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Parameter estimation of bilinear systems based on an adaptive particle swarm optimization

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2010年第7期23卷 1105-1111页

作者： Modares, Hamidreza Alfi, Alireza Sistani, Mohammad-Bagher Naghibi Shahrood Univ Technol Fac Elect & Robot Engn Shahrood *** Iran Ferdowsi Univ Mashhad Dept Elect Engn Mashhad 917751111 Iran

Bilinear models can approximate a large class of nonlinear systems adequately and usually with considerable parsimony in the number of coefficients required. This paper presents the application of Particle Swarm optimization (PSO) algorithm to solve both offline and online parameter estimation problem for bilinear systems. First, an Adaptive Particle Swarm optimization (APSO) is proposed to increase the convergence speed and accuracy of the basic particle swarm optimization to save tremendous computation time. An illustrative example for the modeling of bilinear systems is provided to confirm the validity, as compared with the Genetic Algorithm (GA), Linearly Decreasing Inertia Weight PSO (LDW-PSO), Nonlinear Inertia Weight PSO (NDW-PSO) and Dynamic Inertia Weight PSO (DIW-PSO) in terms of parameter accuracy and convergence speed. Second. APSO is also improved to detect and determine varying parameters. In this case, a sentry particle is introduced to detect any changes in system parameters. Simulation results confirm that the proposed algorithm is a good promising particle swarm optimization algorithm for online parameter estimation. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Bilinear systems Parameter estimation Adaptive particle swarm optimization Inertia weight optimization algorithms

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Quasi-Three-Dimensional Aerodynamic Solver for Multidisciplinary Design optimization of Lifting Surfaces

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JOURNAL OF AIRCRAFT 2014年第2期51卷 547-558页

作者： Mariens, J. Elham, A. van Tooren, M. J. L. Delft Univ Technol Flight Performance & Prop Grp Fac Aerosp Engn NL-2629 HS Delft Netherlands

This paper presents the development of a quasi-three-dimensional aerodynamic solver, which provides accurate results for wing drag comparable to the higher-fidelity aerodynamic solvers at significantly lower computational costs. The proposed solver calculates the viscous wing drag using the combination of a two-dimensional airfoil analysis tool with a vortex lattice code. Validation results show that the results of the quasi-three-dimensional solver are in good agreement with higher-fidelity computational fluid dynamics solvers. The quasi-three-dimensional solver is used for a wing shape multidisciplinary design optimization. A multidisciplinary design optimization problem is formulated to design the wing shape of a typical passenger aircraft. The aircraft maximum takeoff weight is considered as the objective function. Two optimization algorithms, a local and a global optimum finder, are implemented in the multidisciplinary design optimization system. The optimization results indicate that the global optimization algorithm shows a slightly greater reduction in maximum takeoff weight. However, finding the global optimum needs about 20 times the computational time of the local optimization algorithm.

关键词： Wings global optimum lift Solvers optimization algorithms computational time multidisciplinary design optimization Optimal design lifting surfaces

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optimization techniques for analog and RF circuit designs: an overview

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ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING 2021年第3期106卷 511-524页

作者： Kouhalvandi, Lida Ceylan, Osman Ozoguz, Serdar Istanbul Tech Univ Elect & Commun Engn Dept Istanbul Turkey Maury Microwave Ontario CA 91764 USA

optimizations have gained much consideration from the researchers working in the domains of analog and radio frequency (RF), recently. Dealing with highly nonlinear behavior of active components and aiming to meet design specifications are common issues in all nonlinear circuit designs. As a consequence and accordingly, many studies have been conducted on diverse optimization methods and algorithms for tackling the design problems and meeting optimal solutions with high accuracy. The main purpose of this article is to provide a comprehensive and systematic literature review for the optimization approaches applied by the researchers for designing various analog and microwave circuits. We focus on considering the existing optimization methods from the newly published optimization methods in the last decade. Thus, this study can guide and enlighten complementary metal-oxide-semiconductor analog and RF microwave circuit designers to consider optimization methods commonly used in both areas and to expand conventional performance figures used in their area.

