Sequential quadratic programming-based non-cooperative target distributed hybrid processing optimization method

The distributed hybrid processing optimization problem of non-cooperative targets is an important research direction for future networked air-defense and anti-missile firepower systems. In this paper, the air-defense anti-missile targets defense problem is abstracted as a nonconvex constrained combinatorial optimization problem with the optimization objective of maximizing the degree of contribution of the processing scheme to non-cooperative targets, and the constraints mainly consider geographical conditions and anti-missile equipment resources. The grid discretization concept is used to partition the defense area into network nodes, and the overall defense strategy scheme is described as a nonlinear programming problem to solve the minimum defense cost within the maximum defense capability of the defense system network. In the solution of the minimum defense cost problem, the processing scheme, equipment coverage capability, constraints and node cost requirements are characterized, then a nonlinear mathematical model of the non-cooperative target distributed hybrid processing optimization problem is established, and a local optimal solution based on the sequential quadratic programming algorithm is constructed, and the optimal firepower processing scheme is given by using the sequential quadratic programming method containing non-convex quadratic equations and inequality constraints. Finally, the effectiveness of the proposed method is verified by simulation examples.

Automatic differentiation for reduced sequential quadratic programming

In order to slove the large-scale nonlinear programming （NLP） problems efficiently, an efficient optimization algorithm based on reduced sequential quadratic programming （rSQP） and automatic differentiation （AD） is presented in this paper. With the characteristics of sparseness, relatively low degrees of freedom and equality constraints utilized, the nonlinear programming problem is solved by improved rSQP solver. In the solving process, AD technology is used to obtain accurate gradient information. The numerical results show that the combined algorithm, which is suitable for large-scale process optimization problems, can calculate more efficiently than rSQP itself.

SEQUENTIAL QUADRATIC PROGRAMMING METHODS FOR OPTIMAL CONTROL PROBLEMS WITH STATE CONSTRAINTS

A kind of direct methods is presented for the solution of optimal control problems with state constraints. These methods are sequential quadratic programming methods. At every iteration a quadratic programming which is obtained by quadratic approximation to Lagrangian function and linear approximations to constraints is solved to get a search direction for a merit function. The merit function is formulated by augmenting the Lagrangian function with a penalty term. A line search is carried out along the search direction to determine a step length such that the merit function is decreased. The methods presented in this paper include continuous sequential quadratic programming methods and discreate sequential quadratic programming methods.

An Overview of Sequential Approximation in Topology Optimization of Continuum Structure

This paper offers an extensive overview of the utilization of sequential approximate optimization approaches in the context of numerically simulated large-scale continuum structures.These structures,commonly encountered in engineering applications,often involve complex objective and constraint functions that cannot be readily expressed as explicit functions of the design variables.As a result,sequential approximation techniques have emerged as the preferred strategy for addressing a wide array of topology optimization challenges.Over the past several decades,topology optimization methods have been advanced remarkably and successfully applied to solve engineering problems incorporating diverse physical backgrounds.In comparison to the large-scale equation solution,sensitivity analysis,graphics post-processing,etc.,the progress of the sequential approximation functions and their corresponding optimizersmake sluggish progress.Researchers,particularly novices,pay special attention to their difficulties with a particular problem.Thus,this paper provides an overview of sequential approximation functions,related literature on topology optimization methods,and their applications.Starting from optimality criteria and sequential linear programming,the other sequential approximate optimizations are introduced by employing Taylor expansion and intervening variables.In addition,recent advancements have led to the emergence of approaches such as Augmented Lagrange,sequential approximate integer,and non-gradient approximation are also introduced.By highlighting real-world applications and case studies,the paper not only demonstrates the practical relevance of these methods but also underscores the need for continued exploration in this area.Furthermore,to provide a comprehensive overview,this paper offers several novel developments that aim to illuminate potential directions for future research.

Sequential quadratic programming particle swarm optimization for wind power system operations considering emissions

In this paper,a computation framework for addressing combined economic and emission dispatch(CEED)problem with valve-point effects as well as stochastic wind power considering unit commitment(UC)using a hybrid approach connecting sequential quadratic programming(SQP)and particle swarm optimization(PSO)is proposed.The CEED problem aims to minimize the scheduling cost and greenhouse gases(GHGs)emission cost.Here the GHGs include carbon dioxide(CO_(2)),nitrogen dioxide(NO_(2)),and sulphur oxides(SO_(x)).A dispatch model including both thermal generators and wind farms is developed.The probability of stochastic wind power based on the Weibull distribution is included in the CEED model.The model is tested on a standard system involving six thermal units and two wind farms.A set of numerical case studies are reported.The performance of the hybrid computational method is validated by comparing with other solvers on the test system.

