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optimization of an observing system design for the North Atlantic meridional overturning circulation

JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY 2008年第4期25卷 625-634页

作者： Baehr, Johanna McInerney, David Keller, Klaus Marotzke, Jochem Max Planck Inst Meteorol Hamburg Germany Penn State Univ Dept Geosci University Pk PA 16802 USA

Three methods are analyzed for the design of ocean observing systems to monitor the meridional overturning circulation (MOC) in the North Atlantic. Specifically, a continuous monitoring array to monitor the MOC at 1000 m at different latitudes is "deployed" into a numerical model. The authors compare array design methods guided by (i) physical intuition (heuristic array design), (ii) sequential optimization, and (iii) global optimization. The global optimization technique can recover the true global solution for the analyzed array design, while gradient-based optimization would be prone to misconverge. Both global optimization and heuristic array design yield considerably improved results over sequential array design. Global optimization always outperforms the heuristic array design in terms of minimizing the root-mean-square error. However, whether the results are physically meaningful is not guaranteed;the apparent success might merely represent a solution in which misfits compensate for each other accidentally. Testing the solution gained from global optimization in an independent dataset can provide crucial information about the solution's robustness.

关键词： Studies Time series Systems design optimization algorithms

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An improved artificial bee colony algorithm based on mean best-guided approach for continuous optimization problems and real brain MRI images segmentation

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NEURAL COMPUTING & APPLICATIONS 2021年第5期33卷 1671-1697页

作者： Alrosan, Ayat Alomoush, Waleed Norwawi, Norita Alswaitti, Mohammed Makhadmeh, Sharif Naser Univ Sains Islam Malaysia Dept Comp Sci Fac Sci & Technol Nilai Negeri Sembilan Malaysia Imam Abdulrahman Bin Faisal Univ Dept Comp Dammam Saudi Arabia Xiamen Univ Malaysia Sch Elect & Comp Engn ICT Sepang 43900 Selangor Darul Malaysia Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia

The artificial bee colony (ABC) algorithm is a relatively new algorithm inspired by nature and has been shown to be efficient in contrast to other optimization algorithms. Nonetheless, ABC has some similar drawbacks to the optimization algorithms in terms of the unbalanced search behavior. The original ABC algorithm shows strong exploration capability with ineffective exploitation due to the unbalanced search model. In this paper, a new ABC algorithm called MeanABC is introduced to achieve the search behavior balance via a modified search equation based on the information of the mean of the previous best solutions. To evaluate the performance of the proposed algorithm, experiments were divided into two parts: First, the proposed algorithm was tested on a comprehensive set of 14 benchmark functions. The results show that the proposed MeanABC enhances the performance of the original ABC in terms of faster global convergence speed, solution quality, and better robustness when compared to other ABC variants. Secondly, the proposed algorithm was applied as a hybrid with the FCM algorithm as a segmentation technique to a set of 20 volumes of real brain MRI images with 20 images for each volume. All of these images have several characteristics, levels of difficulty, and cover different domains. The obtained results are promising, especially when the performance of the proposed algorithm was compared to other state-of-the-art segmentation techniques.

关键词： optimization algorithms Artificial bee colony Continuous optimization problems Search behavior MRI images

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Accelerating tri-directional material distribution optimization in functionally graded plates with an adaptive design control point variable selection

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2024年第PartA期418卷

作者： Tang, Huy Nguyen, Nam, V Lee, Jaehong Sejong Univ Deep Learning Architecture Res Ctr Seoul 05006 South Korea Ind Univ Ho Chi Minh City Fac Mech Technol Ho Chi Minh City Vietnam

In this paper, the problem of optimizing tri-directional material distribution in functionally graded (FG) plates under static loads to minimize the compliance is studied. Generalized shear deformation theory (GSDT) in the framework of isogeometric analysis is employed as the analyzer for efficiency and accuracy, and a non-uniform rational B-spline function is used for depicting the material distribution. As the number of control points in fine 3D design meshes, i.e., the number of optimization variables, can be too large for an algorithm to find a practically feasible design, we propose an adaptive variable selection mechanism. It gradually selects control points at important regions as variables by considering neighbor material variance, hence accelerates the optimization process. Various plate geometries are considered to validate the present approach. The findings confirm that the design mesh should be fine, and that the proposed variable selection strategy enables the optimization algorithm to find much more refined designs even in cases of large dimensions while also reducing computation.

关键词： Tri-directional material distribution FG plates optimization algorithms GSDT-based isogeometric analysis Adaptive design variable selection

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A Distributed optimization Algorithm for the Predictive Control of Smart Grids

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2016年第12期61卷 3898-3911页

作者： Braun, Philipp Gruene, Lars Kellett, Christopher M. Weller, Steven R. Worthmann, Karl Univ Bayreuth Math Inst D-95440 Bayreuth Germany Univ Newcastle Sch Elect Engn & Comp Sci Callaghan NSW 2308 Australia Tech Univ Ilmenau Inst Math D-99693 Ilmenau Germany

In this paper, we present a hierarchical, iterative distributed optimization algorithm and show that the algorithm converges to the global solution of a particular optimization problem. The motivation for the distributed optimization problem is the predictive control of a smart grid, in which the states of charge of a network of residential-scale batteries are optimally coordinated so as to minimize variability in the aggregated power supplied to/from the grid by the residential network. The distributed algorithm developed in this paper calls for communication between a central entity and an optimizing agent associated with each battery, but does not require communication between agents. The distributed algorithm is shown to achieve the performance of a large-scale centralized optimization algorithm, but with greatly reduced communication overhead and computational burden. A numerical case study using data from an Australian electricity distribution network is presented to demonstrate the performance of the distributed optimization algorithm.

