流竞争特点的基础上,将小时间段内网络状态不变的拥塞控制问题表达成非线性优化问题;其次,运用基于对偶分解理论的价格协作法PCA(price cooperation approach)求解该优化问题,构建了一个基于链路干扰集的价格框架.同时,运用队列长度监控、邻居集合近似和HELLO捎带信息这3种技术将PCA转化成在实际Ad Hoc网络环境中可实施的协议.另一方面,运用状态检测和滚动优化方法,有针对性地解决Ad Hoc网络状态不确定时变性带来的问题,相应地设计了自适应优化策略AOS(adaptive optimization strategy).MATLAB仿真结果表明,AOS策略比PCA对网络状态的变化具有更好的自适应性能.NS仿真实验结果表明了PCA和PCA AOS在几乎所有的仿真场景和移动环境下,在重要的性能指标,包括吞吐量、丢包率、公平性等方面,都比TCP,ATCP和ATP有了明显的改进.     

多收率约束的催化裂化反再系统改进差分进化操作优化

针对现有催化裂化(FCC)装置操作优化中未根据市场需求考虑多产品收率约束的问题,本文提出了一种求解多产品收率约束催化裂化反再系统操作优化的改进差分进化(iDE)算法.首先针对FCC操作优化中约束多和不同操作变量的可行范围差异大的特点,设计了一种协同交互变异策略产生变异个体,以提高算法的开发和探索能力;其次提出了一种具有修复功能的参数自适应策略来更新变异因子和交叉因子.此外考虑到FCC操作优化具有时效强的特点,提出了对每一代种群中最好个体实施加强搜索的方法,以提高算法的收敛速度.仿真结果表明:在求解多产品收率的FCC反再系统操作优化问题上,该算法具有较强的全局寻优能力、鲁棒性以及较快的收敛速度.     

一种无线Mesh网络中可证明安全的HMIPv6路由优化方案

HMIPv6技术能够实现无线Mesh网络的无缝切换,针对其绑定更新过程中执行路由优化存在的安全问题,提出了一种适用于无线Mesh网络的基于椭圆曲线公钥自认证体制的安全路由优化方案。该方案使用户在执行路由优化的过程中能够实现对绑定更新消息的认证与授权,且通过有效的会话密钥协商机制为绑定更新消息的传输提供了安全保障,具有可证明安全性。最后通过性能分析表明,该方案简化了标准路由优化方案的流程,提高了一般注册过程的效率。     

Redis压缩列表研究与优化设计

针对Redis压缩列表（ziplist）更新机制在最坏情况下存在连锁更新问题,透彻分析Redis压缩列表更新机制实现原理,提出两种优化方案,方案一,通过优化连锁更新算法,将其修改为基于统计的顺序遍历更新机制,有效解决压缩列表在出现连锁更新情况下,时间复杂度较高的问题。新机制将更新时间复杂度由[O(N2)]下降为[O(N)],当出现大量节点的连锁更新时,消耗时间与无连锁更新时插入节点的时间接近。方案二,通过优化压缩列表节点结构体,消除了连锁更新现象,从而减少了由于连锁更新带来的额外时间,相比优化更新函数,性能更好。实验表明新方案在不影响原有功能情况下,优化效果显著。     

一种基于相角映射的改进多目标粒子群优化算法

提出一种相角粒子群优化算法求解多目标优化问题。该算法采用相角映射实现了粒子在相角空间上仅依赖于归一化多目标函数的快速搜索,在粒子飞行信息共享机制上引入共享池概念,提出基于关联支配排序和相似度排序的共享池更新策略,提高了Pareto解的多样性。采用Sigma领导策略和混沌变异操作,平衡了算法的快速搜索能力和全局寻优能力。标准多目标测试函数和电力系统广域阻尼控制多目标优化算例表明了所提出算法的可行性和有效性。