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Nonlinear-programming reformulation of the order-value optimization problem

MATHEMATICAL METHODS OF OPERATIONS RESEARCH 2005年第3期61卷 365-384页

作者： Andreani, R Dunder, C Martínez, JM UNICAMP IMECC Dept Appl Math BR-13081970 Campinas SP Brazil Univ Sao Paulo IME Dept Appl Math BR-05508900 Sao Paulo Brazil

Order-value optimization (OVO) is a generalization of the minimax problem motivated by decision-making problems under uncertainty and by robust estimation. New optimality conditions for this nonsmooth optimization problem are derived. An equivalent mathematical programming problem with equilibrium constraints is deduced. The relation between OVO and this nonlinear-programming reformulation is studied. Particular attention is given to the relation between local minimizers and stationary points of both problems.

关键词： order-value optimization optimality conditions nonlinear-programming equilibrium constraints optimization algorithms

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Parameter estimation for an internal variable model using nonlinear optimization and analytical/numerical response sensitivities

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JOURNAL OF ENGINEERING MATERIALS AND TECHNOLOGY-TRANSACTIONS OF THE ASME 1997年第4期119卷 337-345页

作者： Fossum, AF Sandia National Laboratories Albuquerque NM 87185

This paper demonstrates through examples that erroneous material constants for complex visco-plastic material models can be obtained from simultaneous parameter estimation by nonlinear optimization methods unless the laboratory load paths used in the fitting process give significant model response sensitivities to changes in all of the material parameters. A general procedure is proposed in which a nonlinear optimization algorithm is coupled with analytically/numerically derived response sensitivities to evaluate an unambiguous set of material parameters. Response sensitivities enter into the parameter estimation procedure in two ways. Relative response sensitivities are first used to identify an efficient test matrix that when simulated with the model, give model responses that are sensitive to changes in each of the material parameters. Then the corresponding nonzero response sensitivities are used to construct the gradient and Hessian matrices in a gradient-driven optimization algorithm to evaluate the material parameters. A model for braze alloys is used to demonstrate that erroneous parameter values may result if not all of the relative response sensitivities are ''nonzero'' and district.

关键词： optimization Parameter estimation Stress Fittings optimization algorithms Alloys

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Mechanical characterization of hyperelastic materials with fringe projection and optimization techniques

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OPTICS AND LASERS IN ENGINEERING 2006年第5期44卷 423-442页

作者： Genovese, K Lamberti, L Pappalettere, C Politecn Bari Dipartimento Ingn Meccan & Gest I-71026 Bari Italy Univ Basilicata Dipartimento Ingn Fis Ambiente I-85100 Potenza Italy

This paper presents a new hybrid technique for mechanical characterization of hyperelastic materials. The research is motivated by the fact that standard identification procedures based on the fitting of strain-stress curves determined experimentally from planar biaxial tests may be inaccurate for non-uniform states of deformation. Therefore, we propose an alternative approach where the difference ohm between the displacement field measured with Projection Moire and its counterpart predicted by FEM is minimized using non-linear optimization algorithms that finally find unknown material properties. In order to check the feasibility of the new procedure, we considered a thin latex membrane modelling it as a two-parameter Mooney-Rivlin (MR) hypcrelastic material. The ohm function is minimized either using optimization routines available in a commercial finite element package and by implementing a global optimizer able to deal with non-linearity and non-convexity included in the identification process. In order to check accuracy of optimization results, target values of MR constants for the latex specimen tested have previously been determined by fitting experimental stress-strain data gathered from a standard planar biaxial tension test. Results indicate that the present hybrid identification procedure can determine accurately properties of the hyperelastic material under investigation. In fact, the average residual error on displacements was less than 1% while the difference between the MR constants found with optimization and their target values was less than 3.5%. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： hyperelastic materials Mooney-Rivlin constants Moire optimization algorithms

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Buffer allocation optimization in ATM switching networks using ALOPEX algorithm

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NEUROCOMPUTING 1999年第1-3期24卷 1-11页

作者： Pandya, AS Sen, E Hsu, S Florida Atlantic Univ Dept Comp Sci & Engn Boca Raton FL 33431 USA

We describe the use of a stochastic algorithm, called ALOPEX, which could be implemented in VLSI for optimizing the buffer allocation process in ATM switching networks. We present the results of computer simulations for buffer allocation in ATM switching networks using the ALOPEX algorithm. The algorithm uses a scalar cost function which is a measure of global performance. The ALOPEX works by broadcasting the global cost function to all neural processors in the neural network. Since each neural processor solely depends on the global cost function no interaction is needed between the neural processors and the algorithm is more amenable to massively parallel implementation. The application of the ALOPEX algorithm for the buffer allocation optimization in ATM networks assumes limited buffer capacity. The proposed ALOPEX-based approach takes advantage of the favorable control characteristics of the algorithm such as high adaptability and high speed collective computing power for effective buffer utilization. The proposed model uses complete sharing buffer allocation strategy and enhances its performance for high traffic loads by regulating the buffer allocation process dynamically. (C) 1999 Published by Elsevier Science B.V. All rights reserved.