关键词： optimization algorithms CMOS analog circuits RF microwave designs

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Evolving support vector regression using Grey Wolf optimization;forecasting the geomechanical properties of rock

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ENGINEERING WITH COMPUTERS 2022年第2期38卷 1819-1833页

作者： Xu, Chuanhua Amar, Menad Nait Ghriga, Mohammed Abdelfetah Ouaer, Hocine Zhang, Xiliang Hasanipanah, Mahdi Sinosteel Maanshan Inst Min Res Co Ltd State Key Lab Safety & Hlth Met Mines Maanshan 243000 Anhui Peoples R China Sonatrach Div Labs Dept Etud Thermodynam Boumerdes Algeria Univ Pau & Pays Adour E2S Inst Sci Analyt & Phys Chim Environm & Mat IPREM UMR 5254CNRS 2 Ave P AngotTechnopole Helioparc F-64000 Pau France Univ MHamed Bougara Boumerdes Fac Hydrocarbures & Chim Lab Genie Phys Hydrocarbures Ave Independance Boumerdes 35000 Algeria Univ MHamed Bougara Boumerdes Dept Gisement Miniers & Petr Boumerdes Algeria Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam

The geomechanical properties of rock, including shear strength (SS) and uniaxial compressive strength (UCS), are very important parameters in designing rock structures. To improve the accuracy of SS and UCS prediction, this study presented an evolving support vector regression (SVR) using Grey Wolf optimization (GWO). To examine the feasibility and applicability of the SVR-GWO model, the differential evolution (DE) and artificial bee colony (ABC) algorithms were also used. In other words, the SVR hyperparameters were tuned using the GWO, DE, and ABC algorithms. To implement the proposed models, a comprehensive database accessible in an open-source was used in this study. Finally, the comparative experiments such as root mean square error (RMSE) were conducted to show the superiority of the proposed models. The SVR-GWO model predicted the SS and UCS with RMSE of 0.460 and 3.208, respectively, while, the SVR-DE model predicted the SS and UCS with RMSE of 0.542 and 5.4, respectively. Furthermore, the SVR-ABC model predicted the SS and UCS with RMSE of 0.855 and 5.033, respectively. The aforementioned results clearly exhibited the applicability as well as the usability of the proposed SVR-GWO model in the prediction of both SS and UCS parameters. Accordingly, the SVR-GWO model can be also applied to solving various complex systems, especially in geotechnical and mining fields.

关键词： Uniaxial compressive strength Shear strength Support vector regression optimization algorithms

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Data Assimilation and Online optimization With Performance Guarantees

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第5期66卷 2115-2129页

作者： Li, Dan Martinez, Sonia Univ Calif San Diego Dept Mech & Aerosp Engn La Jolla CA 92092 USA

This article considers a class of real-time stochastic optimization problems dependent on an unknown probability distribution. In the considered scenario, data are streaming frequently while trying to reach a decision. Thus, we aim to devise a procedure that incorporates samples (data) of the distribution sequentially and adjusts decisions accordingly. We approach this problem in a distributionally robust optimization framework and propose a novel Online Data Assimilation Algorithm (OnDA Algorithm) for this purpose. This algorithm guarantees out-of-sample performance of decisions with high probability, and gradually improves the quality of the decisions by incorporating the streaming data. We show that the OnDA Algorithm converges under a sufficiently slow data streaming rate, and provide a criteria for its termination after certain number of data have been collected. Simulations illustrate the results.