关键词： Control over communications optimization algorithms predictive control for linear systems smart grid

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STOCHASTIC ISTA/FISTA ADAPTIVE STEP SEARCH algorithms FOR CONVEX COMPOSITE optimization

arXiv

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arXiv 2024年

作者： Nguyen, Lam M. Scheinberg, Katya Tran, Trang H. IBM Research Thomas J. Watson Research Center Yorktown HeightsNY United States H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA United States School of Operations Research and Information Engineering Cornell University IthacaNY United States

We develop and analyze stochastic variants of ISTA and a full backtracking FISTA algorithms Beck and Teboulle [2009], Scheinberg et al. [2014] for composite optimization without the assumption that stochastic gradient is an unbiased estimator. This work extends analysis of inexact fixed step ISTA/FISTA in Schmidt et al. [2011] to the case of stochastic gradient estimates and adaptive step-size parameter chosen by backtracking. It also extends the framework for analyzing stochastic line-search method in Cartis and Scheinberg [2018] to the proximal gradient framework as well as to the accelerated first order methods. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

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Automated 4D BIM development: the resource specification and optimization approach

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ENGINEERING CONSTRUCTION AND ARCHITECTURAL MANAGEMENT 2024年第5期31卷 1896-1922页

作者： Fazeli, Abdulwahed Banihashemi, Saeed Hajirasouli, Aso Mohandes, Saeed Reza Iran Univ Sci & Technol Tehran Iran Univ Canberra Sch Design & Built Environm Canberra Australia Western Sydney Univ Dept Built Environm Sydney Australia Univ Manchester Dept Mech Aerosp & Civil Engn Manchester England

PurposeThis research aims to develop an automated and optimization algorithms (OAs)-integrated 4D building information modeling (BIM) approach and a prototype and enable construction managers and practitioners to estimate the time of compound elements in building projects using the resource specification ***/methodology/approachA 4D BIM estimation process was first developed by applying the resource specification and geometric information from the BIM model. A suite of OA including particle swarm optimization, ant colony, differential evolution and genetic algorithm were developed and compared in order to facilitate and automate the estimation process. The developed processes and porotypes were linked and *** OA-based automated 4D BIM estimation prototype was developed and validated through a real-life construction project. Different OAs were applied and compared, and the genetic algorithm was found as the best performing one. The prototype was successfully linked with BIM timeliner application. By using this approach, the start and finish dates of all object-based activities are developed, and the project completion time is automatically ***/valueUnlike conventional construction estimation methods which need various tools and are error prone and time-consuming, the developed method bypasses the existing time estimation tools and provides the integrated and automated process with BIM and machine learning algorithms. Furthermore, this approach integrates 4D BIM applications into construction design procedures, connected with OA automation.

关键词： BIM 4D Time estimation optimization algorithms Construction resource

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An efficient improved African vultures optimization algorithm with dimension learning hunting for traveling salesman and large-scale optimization applications

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第12期37卷 12367-12421页

作者： Singh, Narinder Houssein, Essam H. Mirjalili, Seyedali Cao, Yankai Selvachandran, Ganeshsree Punjabi Univ Dept Math Patiala Punjab India Minia Univ Fac Comp & Informat Al Minya Egypt Griffith Univ Inst Integrated & Intelligent Syst Nathan Qld Australia Univ British Columbia Fac Appl Sci UBC Chem & Biol Engn Vancouver BC Canada UCSI Univ Fac Business & Management Dept Actuarial Sci & Appl Stat Jalan Menara Gading Kuala Lumpur Malaysia Punjabi Univ Dept Math Patiala 147002 Punjab India

Exploring the finest shortest-path traveling salesman optimization application is a typical NP-hard problem. Similarly the solution of the large-scale optimization applications is also a big challenging issue in front of scientists. First, African Vultures optimization Algorithm (AVOA) was developed to resolve continuous applications where it performed fine. In the last few months, many enhanced strategies of AVOA have been offered in recent literature works and it has been extensively utilized to resolve large-scale engineering optimization applications. This study offers a newly modified dimension learning hunting (DLH)-based AVOA called DLHAV algorithm to resolve highly complex continuous and discrete applications. It helps improve the imbalance amid the hunting (or exploitation) and search (or exploration), the lack of crowd diversity, slow convergence speed, trapping in local optima, and early convergence of the AVOA variant. The proposed strategy benefits from a newly driven approach called the DLH search approach congenital from the separate exploitation behavior of vultures in the search domain. DLH exploration strategy utilizes a distinct method to make the best neighborhood for all vultures in which the nearest member information can be supplied amid vultures. DLH helps in improving the balance amid global and local and sustains diversity. To scrutinize the performance of DLHAV, the solutions of the DLHAV method are verified on 29-CEC'17 and 10-CEC'20 with familiar comparative methods and some other classical optimization approaches over many familiar traveling salesman problem/large-scale instances. With the intention of attaining unbiased and rigorous comparison, descriptive statistics such as standard deviation and mean have been applied, and the statistical Friedman test is also conducted. The experimental solution carried out in this study has revealed that the proposed algorithm outperforms significantly over the other alternative optimizers.

