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Generalized Daily Reference Evapotranspiration Models Based on a Hybrid optimization Algorithm Tuned Fuzzy Tree Approach

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WATER RESOURCES MANAGEMENT 2023年第1期37卷 193-218页

作者： Roy, Dilip Kumar Sarkar, Tapash Kumar Biswas, Sujit Kumar Datta, Bithin Bangladesh Agr Res Inst Irrigat & Water Management Div Joydebpur Gazipur 1701 Bangladesh Bangladesh Rice Res Inst Grain Qual & Nutr Div Joydebpur Gazipur 1701 Bangladesh James Cook Univ Coll Sci & Engn Discipline Civil Engn Townsville Qld 4811 Australia

Reference evapotranspiration (ET0) is an important driver in managing scarce water resources and making decisions on real-time and future irrigation scheduling. Therefore, accurate prediction of ET0 is crucial in the water resources management discipline. In this study, the prediction of ET0 was performed by employing several optimization algorithms tuned Fuzzy Inference System (FIS) and Fuzzy Tree (FT) models, for the first time, whose generalization capability was tested using data from other stations. The FISs and FTs were developed through parameters tuning using Genetic Algorithm (GA), Particle Swarm optimization (PSO), Pattern Search (PS), and their combinations. The FT was developed by combining several FIS objects that received ranked meteorological variables. A total of 50 FIS and FT models were developed and the model ranking was performed utilizing Shannon's Entropy (SE). Evaluation outcomes revealed the superiority of the hybrid PSO-GA tuned Sugeno type 1 FT model (with R = 0.929, NRMSE = 0.169, accuracy = 0.999, NS = 0.856, and IOA = 0.985) over others. For evaluating the generalization capability of the best model, three different parts of datasets (all-inclusive, 1(st) half, and 2(nd) half) of the five test stations were evaluated. The proposed hybrid PSO-GA tuned Sugeno type 1 FT model performed similarly well, according to the findings, on the datasets of the test stations. The study concluded that the hybrid PSO-GA tuned Sugeno type 1 FT approach, which was composed of several standalone FIS objects, was suitable for predicting daily ET0 values.

关键词： Reference evapotranspiration Meteorological variables Fuzzy inference system Fuzzy tree optimization algorithms Shannon's entropy

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Approximation of transformation media-based reshaping action by genetic optimization

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APPLIED PHYSICS A-MATERIALS SCIENCE & PROCESSING 2014年第2期117卷 597-604页

作者： Ozgun, Ozlem Kuzuoglu, Mustafa TED Univ Dept Elect & Elect Engn Ankara Turkey Middle E Tech Univ Dept Elect & Elect Engn TR-06531 Ankara Turkey

The reshaping technique that is based on transformation optics renders an object to be perceived as if it has a different shape irrespective of the location of the observer. This is achieved by coating the object with an anisotropic and spatially varying metamaterial layer by employing the concept of coordinate transformation. This paper presents a design approach for numerical approximation of reshaper medium by means of concentric layers coated over the object, each of which has simpler and easily realizable material parameters that are determined by the genetic optimization algorithm. The results of various finite element simulations are presented.

关键词： transformation media-based reshaping action material parameter approximation Coordinate transformations numerical approximation design approach Anisotropy optimization algorithms

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optimization techniques to enhance the performance of induction motor drives: A review

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2018年第Part2期81卷 1611-1626页

作者： Hannan, M. A. Ali, Jamal A. Mohamed, Azah Hussain, Aini Univ Tenaga Nas Dept Elect Power Engn Coll Engn Kajang 43000 Malaysia Minist Elect Gen Co Elect Prod Middle Reg Baghdad Iraq Univ Kebangsaan Malaysia Dept Elect Elect & Syst Engn Bangi Malaysia

Induction motor (IM) drives, specifically the three-phase IMs, are a nonlinear system that are difficult to explain theoretically because of their sudden changes in load or speed conditions. Thus, an advanced controller is needed to enhance IM performance. Among numerous control techniques, fuzzy logic controller (FLC) has increasing popularity in designing complex IM control system due to their simplicity and adaptability. However, the performance of FLCs depends on rules and membership functions (MFs), which are determined by a trial-and-error procedure. The main objective of this paper is to present a critical review on the control and optimization techniques for solving the problems and enhancing the performance of IM drives. A detailed study on the control of variable speed drive, such as scalar and vector, is investigated. The scalar control functions of speed and V/f control are explained in an open-and closed-loop IM drive. The operation, advantages, and limitations of the direct and indirect field-oriented controls of vector control are also demonstrated in controlling the IM drive. A comprehensive review of the different types of optimization techniques for IM drive applications is highlighted. The rigorous review indicates that existing optimization algorithms in conventional controller and FLC can be used for IM drive. However, some problems still exist in achieving the best MF and suitable parameters for IM drive control. The objective of this review also highlights several factors, challenges, and problems of the conventional controller and FLC of the IM drive. Accordingly, the review provides some suggestions on the optimized control for the research and development of future IM drives. All the highlighted insights and recommendations of this review will hopefully lead to increasing efforts toward the development of advanced IM drive controllers for future applications.

