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Active-set sequential quadratic programming with variable probabilistic constraint evaluations for optimization problems under non-Gaussian uncertainties

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2010年第C6期224卷 1273-1285页

作者： Chan, K-Y Huang, Y-C Natl Cheng Kung Univ Dept Mech Engn Tainan 70101 Taiwan

Design optimization under random uncertainties are formulated as problems with probabilistic constraints. Calculating these constraints presents a major challenge in the optimization. While most research concentrates on uncertainties that are Gaussian, a great number of uncertainties in the environment are non-Gaussian. In this work, various reliability analyses for non-Gaussian uncertainties within a sequential quadratic programming framework are integrated. An analytical reliability contour (RC) is first constructed in the design space to indicate the minimal distance from the feasible boundary of a design at a desired reliability level. A safe zone contour is then created so as to provide a quick estimate of the RC. At each design iteration reliability analyses of different accuracies are selected based on the level needed, depending on the activity of a constraint. For problems with a large number of constraints and relatively few design variables, such as structural problems, the active set strategies significantly improve efficiency, as demonstrated in the examples.

关键词： reliability-based design optimization non-Gaussian random parameters reliability contour active set strategies sequential quadratic programming

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Nonlinear model predictive control via feasibility-perturbed sequential quadratic programming

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2004年第1期28卷 87-121页

作者： Tenny, MJ Wright, SJ Rawlings, JB Univ Wisconsin Dept Chem Engn Madison WI 53706 USA Univ Wisconsin Dept Comp Sci Madison WI 53706 USA

Model predictive control requires the solution of a sequence of continuous optimization problems that are nonlinear if a nonlinear model is used for the plant. We describe briefly a trust-region feasibility-perturbed sequential quadratic programming algorithm ( developed in a companion report), then discuss its adaptation to the problems arising in nonlinear model predictive control. Computational experience with several representative sample problems is described, demonstrating the effectiveness of the proposed approach.

关键词： model predictive control nonlinear programming sequential quadratic programming

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Solution of the 2-dimensional Bratu problem using neural network, swarm intelligence and sequential quadratic programming

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NEURAL COMPUTING & APPLICATIONS 2014年第7-8期25卷 1723-1739页

作者： Raja, Muhammad Asif Zahoor Ahmad, Siraj-ul-Islam Samar, Raza COMSATS Inst Informat Technol Dept Elect Engn Attock Pakistan Pakistan Inst Engn & Appl Sci Islamabad Pakistan COMSATS Inst Informat Technol Dept Phys Islamabad Pakistan Mohammad Ali Jinnah Univ Islamabad Pakistan

In this paper, stochastic techniques have been developed to solve the 2-dimensional Bratu equations with the help of feed-forward artificial neural networks, optimized with particle swarm optimization (PSO) and sequential quadratic programming (SQP) algorithms. A hybrid of the above two algorithms, referred to as the PSO-SQP method is also studied. The original 2-dimensional equations are solved by first transforming them into equivalent one-dimensional boundary value problems (BVPs). These are then modeled using neural networks. The optimization problem for training the weights of the network has been addressed using particle swarm techniques for global search, integrated with an SQP method for rapid local convergence. The methodology is evaluated by applying on three different test cases of BVPs for the Bratu equations. Monte Carlo simulations and extensive analyses are carried out to validate the accuracy, convergence and effectiveness of the schemes. A comparative study of proposed results is made with available exact solution, as well as, reported numerical results.

关键词： 2-Dimensional Bratu equations Neural networks Particle swarm optimization Nonlinear ODEs Memetic computing sequential quadratic programming

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Power recovery of a variable nozzle turbocharged diesel engine at high altitude by response surface methodology and sequential quadratic programming

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2019年第4期233卷 810-823页

作者： Wang, Jun Shen, Lizhong Bi, Yuhua Liu, Shaohua Wan, Mingding Kunming Univ Sci & Technol Yunnan Prov Key Lab Internal Combust Engines 727 Jingming South Rd Kunming 650500 Yunnan Peoples R China

Based on a review of the research methods about diesel engine performance recovery at high altitude and an experimental investigation, by optimizing variable nozzle turbocharger (VNT) and fuel supply system calibration parameters a novel method is proposed to enhance the performance of a turbocharged diesel engine at high altitude. At an altitude of 1920 m, four calibration parameters deeply affecting performance of the diesel engine were selected at the rated power condition, that is, injection quantity, injection timing, injection pressure, and VNT nozzle opening. In order to reduce thermal load of the diesel engine running in the plateau environment, reasonable coded levels of Design of Experiments (DoE) factors were chosen, and an experimental design matrix was selected based on the Box-Behnken design. The interaction effects of the four calibration parameters on engine performance were investigated using the response surface methodology. Power recovery optimization was carried out by means of sequential quadratic programming under a minimum smoke limit and durability constraints. The results show that this performance optimization method can effectively recover engine performance at high altitude. Moreover, it can, to an extent, alleviate the problems such as deterioration of fuel consumption and high thermal load induced by the rise in elevation. With optimized calibration parameters, the rated power of the diesel engine at an altitude of 1920 m proved to be recovered to that at sea level, and there was an increase of brake specific fuel consumption by less than 3% compared with that in the plain area, which met the performance and durability requirements for general turbocharged internal combustion engines at altitudes lower than 2000 m.

