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Hybrid improved Sparrow Search Algorithm and sequential quadratic programming for solving the cost minimization of a hybrid photovoltaic, diesel generator, and battery energy storage system

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2025年第1期47卷 6237-6253页

作者： Tian, Hao Wang, Keqing Yu, Bing Song, Changpo Jermsittiparsert, Kittisak Nanjing Univ Informat Sci & Technol Binjiang Coll Nanjing Jiangsu Peoples R China Tsinghua Univ Dept Elect Engn State Key Lab Control & Simulat Power Syst & Gener Beijing Peoples R China Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Duy Tan Univ Fac Humanities & Social Sci Da Nang Vietnam Henan Univ Econ & Law MBA Sch Henan Peoples R China

In this paper, a hybrid photovoltaic/battery/diesel generator power generation system is designed and optimized to power a network of a remote village in China. The purpose of the system designed here is to lessen the fuel costs of the system dependent on the load demand and some constraints. Therefore, the idea comprises an optimization problem that is solved here based on a new optimization policy by an Improved design of Sparrow Search Algorithm (imSSA) and the sequential quadratic programming (SQP). The results of the proposed method are analyzed in different seasons and also on weekdays and weekends to show their effect on the operational cost of the PV/Diesel BESS system. Final results indicate that in both winter and summer, the weekday costs are lesser than weekend fuel costs. Furthermore, the summer fuel cost is lesser than the winter fuel costs which is because of lesser demand in summer and the higher levels of summer radiation imply less usage of auxiliary origins. The results show that at the weekends cost more than the weekday. For instance, for the proposed mode, the winter weekends and the weekdays' costs are US$12.1 and US$10.2, respectively, or the summer weekends and the weekdays' costs are US$7.3 andUS$5.7, respectively.

关键词： Photovoltaic diesel generator battery energy storage system sequential quadratic programming improved sparrow search algorithm

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Toolpath optimization based on sequential quadratic programming for five-axis flat-end milling with hybrid robots

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JOURNAL OF MANUFACTURING PROCESSES 2025年 141卷 1619-1630页

作者： Zhang, Shiqi Ding, Ye Shanghai Jiao Tong Univ Sch Mech Engn State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China

Optimal toolpath generation is crucial for improving machining efficiency and surface finish quality. However, this task becomes challenging in the context of robotic flat-end milling, where both the contact geometry of cutters on free-form surfaces and robot kinematics need to be addressed. This paper introduces a toolpath optimization framework for flat-end milling with five-axis hybrid robots. The proposed method integrates joint path smoothness and machining width into the objective function, and it simultaneously optimizes the feed direction and tool orientation of the flat-end cutter by including feed angles, inclination angles, and tilt angles as optimization variables. The analytical gradient of the optimization problem is derived by applying differential geometry of surfaces. Based on this gradient derivation, sequential quadratic programming is employed to efficiently solve the original nonconvex problem, resulting in an optimized toolpath that exhibits both improved path smoothness and increased machining width. Feedrate scheduling of the toolpath, which considers workspace and joint space constraints, demonstrates that machining time is reduced for hybrid robots characterized by highly nonlinear kinematics. Simulations and experimental validations further confirm the method's capability to enhance machining efficiency.

关键词： Toolpath optimization Flat-end milling sequential quadratic programming Hybrid robot

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sequential quadratic programming enhanced backtracking search algorithm

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Frontiers of Computer Science 2018年第2期12卷 316-330页

作者： Wenting ZHAO Lijin WANG Yilong YIN Bingqing WANG Yuchun TANG School of Computer Science and Technology Shandong University Jinan 250101 China College of Computer and Information Science Fujian Agriculture and Forestry University Fuzhou 350002 China Research Center for Sectional and Imaging Anatomy Shandong University School of Medicine Jinan 250012 China

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.

关键词： numerical optimization backtracking search algorithm sequential quadratic programming local search

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

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Journal of Systems Engineering and Electronics 2023年第1期34卷 129-140页

作者： SONG Xiaocheng WANG Jiangtao WANG Jun SUN Liang FENG Yanghe LI Zhi Beijing Institute of Electronic Engineering China Aerospace Science and Industry Corporation LimitedBeijing 100039China School of Intelligence Science and Technology University of Science and Technology BeijingBeijing 100083China Institute of Artificial Intelligence University of Science and Technology BeijingBeijing 100083China School of Systems Engineering National University of Defense TechnologyChangsha 410073China

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.