关键词： optimization Uncertainty Data assimilation Random variables Probability distribution Real-time systems Decision making Computational methods intelligent systems optimization optimization algorithms stochastic systems

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Robust Controller Design for Stochastic Nonlinear Systems via Convex optimization

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第10期66卷 4731-4746页

作者： Tsukamoto, Hiroyasu Chung, Soon-Jo CALTECH Aerosp Pasadena CA 91125 USA CALTECH Dept Aerosp GALCIT Pasadena CA 91125 USA

This article presents ConVex optimization-based Stochastic steady-state Tracking Error Minimization (CV-STEM), a new state feedback control framework for a class of Ito stochastic nonlinear systems and Lagrangian systems. Its innovation lies in computing the control input by an optimal contraction metric, which greedily minimizes an upper bound of the steady-state mean squared tracking error of the system trajectories. Although the problem of minimizing the bound is nonconvex, its equivalent convex formulation is proposed utilizing SDC parameterizations of the nonlinear system equation. It is shown using stochastic incremental contraction analysis that the CV-STEM provides a sufficient guarantee for exponential boundedness of the error for all time with L-2-robustness properties. For the sake of its sampling-based implementation, we present discrete-time stochastic contraction analysis with respect to a state- and time-dependent metric along with its explicit connection to continuous-time cases. We validate the superiority of the CV-STEM to PID, H-infinity, and baseline nonlinear controllers for spacecraft attitude control and synchronization problems.

关键词： LMIs nonlinear systems optimization algorithms robust control stochastic optimal control

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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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optimization WITH AN AUXILIARY CONSTRAINT AND DECOMPOSITION

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SIAM JOURNAL ON CONTROL AND optimization 1990年第1期28卷 137-157页

作者： COHEN, G MIARA, B INST NATL RECH INFORMAT & AUTOMAT F-78150 LE CHESNAYFRANCE ECOLE SUPER INGN ELECTROTECH & ELECTR F-93162 NOISY LE GRANDFRANCE

In the context of decomposition/coordination of a linear quadratic optimal control problem, Takahara’s algorithm was an earlier version of the so-called Interaction Prediction Principle that can be examined in the more general framework of infinite-dimensional constrained optimization problems. This principle is both a decomposition principle and a coordination strategy based on a fixed point scheme. It has been later revisited in the general theory of the Auxiliary Problem Principle and the convergence of corresponding iterative algorithms has been analyzed. In this paper, we keep the same decomposition principle but we propose an alternative coordination strategy. The improvement brought by this new strategy is proved theoretically and illustrated by a numerical example. All of this is based on some manipulation of constrained optimization problems that we call the Auxiliary Constraint Principle.

关键词： 49D27 65K10 93A15 optimization algorithms decomposition coordination interaction prediction principle convergence of algorithms

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The Solar System Algorithm: A Novel Metaheuristic Method for Global optimization

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IEEE ACCESS 2021年 9卷 4542-4565页

作者： Zitouni, Farouq Harous, Saad Maamri, Ramdane Kasdi Merbah Univ Dept Comp Sci Ouargla 30000 Algeria Abdelhamid Mehri Univ LIRE Lab Constantine 25000 Algeria United Arab Emirates Univ UAEU Dept Comp Sci & Software Engn Abu Dhabi U Arab Emirates Abdelhamid Mehri Univ Dept Comp Sci Constantine 25000 Algeria

A novel metaheuristic algorithm for global optimization, called the Solar System Algorithm (SSA), is presented. The proposed algorithm imitates the orbiting behavior of some objects found in the solar system: i.e., Sun, planets, moons, stars, and black holes. SSA is used to solve five well-known engineering design problems: three-bar truss, pressure vessel, tension/compression spring, welded beam, and gear train. The obtained results are compared to 16 state-of-the-art metaheuristic algorithms. They show that SSA is very competitive in solving the considered engineering problems. In addition, the performance of SSA is evaluated on the benchmarks CEC 2014 and CEC 2020. The experimental results are compared to 27 (12 for CEC 2014 and 15 for CEC 2020) metaheuristic algorithms. They demonstrate that SSA is very promising in finding efficient solutions.

关键词： optimization algorithms metaheuristic algorithms solar system constrained engineering optimization test functions benchmark CEC 2014 benchmark CEC 2020 givens matrices

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