关键词： Induction motor drive optimization algorithms Scalar control Vector control Fuzzy logic controller

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Polynomial metamodel based fast optimization of nano-CMOS oscillator circuits

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ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING 2014年第3期79卷 437-453页

作者： Mohanty, Saraju P. Kougianos, Elias Univ N Texas Denton TX 76203 USA

Modern consumer electronics are designed as analog/mixed-signal systems-on-chip (AMS-SoCs). In an AMS-SoC, the analog and mixed-signal portions have not received systematic attention due to their complex nature and the fact that their optimization and simulation consume significant portions of the design cycle time. This paper presents a new approach to reduce the design cycle time by combining accurate polynomial metamodels and optimization algorithms. The approach relies on a mathematical representation (metamodel or surrogate model) of AMSSoC subsystems/components. Polynomial metamodels are created from post-layout parasitic netlists and provide an accurate representation for each figure-of-merit over the entire design space of the AMS-SoC component. The metamodel approach saves a very significant amount of time during design iterations. Polynomial metamodels are reusable and language independent. Three algorithms are investigated to compare the speed for optimization on the polynomial metamodels. Two widely used circuits have been designed in two different technologies as comparative case studies: an 180 nm LC-VCO and a 45 nm ring oscillator (RO). Experimental results prove that the metamodel- based optimization achieved speed-up as high as 21,600 for the LC-VCO circuit and 11,750 for the RO in comparison to the actual circuit netlist-based (SPICE) optimization, with less than 1 % error. Thus, the paper demonstrates that the polynomial metamodeling approach to the design problem is an effective and accurate means for fast design space exploration and optimization.

关键词： Mixed-signal circuits Oscillator circuits Polynomial metamodeling Performance optimization optimization algorithms

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Application of Monte Carlo stochastic optimization (MOST) to deep learning

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MATHEMATICS AND COMPUTERS IN SIMULATION 2022年第0期199卷 257-271页

作者： Inage, Sin-ichi Hebishima, Hana Fukuoka Univ Dept Mech Engn Fluid Engn Lab Fukuoka 8140180 Japan Fukuoka Univ Dept Mech Engn 8-19-1NanakumaJonan Ku Fukuoka 8140180 Japan

In this paper, we propose a new optimization method based on the Monte Carlo method. The proposed method is applied to several benchmark problems, and the result of applying it to the optimization of neural network is reported. Deep machine learning using neural networks is one of the important keywords to promote innovation in today's advanced information society. Therefore, research on large-scale, high-speed, and high-precision algorithms has been actively conducted. The author has developed an optimization method in which the search region for multivariate parameters constituting the objective function is divided into two regions for each parameter, the integral values of each divided region are numerically calculated by the Monte Carlo method, the magnitude of each integral value is compared, and the optimum point is judged to be in a small region. The proposed method was applied to 50 variable benchmark functions (Schwefel and Ackley Functions), and was compared with the results of genetic algorithm (GA) which was a representative of existing optimization methods. As a result, it was confirmed that the proposed method is faster and more accurate than GA. In addition, the proposed method is applied to machine learning by neural networks, specifically XOR gate circuits and IRS classification problems, and verified. The neural network optimized by MOST reproduced teacher data and test data faster and more accurately than conventional Adam and genetic algorithms. (c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： optimization algorithms Neural network Deep learning Monte Carlo method Genetic algorithm Adam

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Sperm whale algorithm: An effective metaheuristic algorithm for production optimization problems

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JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING 2016年 29卷 211-222页

作者： Ebrahimi, A. Khamehchi, E. Amirkabir Univ Technol Fac Petr Engn Tehran Polytech Tehran Iran

A new optimization algorithm called sperm whale algorithm (SWA) is proposed to solve production optimization problems. This algorithm is based on the sperm whale's lifestyle. Like other population based algorithms, SWA uses a population of solutions to find the optimum answer. One of the advantages of this method over others is that it uses two contradictory types of answers: it uses the worst and the best answers to reach the optimum point. The SWA algorithm was tested on 26 benchmarks and three benchmarks in several dimensions and one production optimization problem. The results and comparison of its performance with other algorithms show that SWA's performance is superior to other algorithms and it could be confidently used in optimization tasks. (C) 2016 Elsevier B.V. All rights reserved.

