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optimization and optimality of (s, S) stochastic inventory systems with non-quasiconvex costs

PROBABILITY IN THE ENGINEERING AND INFORMATIONAL SCIENCES 2006年第2期20卷 287-306页

作者： Chen, FY Feng, Y Chinese Univ Hong Kong Dept Syst Engn & Engn Management Shatin Hong Kong Peoples R China

This article considers the optimization and optimality of single-item/location, infinite-horizon, (s, S) inventory models. Departing from the conventional approach, we do not assume the loss function describing holding and shortage costs per period to be quasiconvex. As the existing optimization algorithms have been established on the condition of quasiconvexity, our goal in this article is to develop a computational procedure for obtaining optimal (s,S) policies for models with general loss functions. Our algorithm is based on the parametric method commonly used in fractional programming and is intuitive, exact, and efficient. Moreover, this method allows us to extend the optimality of (s, S) policies to a broader class of loss functions that can be non-quasiconvex.

关键词： mathematical programming computational procedure LOSS FUNCTION parametric method infinite horizon optimization algorithms optimality Policies

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A Randomized Block-Coordinate Adam online learning optimization algorithm

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NEURAL COMPUTING & APPLICATIONS 2020年第16期32卷 12671-12684页

作者： Zhou, Yangfan Zhang, Mingchuan Zhu, Junlong Zheng, Ruijuan Wu, Qingtao Henan Univ Sci & Technol Sch Informat Engn Luoyang 471023 Peoples R China

In recent years, stochastic gradient descent (SGD) becomes one of the most important optimization algorithms in many fields, such as deep learning and reinforcement learning. However, the computation of full gradient in SGD is prohibitive when dealing with high-dimensional vectors. For this reason, we propose a randomized block-coordinate Adam (RBC-Adam) online learning optimization algorithm. At each round, RBC-Adam randomly chooses a variable from a subset of parameters to compute the gradient and updates the parameters along the negative gradient direction. Moreover, this paper analyzes the convergence of RBC-Adam and obtains the regret bound,O(T) whereTis a time horizon. The theoretical results are verified by simulated experiments on four public datasets. Moreover, the simulated experiment results show that the computational cost of RBC-Adam is lower than the variants of Adam.

关键词： Adam Convergence Online learning optimization algorithms Randomized block-coordinate Stochastic gradient descent

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FAST ARRAY THINNING USING GLOBAL optimization METHODS

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JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS 2010年第16期24卷 2259-2271页

作者： Fernandez-Delgado, M. Rodriguez-Gonzalez, J. A. Iglesias, R. Barro, S. Ares-Pena, F. J. Univ Santiago de Compostela Radiating Syst Grp Fac Phys Dept Appl Phys Santiago De Compostela 15782 Spain Univ Santiago de Compostela Intelligent Syst Grp Dept Elect & Comp Sci Santiago De Compostela 15782 Spain

A simple and fast method to accelerate the global optimization approaches used in array thinning is described. This method tabulates the contribution of every array element to the far-field pattern in order to improve the numerical efficiency of the optimization algorithm employed. Experiments using our proposal alongside with a genetic algorithm reduce the search computation time about 90%. Simulation results for both linear and planar arrays are shown.

关键词： optimization methods computational time singling Array Element Shortcuts optimization algorithms Planar antennas Global optimization far-field optimization method arrays Genetic Algorithm (GA)

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A BA-based algorithm for parameter optimization of Support Vector Machine

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PATTERN RECOGNITION LETTERS 2017年第Jul.1期93卷 13-22页

作者： Tharwat, Alaa Hassanien, Aboul Ella Elnaghi, Basem E. Suez Canal Univ Fac Engn Ismailia Ismailia Govern Egypt Sci Res Grp Egypt Cairo Egypt Cairo Univ Fac Comp & Informat Giza Giza Governorat Egypt

Support Vector Machine (SVM) parameters such as kernel parameter and penalty parameter (C) have a great impact on the complexity and accuracy of predicting model. In this paper, Bat algorithm (BA) has been proposed to optimize the parameters of SVM, so that the classification error can be reduced. To evaluate the proposed model (BA-SVM), the experiment adopted nine standard datasets which are obtained from UCI machine learning data repository. For verification, the results of the BA-SVM algorithm are compared with grid search, which is a conventional method of searching parameter values, and two well-known optimization algorithms: Genetic Algorithm (GA) and Particle Swarm optimization (PSO). The experimental results proved that the proposed model is capable to find the optimal values of the SVM parameters and avoids the local optima problem. The results also demonstrated lower classification error rates compared with PSO and GA algorithms. (C) 2016 Elsevier B.V. All rights reserved.

