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.

Filter-sequence of quadratic programming method with nonlinear complementarity problem function

A mechanism for proving global convergence in filter-SQP （sequence of quadratic programming） method with the nonlinear complementarity problem （NCP） function is described for constrained nonlinear optimization problem.We introduce an NCP function into the filter and construct a new SQP-filter algorithm.Such methods are characterized by their use of the dominance concept of multi-objective optimization,instead of a penalty parameter whose adjustment can be problematic.We prove that the algorithm has global convergence and superlinear convergence rates under some mild conditions.

基于SQP-GPMP2算法的移动机器人路径规划

针对高斯过程路径规划算法(GPMP2)处理非线性不等式约束能力有限、在复杂障碍物地图中易陷入局部极小值,进而产生碰撞的问题,结合序列二次规划(SQP)算法,提出了改进的SQP-GPMP2算法。首先,该算法从概率的角度将运动规划视为轨迹优化,得到初始轨迹状态;其次,引入碰撞代价函数,用来表示机器人和障碍物的碰撞代价关系;最后,使用SQP算法对轨迹进行迭代修正,保证轨迹的无碰撞和运动学合理性。仿真实验结果显示,相比GPMP2等算法,所提算法在不同尺寸迷宫上的规划成功率至少提高20个百分点,证明该算法在处理复杂约束能力和保证路径规划效率上具有优越性。

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。

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.

New sequential quadratic programming algorithm with consistent subproblems

One of the most interesting topics related to sequential quadratic programming algorithms is how to guarantee the consistence of all quadratic programming subproblems. In this decade, much work trying to change the form of constraints to obtain the consistence of the subproblems has been done The method proposed by De O. Panto-ja J F A and coworkers solves the consistent problem of SQP method, and is the best to the authors’ knowledge. However, the scale and complexity of the subproblems in De O. Pantoja’s work will be increased greatly since all equality constraints have to be changed into absolute form A new sequential quadratic programming type algorithm is presented by means of a special ε-active set scheme and a special penalty function. Subproblems of the new algorithm are all consistent, and the form of constraints of the subproblems is as simple as one of the general SQP type algorithms. It can be proved that the new method keeps global convergence and local superhnear convergence.

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.

基于改进PSO-SQP算法优化控制金氰化浸出过程

针对金氰化浸出过程,建立了生产成本最小为目标的优化模型,采用序列二次规划(SQP)算法与改进粒子群算法(PSO)相结合对模型进行求解,并通过对比优化前后金浸出率,验证了改进PSO-SQP算法的可行性。结果表明:相比于常规方法,改进PSO-SQP算法可有效降低迭代次数和生产成本;浸出过程优化后的金浸出率达97.03%,比优化前提高2.47%。改进PSO-SQP算法对金浸出过程优化控制具有一定的实用价值。

基于专家PID和SQP半自磨给矿控制方法及应用

在半自磨磨矿作业中,半自磨机的给矿通常是粒级分布较宽的固体物料,以实现利用物料自身做磨矿介质,在筒体内进行连续强烈地冲击磨剥达到粉磨的目的。宽粒级组成的固体物料则会导致矿量波动,从而导致控制难度增大,常规的PID控制算法无法满足现场实际需求。同时大多选矿厂半自磨机给矿设备为多台给料机联合布料,因为矿石自然分级等因素的影响,矿仓中的固体物料分布不均匀,存在各个给料机给矿块度和性质差异,给矿块度和矿石性质的巨大变化则会导致半自磨机功率的异常波动,严重影响了生产稳定。针对上述问题,设计开发一套基于专家PID和序列二次规划(SQP)的恒定给矿控制系统,实现给矿量及矿石块度分布稳定控制,达到半自磨机给矿控制恒定以及多给料机频率最优分配。

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。

基于序列二次规划算法的双有源桥变换器优化控制策略

针对双有源桥变换器在充电领域电路损耗大、传输效率低的问题,提出一种结合序列二次规划算法与双重移相控制实现回流功率优化的控制策略。首先,建立双有源桥变换器在双重移相控制时以回流功率最小值为目标函数的二次规划问题数学模型;然后,提出一种采用序列二次规划算法求解上述多约束、非线性的数学模型,在算法中输入目标、约束函数的雅可比矩阵和变换器的相关参数,通过迭代求解出满足双有源桥电路在双重移相控制下最优工况的目标参数;最后,通过仿真验证所提控制策略的有效性。

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GPU加速求解MINLP问题的SQP并行算法

针对确定性算法求解大型复杂混合整数非线性规划的时间不可接受问题,通过对序贯二次规划算法(SQP)和图形处理器(GPU)的架构特点分析,提出了基于GPU加速策略的并行化SQP算法。算法的主要思想是通过枚举法确定二元变量的取值,在保证取值完整的基础上,使用CPU+GPU的并行策略,同时运用大量线程进行非线性规划子问题的求解。算例的数值实验结果表明:本文所提出的算法较之传统串行计算具有较好的加速效果,特别适合求解二元变量较多,约束条件相对少的MINLP问题。