关键词： Diesel engine variable nozzle turbocharger high altitude power recovery response surface methodology sequential quadratic programming

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An adaptive stochastic sequential quadratic programming with differentiable exact augmented lagrangians

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MATHEMATICAL programming 2023年第1-2期199卷 721-791页

作者： Na, Sen Anitescu, Mihai Kolar, Mladen Univ Calif Berkeley Dept Stat Berkeley CA 94720 USA Int Comp Sci Inst Berkeley CA 94704 USA Argonne Natl Lab Math & Comp Sci Div Lemont IL USA Univ Chicago Booth Sch Business Chicago IL 60637 USA

We consider solving nonlinear optimization problems with a stochastic objective and deterministic equality constraints. We assume for the objective that its evaluation, gradient, and Hessian are inaccessible, while one can compute their stochastic estimates by, for example, subsampling. We propose a stochastic algorithm based on sequential quadratic programming (SQP) that uses a differentiable exact augmented Lagrangian as the merit function. To motivate our algorithm design, we first revisit and simplify an old SQP method Lucidi (J. Optim. Theory Appl. 67(2): 227-245, 1990) developed for solving deterministic problems, which serves as the skeleton of our stochastic algorithm. Based on the simplified deterministic algorithm, we then propose a non-adaptive SQP for dealing with stochastic objective, where the gradient and Hessian are replaced by stochastic estimates but the stepsizes are deterministic and prespecified. Finally, we incorporate a recent stochastic line search procedure Paquette and Scheinberg (SIAM J. Optim. 30(1): 349-376 2020) into the non-adaptive stochastic SQP to adaptively select the random stepsizes, which leads to an adaptive stochastic SQP. The global "almost sure" convergence for both non-adaptive and adaptive SQP methods is established. Numerical experiments on nonlinear problems in CUTEst test set demonstrate the superiority of the adaptive algorithm.

关键词： Stochastic optimization sequential quadratic programming Augmented Lagrangian Exact penalty

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A piecewise linear chaotic map and sequential quadratic programming based robust hybrid particle swarm optimization

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INFORMATION SCIENCES 2013年 218卷 85-102页

作者： Xu, Wenxing Geng, Zhiqiang Zhu, Qunxiong Gu, Xiangbai Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Sinopec Engn Beijing 100029 Peoples R China

This paper presents a novel robust hybrid particle swarm optimization (RHPSO) based on piecewise linear chaotic map (PWLCM) and sequential quadratic programming (SQP). The aim of the present research is to develop a new single-objective optimization approach which requires no adjustment of its parameters for both unconstrained and constrained optimization problems. This novel algorithm makes the best of ergodicity of PWLCM to help PSO with the global search while employing the SQP to accelerate the local search. Five unconstrained benchmarks, eighteen constrained benchmarks and three engineering optimization problems from the literature are solved by using the proposed hybrid approach. The simulation results compared with other state-of-art methods demonstrate the effectiveness and robustness of the proposed RHPSO for both unconstrained and constrained problems of different dimensions. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Particle swarm optimization sequential quadratic programming Chaotic optimization Piecewise linear chaotic map Constrained optimization

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Feasibility refinement in sequential quadratic programming using parametric sensitivity analysis

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OPTIMIZATION METHODS & SOFTWARE 2017年第4期32卷 754-769页

作者： Geffken, Soeren Bueskens, Christof Univ Bremen Ctr Ind Math Bremen Germany

In this paper we present results that extend the sequential quadratic programming (SQP) algorithm with an additional feasibility refinement based on parametric sensitivity derivatives. The refinement is applicable without restriction on the problem dimensions in sparse SQP solvers. Parametric sensitivity analysis is a tool for post optimality analysis of the solution of a nonlinear optimization problem. For the refinement approach we apply this technique on the quadratic subproblems in order to improve the overall algorithm. The sensitivity derivatives required for this approach can be computed without noticeable computational effort as the system of linear equations to be solved coincides with the system already solved for the search direction computation. For similar algorithms in the context of post optimality analysis a linear rate of convergence has been proven and therefore an extrapolation method is applied to speed up the process. The presented algorithm has been integrated into the nonlinear program (NLP) solver WORHP and we perform a numerical study to evaluate different termination criteria for the proposed algorithm. Furthermore, numerical results on the CUTEst test set are shown.

