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Optimal power flow solution using improved harmony search method

APPLIED SOFT COMPUTING 2013年第5期13卷 2364-2374页

作者： Sinsuphan, Nampetch Leeton, Uthen Kulworawanichpong, Thanatchai Suranaree Univ Technol Sch Elect Engn Power Syst Res Unit Nakhon Ratchasima Thailand

This paper describes the improved harmony search method (IHS) to solve optimal power flow (OPF) problems. The harmony search is one of meta-heuristic search methods inspired by the improvisation of musicians developed by Geem (2001) [23]. The proposed algorithm was tested with five standard IEEE test systems (6-bus, 14-bus, 30-bus, 57-bus and 118-bus test systems). The tests were divided into smooth and non-smooth fuel-cost cases. The comparisons among solutions obtained by sequential quadratic programming (SQP), genetic algorithms (GA) and IHS were conducted. As revealed from the simulated results, the effectiveness of the IHS for solving OPF problems was confirmed. (C) 2013 Elsevier B. V. All rights reserved.

关键词： Optimal power flow Improved harmony search sequential quadratic programming Genetic algorithms

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An inexact ₁ penalty SQP algorithm for PDE-constrained optimization with an application to shape optimization in linear elasticity

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OPTIMIZATION METHODS & SOFTWARE 2013年第5期28卷 943-968页

作者： Hess, Wolfgang Ulbrich, Stefan Tech Univ Darmstadt Dept Math D-64293 Darmstadt Germany

We develop an optimization algorithm which is able to deal with inexact evaluations of the objective function. The proposed algorithm employs sequential quadratic programming with a line search that uses the (1) penalty function for an Armijo-like condition. Both the objective gradient computations for the quadratic subproblems and the objective function computations for the line search admit some inexactness which is controlled by the algorithm. We prove convergence results for the presented algorithm under suitable assumptions. The kind of optimization problems handled by the algorithm arises, for example, when we apply iterative solvers in the evaluation of the reduced objective function of a shape optimization problem subject to the linear elasticity equations. In the second part of this paper, we present an application of the algorithm to the shape optimization of steel profiles using nested iterations with adaptively refined meshes. It is shown how the inexactness in the algorithm translates to an acceptable residual of the state and adjoint state solutions in the iterative solver. We look at three different stopping criteria for the outer iterations. Numerical results are presented.

关键词： sequential quadratic programming line search inexact objective function evaluations shape optimization iterative solution engineering application 49M37 49Q10 74P10 90C55

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Application of gradient-based optimization methods for a rotor system with static stress, natural frequency,and harmonic response constraints

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2013年第6期47卷 951-962页

作者： Pugachev, Alexander O. Tech Univ Munich Inst Energy Syst D-85747 Garching Germany

This paper demonstrates the application of gradient-based optimization methods to the minimal weight design optimization of rotor systems. A nonlinear constrained optimization problem is considered. Design variables are inner radii and wall thicknesses of shaft sections. Constraints are imposed on torsional and equivalent stresses, natural frequencies, and unbalance response amplitudes. The sizing optimization problem is solved using a gradient projection method and a sequential quadratic programming technique. A typical turbine rotor system is considered. An in-house beam-based finite element method code is used for the prediction of static and dynamic characteristics of the rotor system. Analytical sensitivity analysis is performed for the static and harmonic equations using the adjoint method. Sensitivity coefficients for the natural frequencies are obtained directly from the quadratic eigenvalue problem. Results of several optimization runs with different constraint sets show a significant shaft weight reduction in comparison with the baseline configuration with all constraints being satisfied. The two optimization methods are compared and discussed in regard to their performance.

关键词： Sizing optimization sequential quadratic programming Gradient projection method Rotordynamics Turbine rotor

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Application of the GoRoSo Feedforward Algorithm to Compute the Gate Trajectories for a Quick Canal Closing in the Case of an Emergency

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JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING 2013年第12期139卷 1028-1036页

作者： Soler, Joan Gomez, Manuel Rodellar, Jose Gamazo, Pablo Tech Univ Catalonia Dept Appl Math 3 Barcelona 08036 Spain Univ Republ Water Dept Salto 50000 Uruguay Tech Univ Catalonia Dept Hydraul Coastal & Environm Engn Barcelona 08034 Spain Tech Univ Catalonia Dept Appl Math 3 Barcelona 08034 Spain

The canal delivery system in the Left Hemidelta area of the Ebro River in Spain consists of a tree-shaped net of open canals. The overall system can be quickly isolated in the case of an emergency by closing the upstream pool. Transients, in which the initial state is hydraulically far from the final state, are difficult to handle and cannot be made in only one gate movement in order to protect the canal lining. Therefore, they have to be as smooth as possible. GoRoSo is a feedforward control algorithm for irrigation canals based on sequential quadratic programming. With this tool, it is possible to calculate the gate trajectories that smoothly carry the canal from the initial state to the final state by keeping the water depth constant at checkpoints. The paper shows the efficient implementation of GoRoSo in both the closure and opening operations of the canal delivery system. (C) 2013 American Society of Civil Engineers.

