Design optimization of large-scale structures with sequential linear programming

2002 ◽  
Vol 216 (8) ◽  
pp. 799-811 ◽  
Author(s):  
L Lamberti ◽  
C Pappalettere
Keyword(s):  
Linear Programming ◽  
Design Optimization ◽  
Large Scale ◽  
Optimization Technique ◽  
Computational Cost ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Truss Structures ◽  
Large Set ◽  
Sequential Linear Programming

Design optimization of complex structures entails tasks that oppose the usual constraints on time and computational resources. However, using optimization techniques is very useful because it allows engineers to obtain a large set of designs at low computational cost. Among the different optimization methods, sequential linear programming (SLP) is very popular because of its simplicity and because linear solvers (e.g. Simplex) are easily available. In spite of the inherent theoretical simplicity, well-coded SLP algorithms may outperform more sophisticated optimization methods. This paper describes the experience obtained in the design optimization of large-scale truss structures and beams with SLP-based algorithms. Sizing and configuration problems of structures under multiple loading conditions with up to 1000 design variables and 3500 constraints are considered. The relative performance and merits of some SLP-based algorithms are compared and the efficiency of an advanced SLP-based algorithm called ILEAML (improved linearization error amplitude move limits) is tested. ILEAML is also compared to the sequential quadratic programming (SQP) method, which is considered by theoreticians as probably the best theoretically founded optimization technique.

Linear Programming Approach for Power System State Estimation Using Upper Bound Optimization Techniques

2010 ◽  
Vol 11 (3) ◽  
Author(s):  
Thukaram Dhadbanjan ◽  
Seshadri Sravan Kumar Vanjari
Keyword(s):  
Linear Programming ◽  
State Estimation ◽  
Upper Bound ◽  
Measurement Errors ◽  
Large Scale ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Programming Approach ◽  
Linear Programming Approach ◽  
Bound Optimization

State estimation plays an important role in real time security monitoring and control of power systems. There are many problems in the implementation of state estimator for large scale networks due to measurement errors, weights given and the numerical ill-conditioning associated with the solution techniques. In this paper a new formulation using linear programming approach is presented. The formulation is devoid of weights and errors associated with the measurements are taken care of in constraints. The non linear problem is linearized at previous operating state and constraints are set up using flow mismatches. The implementation of the formulation exploits sparse features of the network matrices and avoids matrix inversions. Upper bound optimization technique is employed to solve the linear programming problem. Illustration of the proposed approach on sample 3-bus and 6-bus systems and a practical Indian Southern grid 72 bus equivalent system are presented.

scholarly journals Metamodel-based multidisciplinary design optimization methods for aerospace system

Astrodynamics ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 185-215
Author(s):  
Renhe Shi ◽  
Teng Long ◽  
Nianhui Ye ◽  
Yufei Wu ◽  
Zhao Wei ◽  
...  
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Computational Cost ◽  
Optimization Methods ◽  
Problem Formulation ◽  
Black Box ◽  
Optimization Techniques ◽  
Multidisciplinary Optimization ◽  
Multidisciplinary Design ◽  
Aerospace Systems

AbstractThe design of complex aerospace systems is a multidisciplinary design optimization (MDO) problem involving the interaction of multiple disciplines. However, because of the necessity of evaluating expensive black-box simulations, the enormous computational cost of solving MDO problems in aerospace systems has also become a problem in practice. To resolve this, metamodel-based design optimization techniques have been applied to MDO. With these methods, system models can be rapidly predicted using approximate metamodels to improve the optimization efficiency. This paper presents an overall survey of metamodel-based MDO for aerospace systems. From the perspective of aerospace system design, this paper introduces the fundamental methodology and technology of metamodel-based MDO, including aerospace system MDO problem formulation, metamodeling techniques, state-of-the-art metamodel-based multidisciplinary optimization strategies, and expensive black-box constraint-handling mechanisms. Moreover, various aerospace system examples are presented to illustrate the application of metamodel-based MDOs to practical engineering. The conclusions derived from this work are summarized in the final section of the paper. The survey results are expected to serve as guide and reference for designers involved in metamodel-based MDO in the field of aerospace engineering.

scholarly journals Solving the Real Power Limitations in the Dynamic Economic Dispatch of Large-Scale Thermal Power Units under the Effects of Valve-Point Loading and Ramp-Rate Limitations

