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Self-Tuned Mirror Descent Schemes for Smooth and Nonsmooth High-Dimensional Stochastic optimization

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2019年第10期64卷 4377-4384页

作者： Majlesinasab, Nahidsadat Yousefian, Farzad Pourhabib, Arash Oklahoma State Univ Sch Ind Engn & Management Stillwater OK 74074 USA

We consider randomized block coordinate stochastic mirror descent (RBSMD) methods for solving high-dimensional stochastic optimization problems with strongly convex objective functions. Our goal is to develop RBSMD schemes that achieve a rate of convergence with a minimum constant factor with respect to the choice of the stepsize sequence. To this end, we consider both subgradient and gradient RBSMD methods addressing nonsmooth and smooth problems, respectively. For each scheme, first, we develop self-tuned stepsize rules characterized in terms of problem parameters and algorithm settings;second. we show that the nonaveraging iterate generated by the underlying RBSMD method converges to the optimal solution both in an almost sure and a mean sense;third, we show that the mean squared error is minimized. When problem parameters are unknown, we develop a unifying self-tuned update rule that can be applied in both subgradient and gradient stochastic mirror descent (SMD) methods, and show that for any arbitrary and small enough initial stepsize, a suitably defined error bound is minimized. We provide constant factor comparisons with standard SMD and RBSMD methods. Our numerical experiments performed on a support vector machine (SVM) model display that the self-tuned schemes are significantly robust with respect to the choice of problem parameters and the initial stepsize.

关键词： Machine learning optimization optimization algorithms randomized algorithms

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

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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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An Advanced Circular Adaptive Search Butterfly optimization Algorithm for the CNN-based Sleep Apnea Detection Approach

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IETE JOURNAL OF RESEARCH 2024年第2期70卷 1425-1437页

作者： Geetha, J. Raja, J. Benadict Anna Univ Chennai Tamil Nadu India PSNA Coll Engn & Technol Dindigul Tamil Nadu India

The analysis of pulse photoplethysmography (PPG) signals using computerized techniques is a developing field in research. Various effective signal-processing tools have been presented for automatic disease detection systems. Sleep apnea is a syndrome that affects the respiratory system and it commonly occurs due to Oxygen desaturation while sleeping. This paper develops an automatic system to detect sleep apnea from PPG signals. The detection of this syndrome is very important and many approaches were presented to improve the performance. The proposed method improves the classification accuracy through the enhancement of the feature extraction method and using the optimized classifier. As a feature extraction process, Hilbert Huang Transform (HHT) with extrema selection reformed (ESR) Empirical mode decomposition (EMD) is presented in this work. The development of the ESR-EMD system provides a better decomposition of signals and makes feature extraction effective. In addition, the computation time process is reduced as the interpolation is done using the more significant extrema points. Afterward, the feature selection is implemented using fisher discriminant analysis (FDA). An improved CNN classifier with a circular adaptive search butterfly optimization algorithm (CASBOA) is presented for classification. The optimum results obtained using BOA can be increased by employing an adaptive circular search function. This approach can increase the accuracy of the classifier and reduce computational time. The proposed approach is validated in MATLAB with a dataset and the performance metrics are compared with the conventional approaches.

关键词： Bio-medical signals Feature extraction Neural networks optimization algorithms Signal processing

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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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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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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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