关键词： optimization algorithms ATM switching neural networks buffer management

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Assessment of optimization Methods Used to Determine Plasticity Parameters Based on DIC and back Calculation Methods

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EXPERIMENTAL TECHNIQUES 2019年第4期43卷 385-396页

作者： Kowalewski, L. Gajewski, M. Warsaw Univ Technol Fac Civil Engn Al Armii Ludowej 16 PL-00637 Warsaw Poland

In this paper, we analyse various minimization algorithms applied to the problem of determining elasto-plastic material parameters using an inverse analysis and digital image correlation (DIC) system. As the DIC system, ARAMIS is used, while for the finite element solution of boundary value problems, Abaqus software is applied. Different minimization algorithms, implemented in the SciPy Python library, were initially juxtaposed, compared and evaluated based on benchmark functions. Next the proper evaluation of the algorithms was performed to determine the material parameters for isotropic metal plasticity with the Huber-Mises yield criterion and isotropic or combined kinematic-isotropic plastic hardening models. For all researchers utilizing back calculation methods based on a DIC measuring system, such analysis results may be interesting. It was concluded that among the local minimization methods, derivative free optimization algorithms, especially the Powell algorithm, perform the most efficiently.

关键词： Plasticity DIC Inverse analysis optimization algorithms Python Abaqus

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Utilizing a Hybrid Cat-Swarm optimization Algorithm for Seamless Vertical Handoff in Vanet

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WIRELESS PERSONAL COMMUNICATIONS 2024年第1期137卷 495-521页

作者： Jahangeer, Naskath Mustafa, Zaid Aldabbas, Hamza Natl Engn Coll Dept Comp Sci & Engn Kovilpatti India Al Balqa Appl Univ Prince Abdullah Bin Ghazi Fac Informat & Commun Te Salt Jordan

In order to enhance spectrum competence for successful data rates, future generation vehicle networks will be able to provide the method for effective data dissemination via diverse radio technologies. Higher mobility networks cause unnecessary handoffs since a vehicle regularly moves between various diverse networks. In this case, a subpar handoff algorithm exacerbates this issue by causing significant packet loss during frequent and erratic handoffs, commonly referred to as ping-pong handoffs. The proposed model Hybrid Cat Swarm optimization-TOPSIS Algorithm (HCSTA) is used to execute the vertical handoffs in urban VANET. Both mobility and network simulator are employed to demonstrate the effectiveness of the proposed techniques. The effectiveness of this work is demonstrated through the combined utilization of mobility and network simulators. Handoff occurrences are affected by the direction of moving vehicles and usage technology. In the Same direction, 150 vehicles experienced 6 handoffs due to longer dwell times, while those in the opposite direction encountered 11 handoffs. In the utility comparison, UMTS (Universal Mobile Telecommunications System) to Wi-Fi transitions face low handoffs compared to DSRC (Dedicated Short Range Communication) to WiMAX (Worldwide Interoperability for Microwave Access), in opposite directions. Utility rates of technologies like Wi-Fi (Wirelesss Fidelity), WiMAX, UMTS, and DSRC vary with speed as high, moderate and low, with WiMAX providing the most consistent performance. The packet delivery rate (0.78) decreases with higher vehicle densities 150 and speeds 85 Kmph due to increased network load and high bandwidth utilization compared to low speed scenarios. The overall network throughput increases from 38.9 to 39.9 Kbps with moderate vehicle densities, with a level noted at 85 and 115 vehicles for the 50-60 km/h speed category, indicating optimal load and moderate bandwidth efficiency.

关键词： Vertical handoff Urban Scenario optimization algorithms TOPSIS algorithm Seamless connectivity

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Single- and Multievent optimization in Combined Sewer Flow and Water Quality Model Calibration

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JOURNAL OF ENVIRONMENTAL ENGINEERING-ASCE 2011年第7期137卷 551-558页

作者： Gamerith, Valentin Gruber, Guenter Muschalla, Dirk Graz Univ Technol Inst Urban Water Management & Landscape Water Eng A-8010 Graz Austria Univ Laval Quebec City PQ G1V 0A6 Canada

Over the last three decades, storm-water quality modeling has been used increasingly commonly to describe the general system behavior and assess the pollution loads transferred in and spilled out of combined sewer systems. The calibration of quality models is, in most cases, based on conventionally obtained calibration data, e.g., by automated sampling. Long-term high-resolution online measurement data are available for the Graz West catchment (Graz, Austria), allowing an assessment of the full dynamics of discharge and pollution concentrations. This paper focuses on the application and comparison of single-event and two different multievent optimization schemes for sewer-water quality model calibration. While both single- and multievent optimization lead to satisfying results for the calibration events in discharge calibration, it is shown that validation events are better reproduced by using multievent calibration. Single- and multievent autocalibration of pollution concentration is based on the best dataset obtained from the discharge calibration. As for discharge, the pollutographs are reproduced satisfactorily, and multievent calibration is more stable. In all cases, the two multievent approaches performed equally well. DOI: 10.1061/(ASCE)EE.1943-7870.0000356. (C) 2011 American Society of Civil Engineers.