关键词： optimization algorithms Sperm whale algorithm optimization Production optimization

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On the optimization of the support vector machine regression hyperparameters setting for gas sensors array applications

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2019年 184卷 22-27页

作者： Laref, R. Losson, E. Sava, A. Siadat, M. Univ Lorraine LCOMS EA 7306 F-57000 Metz France

Support Vector Machine Regression (SVR) has been shown to be more accurate compared to other machine learning techniques that are commonly used for chemical sensors arrays applications. However, the performance of SVR depends strongly on the selection of its hyperparameters. Most of time, researchers in this field rely on trivial grid search methods to find suitable values of SVR hyperparameters by minimizing the cross-validation prediction error. This method is not a practical solution because of the large domain of possible parameter values, which is further exacerbated by the lack of prior knowledge on the data. In this article, we investigate the optimization of SVR hyperparameters by combining the SVR algorithm with a simple algorithm for SVR parameters selection. We begin by studying the influence of each hyperparameter on SVR performance. We then propose the Generalized Pattern Search algorithm (GPS) as a faster alternative to determine these hyperparameters. Finally, we demonstrate that the proposed GPS algorithm, with its simplicity and robustness, gives similar results compared to more complicated alternatives, such as Genetic algorithms, Simulating Annealing, Bayesian optimization or Particle Swarm optimization.

关键词： Gas sensors Gas quantification Support vector machine regression optimization algorithms Generalized pattern search

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Security optimization for an AF MIMO Two-Way Relay-Assisted Cognitive Radio Nonorthogonal Multiple Access Networks With SWIPT

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2022年 17卷 1481-1496页

作者： Hu, Changjie Li, Quanzhong Zhang, Qi Qin, Jiayin Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou 510006 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

This paper investigates the physical layer security issue in an amplify-and-forward (AF) multi-input multi-output (MIMO) two-way relay assisted cognitive radio (CR) nonorthogonal multiple access (NOMA) network, where the simultaneous wireless information and power transfer (SWIPT) technology is employed to improve network energy efficiency. We consider the scenario that a pair of primary users and two pairs of secondary users (SUs) exchange information via a MIMO two-way relay, where the edge SU of each SU pair is untrusted and tries to wiretap the central SU's information. For ensuring security, we aim to maximize the sum achievable secrecy rate (SASR) by jointly optimizing the power allocation at all users, power splitting factor and relay beamforming subject to the quality of service (QoS), energy harvesting and transmit power constraints. The formulated optimization problem is highly nonconvex due to coupling variables, thus it is challenging to solve. An effective path-following (PF)-based algorithm is proposed, which is proven to converge to a stationary point. Theoretical and simulation results show that the proposed PF-based algorithm has lower complexity than the state-of-art algorithm. To further reduce complexity, we proposed a zero-forcing (ZF)-based scheme. Numerical simulations show that the proposed PF-based algorithm achieves the same SASR as the state-of-art algorithm with moderate complexity, while the proposed ZF-based scheme strikes a good balance between performance and complexity.

关键词： NOMA Relays MIMO communication Security optimization Array signal processing Complexity theory Nonorthogonal multiple access cognitive radio simultaneous wireless information and power transfer two-way relaying physical layer security optimization algorithms

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Distributed Online Aggregative optimization for Dynamic Multirobot Coordination

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第6期68卷 3736-3743页

作者： Carnevale, Guido Camisa, Andrea Notarstefano, Giuseppe Univ Bologna Dept Elect Elect & Informat Engn I-40126 Bologna Italy

This article focuses on an online version of the emerging distributed constrained aggregative optimization framework, which is particularly suited for applications arising in cooperative robotics. Agents in a network want to minimize the sum of local cost functions, each one depending both on a local optimization variable, subject to a local constraint, and on an aggregated version of all the variables (e.g., the mean). We focus on a challenging online scenario in which the cost, the aggregation functions, and the constraints can all change over time, thus enlarging the class of captured applications. Inspired by an existing scheme, we propose a distributed algorithm with constant step size, named projected aggregative tracking, to solve the online optimization problem. We prove that the dynamic regret is bounded by a constant term and a term related to time variations. Moreover, in the static case (i.e., with constant cost and constraints), the solution estimates are proved to converge with a linear rate to the optimal solution. Finally, numerical examples show the efficacy of the proposed approach on a robotic surveillance scenario.

关键词： Cost function Costs Heuristic algorithms Distributed algorithms Surveillance Linear programming Mirrors Cooperative control distributed optimization optimization algorithms

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Multicanonical basin hopping: A new global optimization method for complex systems

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JOURNAL OF CHEMICAL PHYSICS 2004年第12期120卷 5536-5542页

作者： Zhan, LX Piwowar, B Liu, WK Hsu, PJ Lai, SK Chen, JZY Univ Waterloo Dept Phys Waterloo ON N2L 3G1 Canada Natl Cent Univ Dept Phys Chungli 320 Taiwan

We introduce a new optimization algorithm that combines the basin-hopping method, which can be used to efficiently map out an energy landscape associated with minima, with the multicanonical Monte Carlo method, which encourages the system to move out of energy traps during the computation. As an example of implementing the algorithm for the global minimization of a multivariable system, we consider the Lennard-Jones systems containing 150-185 particles, and find that the new algorithm is more efficient than the original basin-hopping method. (C) 2004 American Institute of Physics.

关键词： Complex systems optimization algorithms Energy sources Global optimization optimization method ENERGY TRAPPING

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