Sequential quadratic programming enhanced backtracking search algorithm

In this paper, we propose a new hybrid method called SQPBSA which combines backtracking search optimization algorithm （BSA） and sequential quadratic programming （SQP）. BSA, as an exploration search engine, gives a good direction to the global optimal region, while SQP is used as a local search technique to exploit the optimal solution. The experiments are carried on two suits of 28 functions proposed in the CEC-2013 competitions to verify the performance of SQPBSA. The results indicate the proposed method is effective and competitive.

The dynamic relaxation form finding method aided with advanced recurrent neural network

How to establish a self‐equilibrium configuration is vital for further kinematics and dynamics analyses of tensegrity mechanism.In this study,for investigating tensegrity form‐finding problems,a concise and efficient dynamic relaxation‐noise tolerant zeroing neural network(DR‐NTZNN)form‐finding algorithm is established through analysing the physical properties of tensegrity structures.In addition,the non‐linear constrained opti-misation problem which transformed from the form‐finding problem is solved by a sequential quadratic programming algorithm.Moreover,the noise may produce in the form‐finding process that includes the round‐off errors which are brought by the approximate matrix and restart point calculating course,disturbance caused by external force and manufacturing error when constructing a tensegrity structure.Hence,for the purpose of suppressing the noise,a noise tolerant zeroing neural network is presented to solve the search direction,which can endow the anti‐noise capability to the form‐finding model and enhance the calculation capability.Besides,the dynamic relaxation method is contributed to seek the nodal coordinates rapidly when the search direction is acquired.The numerical results show the form‐finding model has a huge capability for high‐dimensional free form cable‐strut mechanisms with complicated topology.Eventually,comparing with other existing form‐finding methods,the contrast simulations reveal the excellent anti‐noise performance and calculation capacity of DR‐NTZNN form‐finding algorithm.

Maximum likelihood estimation of nonlinear mixed-effects models with crossed random effects by combining first-order conditional linearization and sequential quadratic programming

Nonlinear mixed-eirects (NLME) modek have become popular in various disciplines over the past several decades.However,the existing methods for parameter estimation imple-mented in standard statistical packages such as SAS and R/S-Plus are generally limited k) single-or multi-level NLME models that only allow nested random effects and are unable to cope with crossed random effects within the framework of NLME modeling.In t his study,wc propose a general formulation of NLME models that can accommodate both nested and crassed random effects,and then develop a computational algorit hm for parameter estimation based on normal assumptions.The maximum likelihood estimation is carried out using the first-order conditional expansion (FOCE) for NLME model linearization and sequential quadratic programming (SCJP) for computational optimization while ensuring positive-definiteness of the estimated variance-covariance matrices of both random effects and error terms.The FOCE-SQP algorithm is evaluated using the height and diameter data measured on trees from Korean larch (L.olgeiisis var,Chang-paienA.b) experimental plots aa well as simulation studies.We show that the FOCE-SQP method converges fast with high accuracy.Applications of the general formulation of NLME models are illustrated with an analysis of the Korean larch data.

Design of a Computational Heuristic to Solve the Nonlinear Liénard Differential Model

In this study,the design of a computational heuristic based on the nonlinear Liénard model is presented using the efficiency of artificial neural networks(ANNs)along with the hybridization procedures of global and local search approaches.The global search genetic algorithm(GA)and local search sequential quadratic programming scheme(SQPS)are implemented to solve the nonlinear Liénard model.An objective function using the differential model and boundary conditions is designed and optimized by the hybrid computing strength of the GA-SQPS.The motivation of the ANN procedures along with GA-SQPS comes to present reliable,feasible and precise frameworks to tackle stiff and highly nonlinear differentialmodels.The designed procedures of ANNs along with GA-SQPS are applied for three highly nonlinear differential models.The achieved numerical outcomes on multiple trials using the designed procedures are compared to authenticate the correctness,viability and efficacy.Moreover,statistical performances based on different measures are also provided to check the reliability of the ANN along with GASQPS.