关键词： optimization algorithms Support Vector Machine (SVM) Classification Parameter optimization Swarm intelligent Bat algorithm (BA)

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ECG Signal Compression Based on optimization of Wavelet Parameters and Threshold Levels Using Evolutionary Techniques

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2023年第6期42卷 3509-3537页

作者： Singhai, Paridhi Kumar, Anil Ateek, A. Ansari, Irshad Ahmad Singh, G. K. Lee, Heung No PDPM Indian Inst Informat Technol Design & Mfg Jabalpur 482005 India Indian Inst Technol Roorkee Roorkee 247667 Uttarakhand India Gwangju Inst Sci & Technol Sch Elect Engn & Comp Sci Gwangju South Korea

The ECG (electrocardiogram) signals are an indicator of the electrical activity of the heart. Given its noninvasive nature ECG are an extremely popular medium for heart checkups. With the advent of modern technology, the world is moving toward a connected environment, and with the availability of wearable devices, there is an exponential increase in the transmission and storage of ECG and other physiological signals. It becomes necessary to compress the ECG signals for storage and transmission. Therefore, this paper presents an ECG compression algorithm based on discrete wavelet transform (DWT) and several nature-inspired optimization techniques. The ECG compression method uses optimization techniques to find the optimal values of wavelet design parameters and optimal threshold levels. In the proposed work, DWT is used to decompose the signal into sub-bands, and coefficients are obtained. Then, threshold values for each sub-band are selected using the optimization algorithms. After thresholding, the coefficients are further compressed using the modified run-length encoding (MRLE). The proposed work shows promising results and the original signal features are well preserved after reconstruction. The performance of this algorithm is tested by calculating different parameters such as percentage root-mean-square difference (PRD), quality score (QS), signal-to-noise ratio (SNR), and compression ratio (CR). This method is capable of providing a higher compression ratio with minimum distortion in ECG signal.

关键词： ECG compression Discrete wavelet transform optimization algorithms Modified run-length encoding (MRLE)

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Feature selection optimization with filtering and wrapper methods: two disease classification cases

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2023年第7期31卷 1329-1342页

作者： Atik, Serhat Dalyan, Tugba Istanbul Bilgi Univ Fac Engn & Nat Sci Dept Comp Engn Istanbul Turkiye

Discarding the less informative and redundant features helps to reduce the time required to train a learning algorithm and the amount of storage required, improving the learning accuracy as well as the quality of results. In this study, we present different feature selection approaches to address the problem of disease classification based on the Parkinson and Cardiac Arrhythmia datasets. For this purpose, first we utilize three filtering algorithms including the Pearson correlation coefficient, Spearman correlation coefficient, and relief. Second, metaheuristic algorithms are compared to find the most informative subset of the features to obtain better classification accuracy. As a final method, a hybrid model involving filtering algorithms is applied to the datasets to eliminate half of the features, and then a metaheuristic algorithm based on a proposed genetic algorithm is applied to the rest of the datasets. With all three methods, we use three classification algorithms: support vector machine, K-nearest neighbor, and random forest. The results show that the best scores are obtained from the metaheuristic algorithm based on the proposed genetic algorithm for both datasets. This comparative study contributes to the literature by increasing the accuracy of classification for both datasets and presenting a hybrid model with filtering and a metaheuristic algorithm.

关键词： Feature selection optimization algorithms metaheuristic algorithms genetic algorithms filtering methods

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Dynamical System Approach for Time-Varying Constrained Convex optimization Problems

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2024年第6期69卷 3822-3834页

作者： Raveendran, Rejitha Mahindrakar, Arun D. Vaidya, Umesh Tata Consultancy Serv Res & Innovat Grp Bengaluru 560066 Karnataka India Indian Inst Technol Madras Dept Elect Engn & member RBCDSAI Chennai 600036 Tamil Nadu India Clemson Univ Dept Mech Enginnering Clemson SC 29634 USA

optimization problems emerging in most of the real-world applications are dynamic, where either the objective function or the constraints change continuously over time. This article proposes projected primal-dual dynamical system approaches to track the primal and dual optimizer trajectories of an inequality constrained time-varying (TV) convex optimization problem with a strongly convex objective function. First, we present a dynamical system that asymptotically tracks the optimizer trajectory of an inequality constrained TV optimization problem. Later, we modify the proposed dynamics to achieve the convergence to the optimizer trajectory within a fixed time. The asymptotic and fixed-time convergence of the proposed dynamical systems to the optimizer trajectory is shown via the Lyapunov-based analysis. Finally, we consider the TV extended Fermat-Torricelli problem of minimizing the sum-of-squared distances to a finite number of nonempty, closed, and convex TV sets, to illustrate the applicability of the projected dynamical systems proposed in this article.