关键词： Canals Free flow Algorithms Gates Feedforward canal control Free-surface flow sequential quadratic programming

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A GLOBALLY CONVERGENT STABILIZED SQP METHOD

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SIAM JOURNAL ON OPTIMIZATION 2013年第4期23卷 1983-2010页

作者： Gill, Philip E. Robinson, Daniel P. Univ Calif San Diego Dept Math La Jolla CA 92093 USA Johns Hopkins Univ Dept Appl Math & Stat Baltimore MD 21218 USA

sequential quadratic programming (SQP) methods are a popular class of methods for nonlinearly constrained optimization. They are particularly effective for solving a sequence of related problems, such as those arising in mixed-integer nonlinear programming and the optimization of functions subject to differential equation constraints. Recently, there has been considerable interest in the formulation of stabilized SQP methods, which are specifically designed to handle degenerate optimization problems. Existing stabilized SQP methods are essentially local in the sense that both the formulation and analysis focus on the properties of the methods in a neighborhood of a solution. A new SQP method is proposed that has favorable global convergence properties yet, under suitable assumptions, is equivalent to a variant of the conventional stabilized SQP method in the neighborhood of a solution. The method combines a primal-dual generalized augmented Lagrangian function with a flexible line search to obtain a sequence of improving estimates of the solution. The method incorporates a convexification algorithm that allows the use of exact second derivatives to define a convex quadratic programming (QP) subproblem without requiring that the Hessian of the Lagrangian be positive definite in the neighborhood of a solution. This gives the potential for fast convergence in the neighborhood of a solution. Additional benefits of the method are that each QP subproblem is regularized and the QP subproblem always has a known feasible point. Numerical experiments are presented for a subset of the problems from the CUTEr test collection.

关键词： nonlinear programming nonlinear constraints augmented Lagrangian sequential quadratic programming SQP methods stabilized SQP regularized methods primal-dual methods

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Using optimization procedures to minimize machining time while maintaining surface quality

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2013年第9-12期65卷 1659-1667页

作者： Corso, L. L. Zeilmann, R. P. Nicola, G. L. Missell, F. P. Gomes, H. M. Univ Caxias Sul Ctr Cincias Exatas & Tecnol Caxias Do Sul RS Brazil Univ Fed Rio Grande do Sul Porto Alegre RS Brazil

In this study, we present a methodology for minimizing machining time, where the surface roughness is constrained for the problem. The objective of the methodology is to encounter optimized cutting parameters which reduce machining time without reducing the surface quality of the machined workpiece. Three optimization schemes were considered to encounter the minima of a quantity which is a function of machining parameters: (a) sequential quadratic programming, (b) genetic algorithms, and (c) simulated annealing. For the discussion of the methodologies employed, an example of a machined surface is presented. The formalisms are used to obtain the parameters which minimize the machining time while maintaining the surface roughness within acceptable limits.

关键词： Machining sequential quadratic programming Genetic algorithms Simulated annealing Roughness Optimization

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Analysis and Optimization of Double-Jet Film-Cooling Holes

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JOURNAL OF THERMOPHYSICS AND HEAT TRANSFER 2013年第2期27卷 246-254页

作者： Choi, Dae-Woong Lee, Ki-Don Kim, Kwang-Yong Inha Univ Dept Mech Engn Inchon 402751 South Korea Inha Univ Inchon 402751 South Korea

A parametric analysis and optimization of double-jet film-cooling holes was performed using three-dimensional Reynolds averaged Navier-Stokes equations with the shear stress transport turbulence model. The numerical results for film-cooling effectiveness were validated in comparison with experimental data. The lateral and streamwise distances between the centers of the holes, along with two lateral ejection angles, were chosen as the design variables. The effects of these four variables on the film-cooling effectiveness were evaluated. For optimization of double-jet film-cooling holes, film-cooling effectiveness was considered as the objective function. Latin hypercube sampling was used to determine the design points. A weighted average surrogate model was constructed using the objective function values calculated at the design points. sequential quadratic programming was used to search for the optimal point from the constructed surrogate. The cooling performance of double-jet film-cooling holes was improved considerably by optimization in comparison with the reference geometry.