采用融合遗传算法的高速公路服务区综合能源系统优化调度研究

为达成“碳中和”目标愿景、促进公路交通系统与新能源的融合,以高速公路服务区为研究对象,考虑服务区内电、冷、热、气共4种负荷需求,构建了包含风光发电的新能源发电方式和电转气设备的高速公路服务区综合能源系统。在此基础上,以风电、光伏出力日前预测和多能负荷日前消耗为输入,各能源设备出力及购能分配为输出,以总成本最低为目标函数,考虑能量平衡、设备安全、运行状态等约束,建立了高速公路服务区综合能源系统优化调度模型。针对高速公路服务区综合能源系统调度问题,设计了遗传-序列二次规划融合优化算法,并以某服务区夏季典型日为例进行验证。结果表明:所构建的调度系统能够有效消纳可再生能源出力,协调外部购电、购气的比例,最终达到降低成本的效果;所提融合算法的调度结果与传统遗传算法、传统序列二次规划算法相比,在成本上分别降低了11.52%、0.70%,求解耗时仅为传统遗传算法的6.7%,独立性相比传统序列二次规划算法得到了提高。

基于特征提取和最优加权集成策略的风机叶片结冰故障检测

针对风机叶片结冰检测中现有集成方法不能充分发挥不同个体分类器优势的问题,提出了一种基于特征提取和最优加权集成学习的叶片结冰检测模型。首先,用堆叠降噪自动编码器提取结冰关联特征后,考虑不同单一分类器在二分类应用中的表现及其差异,选择随机森林、极限梯度提升树、轻量梯度提升机、K-近邻算法作为个体学习器,并用贝叶斯算法对其进行超参数优化。然后提出基于序列二次规划的最优加权集成策略对叶片状态进行判别。最后利用金风科技提供的15号和21号风机的历史数据进行了仿真实验,结果表明:所提出的检测模型与个体学习器及其他集成模型相比多项指标均有所提升,准确度达到了99.2%,在结冰检测方面具有一定的有效性。

序列二次规划方法在斜拉桥索力调整中的应用研究

斜拉桥施工至成桥阶段时,由于各种因素影响,结构实际线形和内力与理论成桥状态相比,存在一定的误差,通常需要进行索力调整。该文针对斜拉桥在调索阶段的索力调整量计算问题,提出采用一种基于影响矩阵和序列二次规划求解索力调整量的方法。以某大跨度混合梁斜拉桥为背景,首先计算各索力单位变化下结构的线形和内力响应值,得到索力影响矩阵,然后选取合适的目标函数及约束条件,构建索力调整量计算模型,最后引入序列二次规划法求解索力调整量,得到索力调整后的结构内力与线形状态。计算结果表明:该方法计算简便,索力调整后的结构线形与内力均能满足施工控制要求。

大口径火炮弹协调器机构可靠性优化设计研究

为提高弹协调器的交弹效率,同时保障协调交弹动作具有高可靠度,开展某弹协调器机构的可靠性优化设计。考虑主要几何尺寸、制造误差、重要构件弹性变形等影响因素,建立某弹协调器的参数化刚柔耦合动力学模型,通过参数化动力学分析,复现协调器的协调交弹动作失效,并建立相对应的功能函数及协调器可靠性优化设计模型。针对协调器可靠性优化模型,为提高优化设计的效率和精度,构建新的Kriging模型自适应更新策略,并与序列二次规划(SQP)方法、功能函数度量法(PMA)/可靠度指标法(RIA)结合,提出协调器机构可靠性优化设计方法。研究结果表明,在协调交弹可靠度满足要求的情况下大幅提高了某协调器的协调效率,也验证了所提方法的有效性和工程价值。

基于SQP算法的厢舱类产品快速设计技术研究

针对大规模定制背景下产品设计制造过程中遇到的轻量化和强度校核等需求,提出了一种基于序列二次规划(SQP)算法的结构优化与快速设计方法。以方舱为实例对象,按照最优控制理论构建产品关键参数设计数学计算模型,并运用SQP算法实现参数的最优求解,同时通过产品族模块化划分和参数化变型配置技术实现产品结构的快速设计。最后通过二次开发构建了相应的数字化设计平台,验证了该方法的可行性。结果表明:该方法能有效提高产品结构设计的效率和合理性。