关键词： optimization TV Trajectory Dynamical systems Linear programming Prediction algorithms Convergence Lyapunov methods optimization algorithms stability of nonlinear systems time-varying (TV) optimization

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Gnowee: A Hybrid Metaheuristic optimization Algorithm for Constrained, Black Box, Combinatorial Mixed-Integer Design

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NUCLEAR TECHNOLOGY 2019年第4期205卷 542-562页

作者： Bevins, James E. Slaybaugh, R. N. Air Force Inst Technol Dept Engn Phys 2950 Hobson Way Wright Patterson AFB OH 45433 USA Univ Calif Berkeley Nucl Engn Dept 4173 Etcheverry Hall Berkeley CA 94720 USA

This paper introduces Gnowee, a modular, Python-based, open-source hybrid metaheuristic optimization algorithm (available from https://***/SlaybaughLab/Gnowee) . Gnowee is designed for rapid convergence to nearly globally optimum solutions for complex, constrained nuclear engineering problems with mixed-integer (MI) and combinatorial design vectors and high-cost, noisy, discontinuous, black box objective function evaluations. Gnowee's hybrid metaheuristic framework is a new combination of a set of diverse, robust heuristics that appropriately balance diversification and intensification strategies across a wide range of optimization problems. There are many potential applications for this novel algorithm both within the nuclear community and beyond. Given that a set of well-known and studied nuclear benchmarks does not exist for the purpose of testing optimization algorithms, comparisons between Gnowee and several well-established metaheuristic algorithms are made for a set of 18 established continuous, MI, and combinatorial benchmarks representing a wide range of types of engineering problems and solution space behaviors. These results demonstrate Gnoweee to have superior flexibility and convergence characteristics over this diverse set of design spaces. We anticipate this wide range of applicability will make this algorithm desirable for many complex engineering applications.

关键词： Evolutionary computation metaheuristic optimization algorithms

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Effect of inexact adjoint solutions on the discrete-adjoint approach to gradient-based optimization

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optimization AND ENGINEERING 2022年第3期23卷 1643-1676页

作者： Brown, David A. Nadarajah, Siva McGill Univ Montreal PQ H3A 2T5 Canada

New algorithms are developed to adapt the convergence tolerances for the constraint and adjoint equations for practical engineering optimization problems solved using the discrete adjoint approach. The algorithms are designed to achieve design order convergence of the optimization algorithm at reduced computational cost. We have found based on analysis and supported by numerical experimentation that adapting both the constraint and adjoint equation tolerances based on the norm of the gradient is sufficient to achieve design order convergence. We have also found that adapting the constraint equation tolerance is necessary, though we were not able to show analytically that adapting the adjoint equation tolerance is necessary. Based on the numerical experimentation, it appears that design order convergence can be achieved without adapting the adjoint equation in some cases but not others. The gain in computational efficiency using the new algorithms over using fixed tolerances is demonstrated through three numerical test problems.

关键词： optimization algorithms Computational fluid dynamics Inexact optimization Discrete adjoint Gradient-based optimization

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A modified particle swarm approach for multi-objective optimization of laminated composite structures

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STRUCTURAL ENGINEERING AND MECHANICS 2012年第3期42卷 335-352页

作者： Sepehri, A. Daneshmand, F. Jafarpur, K. Shiraz Univ Sch Mech Engn Shiraz Iran McGill Univ Dept Mech Engn Montreal PQ H3A 2K6 Canada

Particle Swarm optimization (PSO) is a stochastic population based optimization algorithm which has attracted attentions of many researchers. This method has great potentials to be applied to many optimization problems. Despite its robustness the standard version of PSO has some drawbacks that may reduce its performance in optimization of complex structures such as laminated composites. In this paper by suggesting a new variation scheme for acceleration parameters and inertial weight factors of PSO a novel optimization algorithm is developed to enhance the basic version's performance in optimization of laminated composite structures. To verify the performance of the new proposed method, it is applied in two multi-objective design optimization problems of laminated cylindrical. The numerical results from the proposed method are compared with those from two other conventional versions of PSO-based algorithms. The convergancy of the new algorithms is also compared with the other two versions. The results reveal that the new modifications in the basic forms of particle swarm optimization method can increase its convergence speed and evade it from local optima traps. It is shown that the parameter variation scheme as presented in this paper is successful and can evenfind more preferable optimum results in design of laminated composite structures.

关键词： cylindrical composite laminated shell optimization algorithms particle swarm optimization

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