2021 ◽  
Vol 13 (3) ◽  
pp. 1274
Author(s):  
Loau Al-Bahrani ◽  
Mehdi Seyedmahmoudian ◽  
Ben Horan ◽  
Alex Stojcevski
Keyword(s):  
Large Scale ◽  
Thermal Power ◽  
Optimization Technique ◽  
Economic Dispatch ◽  
Pso Algorithm ◽  
Search Space ◽  
Economic Benefits ◽  
Optimization Techniques ◽  
Ramp Rate ◽  
Dynamic Economic Dispatch

Few non-traditional optimization techniques are applied to the dynamic economic dispatch (DED) of large-scale thermal power units (TPUs), e.g., 1000 TPUs, that consider the effects of valve-point loading with ramp-rate limitations. This is a complicated multiple mode problem. In this investigation, a novel optimization technique, namely, a multi-gradient particle swarm optimization (MG-PSO) algorithm with two stages for exploring and exploiting the search space area, is employed as an optimization tool. The M particles (explorers) in the first stage are used to explore new neighborhoods, whereas the M particles (exploiters) in the second stage are used to exploit the best neighborhood. The M particles’ negative gradient variation in both stages causes the equilibrium between the global and local search space capabilities. This algorithm’s authentication is demonstrated on five medium-scale to very large-scale power systems. The MG-PSO algorithm effectively reduces the difficulty of handling the large-scale DED problem, and simulation results confirm this algorithm’s suitability for such a complicated multi-objective problem at varying fitness performance measures and consistency. This algorithm is also applied to estimate the required generation in 24 h to meet load demand changes. This investigation provides useful technical references for economic dispatch operators to update their power system programs in order to achieve economic benefits.

Crash worthiness design optimization using multipoint sequential linear programming

1996 ◽  
Vol 12 (4) ◽  
pp. 222-228 ◽  
Author(s):  
L. F. P. Etman ◽  
J. M. T. A. Adriaens ◽  
M. T. P. Slagmaat ◽  
A. J. G. Schoofs
Keyword(s):  
Linear Programming ◽  
Design Optimization ◽  
Sequential Linear Programming

scholarly journals Μέθοδοι σχεδιασμού-βελτιστοποίησης στις στροβιλομηχανές βασισμένες στους εξελικτικούς αλγορίθμους

2013 ◽  
Author(s):  
Στυλιανός Κυριάκου
Keyword(s):  
Design Optimization ◽  
Real World ◽  
Large Scale ◽  
Optimization Methods ◽  
Industrial Applications ◽  
Turn Around Time ◽  
Phd Thesis

The scope of this PhD thesis is to pΙopose a set of improvements to existingshape design-optimization methods in fluid dynamiοs based on EvolutionaryΑlgorithms (EAs) and demonstrate their effiοienοy in real-world applications.Though the proposed method and the developed EA-based software are bothgeneriο, this thesis foοuses on applicatiοns in the fields of hydrau1ic andthermal turbomaοhines. With the proposed a1gorithmic variants, theoptimization turn-around time is notiοeably reduοed with respeοt to that ofοonventional (reference, background) methods. Though the latter areοomputationally expensive, with the proposed add-ons, they becomeaffordable even for large-scale industrial applications.

scholarly journals Convex optimization techniques in compliant assembly simulation

2020 ◽  
Vol 21 (4) ◽  
pp. 1665-1690
Author(s):  
Maria Stefanova ◽  
Olga Minevich ◽  
Stanislav Baklanov ◽  
Margarita Petukhova ◽  
Sergey Lupuleac ◽  
...  
Keyword(s):  
Large Scale ◽  
Hessian Matrix ◽  
Linear Complementarity Problems ◽  
Contact Problems ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Active Set ◽  
Assembly Simulation ◽  
The Matrix ◽  
Compliant Parts

Abstract A special class of quadratic programming (QP) problems is considered in this paper. This class emerges in simulation of assembly of large-scale compliant parts, which involves the formulation and solution of contact problems. The considered QP problems can have up to 20,000 unknowns, the Hessian matrix is fully populated and ill-conditioned, while the matrix of constraints is sparse. Variation analysis and optimization of assembly process usually require massive computations of QP problems with slightly different input data. The following optimization methods are adapted to account for the particular features of the assembly problem: an interior point method, an active-set method, a Newton projection method, and a pivotal algorithm for the linear complementarity problems. Equivalent formulations of the QP problem are proposed with the intent of them being more amenable to the considered methods. The methods are tested and results are compared for a number of aircraft assembly simulation problems.