关键词： Film Cooling Reynolds Averaged Navier Stokes Numerical Analysis sequential quadratic programming Turbulence Models Shear Stress Coolant Vortices Heat Transfer Coefficients Inlet Temperature

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SFC Optimization for Aero Engine Based on Hybrid GA-SQP Method

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INTERNATIONAL JOURNAL OF TURBO & JET-ENGINES 2013年第4期30卷 383-391页

作者： Li, Jie Fan, Ding Sreeram, Victor Changan Univ Sch Elect & Control Engn Xian 710064 Peoples R China Northwestern Polytech Univ Sch Power & Energy Xian 710072 Peoples R China Univ Western Australia Dept Elect Elect & Comp Engn Crawley WA 6009 Australia

This study focuses on on-line specific fuel consumption (SFC) optimization of aero engines. For solving this optimization problem, a nonlinear pneumatic and thermodynamics model of the aero engine is built and a hybrid optimization technique which is formed by combining the genetic algorithm (GA) and the sequential quadratic programming (SQP) is presented. The ability of standard GA and standard SQP in solving this type of problem is investigated. It has been found that, although the SQP is fast, very little SFC reductions can be obtained. The GA is able to solve the problem well but a lot of computational time is needed. The presented hybrid GA-SQP gives a good SFC optimization effect and saves 76.6% computational time when compared to the standard GA. It has been shown that the hybrid GA-SQP is a more effective and higher real-time method for SFC on-line optimization of the aero engine.

关键词： specific fuel consumption aero engine optimal control genetic algorithm sequential quadratic programming hybrid algorithm

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Multidisciplinary Design Optimization: A Survey of Architectures

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AIAA JOURNAL 2013年第9期51卷 2049-2075页

作者： Martins, Joaquim R. R. A. Lambe, Andrew B. Univ Michigan Ann Arbor MI 48109 USA Univ Toronto Toronto ON M3H 5T6 Canada

Multidisciplinary design optimization is a field of research that studies the application of numerical optimization techniques to the design of engineering systems involving multiple disciplines or components. Since the inception of multidisciplinary design optimization, various methods (architectures) have been developed and applied to solve multidisciplinary design-optimization problems. This paper provides a survey of all the architectures that have been presented in the literature so far. All architectures are explained in detail using a unified description that includes optimization problem statements, diagrams, and detailed algorithms. The diagrams show both data and process flow through the multidisciplinary system and computational elements, which facilitate the understanding of the various architectures, and how they relate to each other. A classification of the multidisciplinary design-optimization architectures based on their problem formulations and decomposition strategies is also provided, and the benefits and drawbacks of the architectures are discussed from both theoretical and experimental perspectives. For each architecture, several applications to the solution of engineering-design problems are cited. The result is a comprehensive but straightforward introduction to multidisciplinary design optimization for nonspecialists and a reference detailing all current multidisciplinary design-optimization architectures for specialists.

关键词： MDO Numerical Optimization Lagrange Multipliers sequential quadratic programming Structural Optimization Computing Structural Analysis Aircraft Design Aerodynamic Loads sequential Linear programming

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On-Line Path Generation for Unmanned Aerial Vehicles Using B-Spline Path Templates

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2013年第6期36卷 1642-1653页

作者： Jung, Dongwon Tsiotras, Panagiotis Korea Aerosp Res Inst Taejon 305333 South Korea Georgia Inst Technol Atlanta GA 30332 USA

The problem of generating a smooth reference path, given a finite family of discrete, locally optimal paths, is investigated. A finite discretization of the environment results in a sequence of obstacle-free square cells. The generated path must lie inside the channel generated by these obstacle-free cells, while minimizing certain performance criteria. Two constrained optimization problems are formulated and solved subject to the given geometric (linear) constraints and boundary conditions in order to generate a library of B-spline path templates offline. These templates are recalled during implementation and are merged together on the fly in order to construct a smooth and feasible reference path to be followed by a closed-loop tracking controller. Combined with a discrete path planner, the proposed algorithm provides a complete solution to the obstacle-free path-generation problem for an unmanned aerial vehicle in a computationally efficient manner, which is suitable for real-time implementation.

关键词： UAV Obstacle Avoidance B Splines Path Planning sequential quadratic programming Linear Interpolation Search Algorithm Proximity Sensors Navigation Algorithm Computer Memory

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