关键词： non-cooperative target distributed hybrid processing multiple constraint minimum defense cost sequential quadratic programming

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sequential quadratic programming method with a global search strategy on the cutting-stock problem with rotatable polygons

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JOURNAL OF INTELLIGENT MANUFACTURING 2012年第3期23卷 787-795页

作者： Yu, M. T. Lin, T. Y. Hung, C. Natl Def Univ Dept Mechatron Energy & Aerosp Engn Tao Yuan 335 Taiwan Natl Chiao Tung Univ Dept Mech Engn Hsinchu 300 Taiwan

The cutting-stock problem, which considers how to arrange the component profiles on the material without overlaps, can increase the utility rate of the sheet stock. It is thus a standard constrained optimization problem. In some applications the components should be placed with specific orientations, but in others the components may be placed with any orientation. In general, the methods used to solve the cutting-stock problem usually have global search strategies to improve the solution, such as the Genetic Algorithm and the Simulated Annealing Algorithm. Unfortunately, many parameters, such as the temperature and the cooling rate of the Simulated Annealing method and the mutation rate of the Genetic Algorithm, have to be set and different settings of these parameters will strongly affect the result. This study formulates the cutting-stock problem as an optimization problem and solves it by the SQP method. The proposed method will make it easy to consider different orientations of components. This study also presents a global search strategy for which the parameter setting is easy.

关键词： Cutting-stock problem Material saving Rotatable sequential quadratic programming Global optimization

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sequential quadratic programming based differential evolution algorithm for optimal power flow problem

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IET GENERATION TRANSMISSION & DISTRIBUTION 2011年第11期5卷 1149-1154页

作者： Sivasubramani, S. Swarup, K. S. Indian Inst Technol Dept Elect Engn Chennai 600036 Tamil Nadu India

This study proposes a hybrid algorithm combining sequential quadratic programming (SQP) and differential evolution (DE) algorithm for solving the optimal power flow (OPF) problem. In this hybrid method, SQP is used to generate an individual, which is a member of an initial population, for DE algorithm. Having generated an individual by SQP, which will be nearer to the optimal solution, DE algorithm can reach the optimal solution more effectively than the classical evolutionary algorithms can. The proposed method has been used to solve the OPF problem on the standard IEEE 30- and IEEE 118-bus test systems to validate the effectiveness. Two different objectives, namely fuel cost considering valve-point effects and the transmission line losses, have been considered. The simulation results obtained from the proposed hybrid method reveal that this algorithm gives better solution for the problem having more non-convexity.

关键词： evolutionary computation differential evolution algorithm quadratic programming sequential quadratic programming DE algorithm Power transmission, distribution and supply load flow valve-point effect transmission line loss IEEE 118-bus test system OPF problem SQP IEEE 30-bus test system optimal power flow problem Optimisation techniques

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sequential quadratic-programming FOR CERTAIN PARAMETER-IDENTIFICATION PROBLEMS

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MATHEMATICAL programming 1991年第3期51卷 281-305页

作者： KELLEY, CT WRIGHT, SJ ARGONNE NATL LAB DIV MATH & COMP SCIARGONNEIL 60439 N CAROLINA STATE UNIV DEPT MATHRALEIGHNC 27695

We analyze the method of sequential quadratic programming for equality constrained minimization problems in Hilbert spaces of functions, and for the discrete approximations of such problems in the context of an elliptic parameter identification problem. We show how the discretization can be constructed so as to preserve the convergence behavior of the iterates for the infinite dimensional problem in the finite dimensional approximations. We use the structure of the parameter identification problem to reduce the size of the linear system for the SQP step and verify nondegeneracy of the constraints.

关键词： sequential quadratic programming PARAMETER IDENTIFICATION

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sequential quadratic programming for large-scale nonlinear optimization

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2000年第1-2期124卷 123-137页

作者： Boggs, PT Tolle, JW Sandia Natl Labs Comp Sci & Math Res Dept Livermore CA 94550 USA Univ N Carolina Dept Operat Res & Math Chapel Hill NC 27514 USA

The sequential quadratic programming (SQP) algorithm has been one of the most successful general methods for solving nonlinear constrained optimization problems. We provide an introduction to the general method and show its relationship to recent developments in interior-point approaches, emphasizing large-scale aspects. (C) 2000 Elsevier Science B.V. All rights reserved.