Rapid design optimization of antennas using variable-fidelity EM models and adjoint sensitivities

2016 ◽  
Vol 33 (7) ◽  
pp. 2007-2018 ◽  
Author(s):  
Slawomir Koziel ◽  
Adrian Bekasiewicz
Keyword(s):  
Design Optimization ◽  
Optimization Technique ◽  
Computational Cost ◽  
Trust Region ◽  
Simulation Models ◽  
Direct Optimization ◽  
Content Type ◽  
Variable Fidelity ◽  
Em Simulation ◽  
Conventional Methods

Purpose Development of techniques for expedited design optimization of complex and numerically expensive electromagnetic (EM) simulation models of antenna structures validated both numerically and experimentally. The paper aims to discuss these issues. Design/methodology/approach The optimization task is performed using a technique that combines gradient search with adjoint sensitivities, trust region framework, as well as EM simulation models with various levels of fidelity (coarse, medium and fine). Adaptive procedure for switching between the models of increasing accuracy in the course of the optimization process is implemented. Numerical and experimental case studies are provided to validate correctness of the design approach. Findings Appropriate combination of suitable design optimization algorithm embedded in a trust region framework, as well as model selection techniques, allows for considerable reduction of the antenna optimization cost compared to conventional methods. Research limitations/implications The study demonstrates feasibility of EM-simulation-driven design optimization of antennas at low computational cost. The presented techniques reach beyond the common design approaches based on direct optimization of EM models using conventional gradient-based or derivative-free methods, particularly in terms of reliability and reduction of the computational costs of the design processes. Originality/value Simulation-driven design optimization of contemporary antenna structures is very challenging when high-fidelity EM simulations are utilized for performance utilization of structure at hand. The proposed variable-fidelity optimization technique with adjoint sensitivity and trust regions permits rapid optimization of numerically demanding antenna designs (here, dielectric resonator antenna and compact monopole), which cannot be achieved when conventional methods are of use. The design cost of proposed strategy is up to 60 percent lower than direct optimization exploiting adjoint sensitivities. Experimental validation of the results is also provided.

Sequential Quadratic Programming

Acta Numerica ◽  
1995 ◽  
Vol 4 ◽  
pp. 1-51 ◽  
Author(s):  
Paul T. Boggs ◽  
Jon W. Tolle
Keyword(s):  
Quadratic Programming ◽  
Constrained Optimization ◽  
Sequential Quadratic Programming ◽  
Large Scale ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Public Domain ◽  
Large Set ◽  
Constrained Optimization Problems ◽  
Nonlinearly Constrained Optimization

Since its popularization in the late 1970s, Sequential Quadratic Programming (SQP) has arguably become the most successful method for solving nonlinearly constrained optimization problems. As with most optimization methods, SQP is not a single algorithm, but rather a conceptual method from which numerous specific algorithms have evolved. Backed by a solid theoretical and computational foundation, both commercial and public-domain SQP algorithms have been developed and used to solve a remarkably large set of important practical problems. Recently large-scale versions have been devised and tested with promising results.

Mode-Pursuing Sampling Method Using Discriminative Coordinate Perturbation for High-Dimensional Expensive Black-Box Optimization

2020 ◽  
Vol 143 (4) ◽  
Author(s):  
Yufei Wu ◽  
Teng Long ◽  
Renhe Shi ◽  
G. Gary Wang
Keyword(s):  
Design Optimization ◽  
Sampling Method ◽  
Sequential Sampling ◽  
Computational Cost ◽  
Optimization Methods ◽  
Black Box ◽  
High Dimensional ◽  
Beam Design ◽  
Convergence Performance ◽  
Coordinate Perturbation

Abstract This article presents a novel mode-pursuing sampling method using discriminative coordinate perturbation (MPS-DCP) to further improve the convergence performance of solving high-dimensional, expensive, and black-box (HEB) problems. In MPS-DCP, a discriminative coordinate perturbation strategy is integrated into the original mode-pursuing sampling (MPS) framework for sequential sampling. During optimization, the importance of variables is defined by approximated global sensitivities, while the perturbation probabilities of variables are dynamically adjusted according to the number of optimization stalling iterations. Expensive points considering both optimality and space-filling property are selected from cheap points generated by perturbing the current best point, which balances between global exploration and local exploitation. The convergence property of MPS-DCP is theoretically analyzed. The performance of MPS-DCP is tested on several numerical benchmarks and compared with state-of-the-art metamodel-based design optimization methods for HEB problems. The results indicate that MPS-DCP generally outperforms the competitive methods regarding convergence and robustness performances. Finally, the proposed MPS-DCP is applied to a stepped cantilever beam design optimization problem and an all-electric satellite multidisciplinary design optimization (MDO) problem. The results demonstrate that MPS-DCP can find better feasible optima with the same or less computational cost than the competitive methods, which demonstrates its effectiveness and practicality in solving real-world engineering problems.

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