关键词： Calibration Combined sewers Hydraulic models optimization algorithms Monitoring Water pollution

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Boosting whale optimization with evolution strategy and Gaussian random walks: an image segmentation method

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ENGINEERING WITH COMPUTERS 2023年第3期39卷 1935-1979页

作者： Hussien, Abdelazim G. Heidari, Ali Asghar Ye, Xiaojia Liang, Guoxi Chen, Huiling Pan, Zhifang Linkoping Univ Dept Comp & Informat Sci Linkoping Sweden Fayoum Univ Fac Sci Al Fayyum Egypt Wenzhou Univ Dept Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China Shanghai Lixin Univ Accounting & Finance Shanghai 201209 Peoples R China Wenzhou Polytech Dept Informat Technol Wenzhou 325035 Peoples R China Wenzhou Med Univ Affiliated Hosp 1 Wenzhou 325000 Peoples R China

Stochastic optimization has been found in many applications, especially for several local optima problems, because of their ability to explore and exploit various zones of the feature space regardless of their disadvantage of immature convergence and stagnation. Whale optimization algorithm (WOA) is a recent algorithm from the swarm-intelligence family developed in 2016 that attempts to inspire the humpback whale foraging activities. However, the original WOA suffers from getting trapped in the suboptimal regions and slow convergence rate. In this study, we try to overcome these limitations by revisiting the components of the WOA with the evolutionary cores of Gaussian walk, CMA-ES, and evolution strategy that appeared in Virus colony search (VCS). In the proposed algorithm VCSWOA, cores of the VCS are utilized as an exploitation engine, whereas the cores of WOA are devoted to the exploratory phases. To evaluate the resulted framework, 30 benchmark functions from IEEE CEC2017 are used in addition to four different constrained engineering problems. Furthermore, the enhanced variant has been applied in image segmentation, where eight images are utilized, and they are compared with various WOA variants. The comprehensive test and the detailed results show that the new structure has alleviated the central shortcomings of WOA, and we witnessed a significant performance for the proposed VCSWOA compared to other peers.

关键词： Exploration and exploitation Nature-inspired method Metaheuristic optimization algorithms Engineering problems

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Application of the improved chaotic grey wolf optimization algorithm as a novel and efficient method for parameter estimation of solid oxide fuel cells model

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2021年第73期46卷 36454-36465页

作者： Hao, Peng Sobhani, Behnam Shanxi Inst Mech & Elect Engn Changzhi 046011 Shanxi Peoples R China Iran Univ Sci & Technol Sch Elect Engn Tehran Iran

ABS T R A C T In this paper, important functional parameters of solid oxide fuel cells are identified by introducing a novel high-speed optimization method, namely adaptive chaotic grey wolf optimization algorithm. The suggested optimization method is obtained by combining the adaptive grey wolf optimization and chaotic grey wolf optimization algorithms. The chaotic algorithm is applied to the basic grey wolf optimization to achieve higher convergence speed, keep the population's diversity, and provide an initial population with uniform distribution. Besides, a nonlinear convergence factor is defined for balancing the global and local exploration abilities. Employing the improved convergence factor resulted in a new version of the grey wolf optimization algorithm, namely adaptive grey wolf optimization algorithm. Adaptive chaotic grey wolf optimization algorithm adopts the advantages of both chaotic grey wolf optimization and adaptive grey wolf optimization methods simultaneously. The adaptive grey wolf optimization algorithm is applied to a 5 kW dynamic tubular stack. The results of the simulation report the lowest values of mean squared error, higher accuracy, higher robustness, and high convergence speed for the adaptive grey wolf optimization algorithm compared to some well-known optimization methods. Besides, the proposed method shows a good agreement with experimental results with lower computational difficulty. (c) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Adaptive grey wolf optimization algorithm Parameter identification Solid oxide fuel cell optimization algorithms

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Detecting symmetry in grey level images: The global optimization approach

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INTERNATIONAL JOURNAL OF COMPUTER VISION 1998年第1期29卷 29-45页

作者： Kiryati, N Gofman, Y Technion Israel Inst Technol Dept Elect Engn IL-32000 Haifa Israel

The detection of significant local reflectional symmetry in grey level images is considered. Prior segmentation is not assumed, and it is intended that the results could be used for guiding visual attention and for providing side information to segmentation algorithms. A local measure of reflectional symmetry that transforms the symmetry detection problem to a global optimization problem is defined. Reflectional symmetry detection becomes equivalent to finding the global maximum of a complicated multimodal function parameterized by the location of the center of the supporting region, its size, and the orientation of the symmetry axis. Unlike previous approaches, time consuming exhaustive search is avoided. A global optimization algorithm for solving the problem is presented. It is related to genetic algorithms and to adaptive random search techniques. The efficiency of the suggested algorithm is experimentally demonstrated. Just one thousand evaluations of the local symmetry measure are typically needed in order to locate the dominant symmetry in natural test images.

关键词： Skewed symmetries Shape-description Local symmetry Axis Transform Segmentation Intensity Axes search technique Axis of Symmetry Symmetry grey Global Maximum Reflectional symmetry optimization algorithms Transform Global optimization exhaustive search

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