关键词： sequential quadratic programming nonlinear optimization Newton methods interior-point methods local trust-region methods convergence global convergence

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sequential quadratic programming (SQP) for optimal control in direct numerical simulation of turbulent flow

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JOURNAL OF COMPUTATIONAL PHYSICS 2014年 256卷 1-16页

作者： Badreddine, Hassan Vandewalle, Stefan Meyers, Johan Katholieke Univ Leuven Dept Mech Engn B-3001 Louvain Belgium Katholieke Univ Leuven Dept Comp Sci B-3001 Louvain Belgium

The current work focuses on the development and application of an efficient algorithm for optimization of three-dimensional turbulent flows, simulated using Direct Numerical Simulation (DNS) or Large-Eddy Simulations, and further characterized by large-dimensional optimization-parameter spaces. The optimization algorithm is based on sequential quadratic programming (SQP) in combination with a damped formulation of the limited-memory BFGS method. The latter is suitable for solving large-scale constrained optimization problems whose Hessian matrices cannot be computed and stored at a reasonable cost. We combine the algorithm with a line-search merit function based on an L-1-norm to enforce the convergence from any remote point. It is first shown that the proposed form of the damped L-BFGS algorithm is suitable for solving equality constrained Rosenbrock type functions. Then, we apply the algorithm to an optimal-control test problem that consists of finding the optimal initial perturbations to a turbulent temporal mixing layer such that mixing is improved at the end of a simulation time horizon T. The controls are further subject to a non-linear equality constraint on the total control energy. DNSs are used to resolve all turbulent scales of motion, and a continuous adjoint formulation is employed to calculate the gradient of the cost functionals. We compare the convergence speed of the SQP L-BFGS algorithm to a conventional non-linear conjugate-gradient method (i.e. the current standard in DNS-based optimal control), and find that the SQP algorithm is more than an order of magnitude faster than the conjugate-gradient method. (C) 2013 Elsevier Inc. All rights reserved.

关键词： sequential quadratic programming Damped limited-memory BFGS Turbulent mixing layer Optimal control Direct Numerical Simulations Adjoint equations

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sequential quadratic programming and analytic hierarchy process for nonlinear multiobjective optimization of a hydropower network

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OPTIMAL CONTROL APPLICATIONS & METHODS 2010年第4期31卷 351-364页

作者： Moosavian, S. Ali A. Ghaffari, Ali Salimi, Amir KN Toosi Univ Technol Dept Mech Engn Adv Robot & Automated Syst ARAS Lab Tehran Iran

In this paper, an optimal annual scheduling for power generation (i.e. rule curves and volume of water releases) in serial or parallel hydropower plants is developed. Multiobjective programming and weighted sum method are used to convert a multiobjective problem to a single objective one Furthermore, to obtain viable alternatives under existing uncertainty, some random weight vectors in the whole weighting space are generated and for each weight vector an optimal solution is found using sequential quadratic programming (SQP). Then, analytic hierarchy process (AHP) is used to select the best solution according to a given criterion, and determine the most preferred one. Besides, this method does not require choosing a priori preference for the objective function, thus making an ideal tool for handling complicated multiobjective models and easy to use with the aid of computer programs. Combination of multiobjective optimization and multicriteria decision analysis (MCDA) is an integrated methodology that is capable of dealing with complex water management problems. Various scenarios for dry, median, and wet years are assumed based on stochastic flows from external sources (e.g. flooding rivers, unexpected rains, etc.) coming into each reservoir, and turbine power generation is obtained from hill diagrams provided by the manufacturer. The application of this methodology is illustrated in a case study for optimal scheduling of Karoon River Basin, where the decision support system and optimization routines are implemented in MATLAB. The total energy productions for 10 optimized solutions under dry, median, and wet scenarios (generated from forty-year historical inflow records) are calculated, and specific water consumption for each reservoir is obtained in different months. This can significantly help decision makers to have an optimal and more intelligent management over energy productions in hydropower plants and associated thermal power plants Copyright (C) 2009 John Wi

关键词： multiobjective optimization stochastic-weighted sum method sequential quadratic programming multicriteria decision analysis analytical hierarchy process

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