关键词： african vultures optimization dimension learning hunting IEEE 29-CEC'17 and 10-CEC'20 test suites metaheuristic optimization algorithms optimization problems traveling salesman and large-scale optimization applications

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Embedded Online optimization for Model Predictive Control at Megahertz Rates

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2014年第12期59卷 3238-3251页

作者： Jerez, Juan L. Goulart, Paul J. Richter, Stefan Constantinides, George A. Kerrigan, Eric C. Morari, Manfred ETH Automat Control Lab CH-8092 Zurich Switzerland Univ London Imperial Coll Sci Technol & Med Dept Elect & Elect Engn London SW7 2AZ England Univ London Imperial Coll Sci Technol & Med Dept Aeronaut London SW7 2AZ England

Faster, cheaper, and more power efficient optimization solvers than those currently possible using general-purpose techniques are required for extending the use of model predictive control (MPC) to resource-constrained embedded platforms. We propose several custom computational architectures for different first-order optimization methods that can handle linear-quadratic MPC problems with input, input-rate, and soft state constraints. We provide analysis ensuring the reliable operation of the resulting controller under reduced precision fixed-point arithmetic. Implementation of the proposed architectures in FPGAs shows that satisfactory control performance at a sample rate beyond 1 MHz is achievable even on low-end devices, opening up new possibilities for the application of MPC on embedded systems.

关键词： Embedded systems optimization algorithms predictive control of linear systems

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Usage of coot optimization-based random forests analysis for determining the shallow foundation settlement

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GEOMECHANICS AND ENGINEERING 2023年第3期32卷 271-291页

作者： Han, Yi Jiang, Xingliang Wang, Ye Wang, Hui Anhui Sci & Technol Univ Sch Architecture Bengbu 233100 Anhui Peoples R China CCCC Water Transportat Consultants Co Ltd Beijing 100007 Peoples R China Tongji Univ Dept Civil Engn Shanghai 200092 Peoples R China

Settlement estimation in cohesion materials is a crucial topic to tackle because of the complexity of the cohesion soil texture, which could be solved roughly by substituted solutions. The goal of this research was to implement recently developed machine learning features as effective methods to predict settlement (S-m) of shallow foundations over cohesion soil properties. These models include hybridized support vector regression (SVR), random forests (RF), and coot optimization algorithm (COM), and black widow optimization algorithm (BWOA). The results indicate that all created systems accurately simulated theS(m) with anR(2) of better than 0.979 and 0.9765 for the train and test data phases, respectively. This indicates extraordinary efficiency and a good correlation between the experimental and simulatedS(m). The model's results outperformed those ofANFIS -PSO andCOM -RF findings were much outstanding to those of the literature. By analyzing established designs utilizing different analysis aspects, such as various error criteria, Taylor diagrams, uncertainty analyses, and error distribution, it was feasible to arrive at the final result that the recommendedCOM-RF. was the outperformed approach in the forecasting process of S-m of shallow foundation, while other techniques were also reliable.

关键词： forecasting optimization algorithms random forests settlement shallow foundation support vector regression

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A response surface for structural optimization

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JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING-TRANSACTIONS OF THE ASME 1997年第3期119卷 196-202页

作者： Rule, WK Department of Aerospace Engineering and Mechanics University of Alabama Box 870280 Tuscaloosa AL 35487-0280

Structural optimization is computationally intensive. Typically, a finite element model must be solved repeatedly during the process. Accordingly, various approximation schemes have been developed to reduce the cost of solves or to eliminate solves entirely during portions of the optimization process. In this paper, a new type of approximation or interpolation function in the form of a global response surface is described. With this function, approximations can be made with a limited amount of calibrating data points. The response surface requires no matrix inversions for coefficient evaluation, and therefore it is relatively efficient and stable. The new response surface is used in conjunction with a modified zero-order Powell search algorithm. The effectiveness of the response surface is evaluated for a reasonably realistic offshore structure application. This planar truss structure is subjected to a deck load, a lateral wave load (considered quasi-static), and member self-weight loads. The objective for this test case was to minimize the structural cost. Nodal coordinates were considered to be the explicit design variables. Response surface results are compared with those obtained by the common optimization algorithms: steepest descent, conjugate directions, and a conventional implementation of Powell's method. The response surface appears to be effective for the test case considered.

关键词： Structural optimization Weight (Mass) optimization Function approximation Waves Interpolation Trusses (Building) Stress Design Offshore structures optimization algorithms Approximation Response surface methodology Finite element model algorithms

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