关键词： nonlinear optimization parametric sensitivity analysis sequential quadratic programming

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Bio-inspired heuristics hybrid with sequential quadratic programming and interior-point methods for reliable treatment of economic load dispatch problem

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NEURAL COMPUTING & APPLICATIONS 2019年第1-Sup期31卷 447-475页

作者： Raja, Muhammad Asif Zahoor Ahmed, Usman Zameer, Aneela Kiani, Adiqa Kausar Chaudhary, Naveed Ishtiaq COMSATS Inst Informat Technol Dept Elect Engn Attock Pakistan Pakistan Inst Engn & Appl Sci Dept Comp & Informat Sci Nilore Pakistan Fed Urdu Univ Arts Sci & Technol Dept Econ Islamabad Pakistan Int Islamic Univ Dept Elect Engn Islamabad Pakistan

In the present study, bio-inspired computational heuristics are exploited for finding the solution of economic load dispatch (ELD) problem with valve point loading effect using variants of genetic algorithm (GA) hybrid with sequential quadratic programming (SQP) and interior-point algorithms (IPAs). Variants of GAs are constructed using different sets of routines for its fundamental operators in order to explore the entire search space for global optimum solutions while SQP and IPA are integrated with GAs for rapid local convergence. Nine variants of each design scheme based on GAs, GA-SQP and GA-IPAs are applied on three different ELD problems of thermal power plant systems. Comparative studies of the proposed schemes are performed through the results of statistical performance indices in order to establish the worth and effectiveness in terms of accuracy, convergence and complexity measures.

关键词： Economic load dispatch Hybrid computing Evolutionary computations Genetic algorithms sequential quadratic programming Interior-point algorithms

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Adaptive fractional-order nonsingular terminal sliding mode control and sequential quadratic programming torque distribution for lateral stability of FWID-EVs with actuator constraints

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ISA TRANSACTIONS 2024年 150卷 208-222页

作者： Hua, Xingqi Wong, Pak Kin Zhao, Jing Xie, Zhengchao Univ Macau Dept Electromech Engn Taipa 999078 Macau Sar Peoples R China South China Univ Technol Sch Mech & Automot Engn Guangzhou 510641 Peoples R China

This paper proposes a novel sliding mode control (SMC) algorithm for direct yaw moment control of four-wheel independent drive electric vehicles (FWID-EVs). The algorithm integrates adaptive law theory, fractional -order theory, and nonsingular terminal sliding mode reaching law theory to reduce chattering, handle uncertainty, and avoid singularities in the SMC system. A sequential quadratic programming (SQP) method is also proposed to optimize the yaw moment distribution under actuator constraints. The performance of the proposed algorithm is evaluated by a hardware -in -the -loop test with two driving maneuvers and compared with two existing SMC-based schemes together with the cases with the change of vehicle parameters and disturbances. The results demonstrate that the proposed algorithm can eliminate chattering and achieve the best lateral stability as compared with the existing schemes.

关键词： Sliding mode control (SMC) Direct yaw moment control Fractional-order sequential quadratic programming

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Optimization investigation on variable-density multi-layer insulation coupled with vapor-cooled shield for liquid hydrogen tank based on sequential quadratic programming

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APPLIED THERMAL ENGINEERING 2024年 252卷

作者： Li, Ke Wen, Jian Xin, Biping Xi An Jiao Tong Univ Sch Energy & Power Engn Dept Refrigerat & Cryogen Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ State Key Lab Multiphase Flow Power Engn Xian 710049 Peoples R China

The MLI (multi-layer insulation), commonly used in liquid hydrogen tank, acts to reduce HLF (heat leakage flux) into the tank and slow down tank pressurization. It is usually optimally designed into three or four regions, with the same RS spacing in every region, which is regarded as the discontinuous variable density and called VDMLI (variable-density multi-layer insulation). However, the VCS (vapor-cooled shield) and P-O (para-to-ortho hydrogen conversion) in VDMLI are not considered in the optimal design of VDMLI. Therefore, a kind of nonlinear programming algorithm named SQP (sequential quadratic programming), in which HLF solver of liquid hydrogen tank is embedded, is firstly introduced to obtain the optimized distribution of RS (radiative shield) spacing to minimize HLF of liquid hydrogen storage tank. The HLF solver is a numerical model of VDMLI coupled with VCS and P-O, and SQP can be regarded as the optimization process of searching for an optimal solution in high dimensional space, under inequality constraints. The calculation results show that the best SN (serial number) of RS for SVCS (single vapor-cooled shield) setting to minimize HLF, in three cases of before optimization with P-O, after optimization with P-O, and after optimization without P-O is 16, 11, and 14 respectively. With P-O, the minimum HLF after optimization is reduced by 8.45% compared with the minimum HLF before optimization. When SVCS is set at the 4th RS, P-O leads to the most decreasing range of HLF, which is 28.3%, while the optimization process leads to the most decreasing range of 48.1% under condition that SVCS is set at the 2nd RS and P-O is coupled. Under condition of no P-O, after optimization, the minimum HLF occurs when the SN of RS where DVCS (double vapor-cooled shield) is set is (8,21), and the minimum HLF is reduced by 9.0%, compared to that before optimization with DVCS, and is reduced by 50.43% compared to that after optimization with SVCS.

关键词： Liquid hydrogen tank Variable -density multi -layer insulation Vapor -cooled shield Para-to-ortho hydrogen conversion sequential quadratic programming Optimization

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