基于遗传算法-序列二次规划的磁共振被动匀场优化方法

为了解决磁共振成像(MRI)系统中固有的主磁场(B0)不均匀的问题,提出遗传算法-序列二次规划(GASQP)算法,以提高7 T磁共振的主磁场均匀性.从被动匀场数学模型的角度出发,该混合算法利用GA算法获得稳定的初始解,实现主磁场的第1次优化,再通过SQP算法的快速求解,在较少的时间内实现主磁场的第2次优化,同时提高磁共振主磁场的均匀性.采用正则化方法减少磁场均匀所需的铁片质量,并且获得稀疏的铁片分布.在仿真建模的案例研究中,7 T磁共振裸磁场均匀度可以从462×10-6优化到4.5×10-6,并且在匀场空间上仅消耗0.8 kg的铁片.相比于传统的GA优化方法,新方案的磁场均匀性提高了96.7%,总铁片消耗质量减少了85.7%.实验结果表明,GA-SQP算法比其他优化算法具有更强的鲁棒性和竞争力.

基于拓扑管网法的抽水试验渗透系数反演研究

抽水试验是获取当地水文地质参数的常规手段。本文根据环北部湾广西水资源配置工程郁江那凤干线现场抽水试验,建立多孔抽水拓扑管网模型,并将序列二次规划法优化方法(SQP)应用于渗透系数反演计算中,最终获得工程区内承压含水层平行断层方向和垂直断层方向的渗透系数,与实测数据对比,反演效果良好。该方法能获取各向异性渗透系数,为抽水试验反演地下水参数提供了一种新的思路和方法。

求解双层规划问题的松弛序列二次规划方法

考虑一类具有特殊结构的双层规划问题,其下层问题为凸问题.首先通过内点罚方法将下层的约束函数惩罚到目标函数,使得下层问题近似为一系列无约束优化问题.然后使用KKT条件替换无约束的下层问题的最优解集,那么双层规划问题被一系列松弛的单层问题近似.文中设计了一种光滑的序列二次规划算法求解该松弛问题,并证明了当罚因子趋近于0时,该算法生成的迭代点列收敛到双层规划问题的弱稳定点.数值实验验证了算法的可行性.

模型预测控制技术在婴配乳粉配料优化中应用

为降低婴幼儿配方乳粉生产过程中的营养成分含量波动,本研究基于模型预测控制(MPC)算法和序列二次规划(SQP)算法构建婴幼儿配方乳粉配料优化控制模型,进行配料过程营养成分含量预测、反馈校正和滚动优化。结果表明,通过配料优化控制模型模拟干预100批次乳粉生产过程后,蛋白质、脂肪和碳水化合物含量的标准差分别从5.18、4.91、5.86 g·kg^(-1)降低至1.01、1.22和1.33 g·kg^(-1),验证了配料优化控制模型的有效性。本研究可为实现婴幼儿配方乳粉的配料优化控制提供参考。

抗干扰场景下基于LPI的资源优化分配

为了使雷达能够应对干扰场景并提高信号的LPI性能,本文提出了一种新型资源分配方案,而所研究的具体干扰类型为欺骗性干扰。首先,推导出欺骗性距离在三维情况下的CRLB。然后,依据CRLB建立抗干扰下的资源分配问题,具体来说,将总功率消耗量作为目标函数,并将CRLB作为性能约束,通过调配传感器资源、功率资源和带宽资源使雷达满足预定性能约束的前提下抑制总功率数值。为了求解所提出的优化问题,本文有针对性地先求解属于整数规划范畴的传感器资源分配问题,然后采用循环形式的SQP算法求解功率和带宽联合分配问题。最终的仿真结果显示,与仅优化功率的分配方案相比,新方案的功率消耗量低于该方案的50.0%,验证了新方案在降低总功率方面的可行性。此外,仿真实验中又与非线性规划遗传算法作对比,简单说明本文算法的优势和不足。最后给出了截获接收机处的功率对比结果和分析,结果显示出最大功率成分的有效下降,从而提高了信号的LPI特性。

Applying Analytical Derivative and Sparse Matrix Techniques to Large-Scale Process Optimization Problems

The performance of analytical derivative and sparse matrix techniques applied to a traditional dense sequential quadratic programming (SQP) is studied, and the strategy utilizing those techniques is also presented.Computational results on two typical chemical optimization problems demonstrate significant enhancement in efficiency, which shows this strategy is promising and suitable for large-scale process optimization problems.