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International conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech)

作者： Md. Golam Sadeque Md. Keas Al Fayshal Md. Shahorin Islam Shaun A.H.M. Iftekharul Ferdous Md. Galib Hasan Md. Tanjil Sarker Department of Electrical and Electronic Engineering Pabna University of Science and Technology Pabna Bangladesh Faculty of Engineering Multimedia University Cyberjaya Malaysia

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

Power amplifiers are important in today's wireless communication systems. This paper describes the design, tuning, and performance optimization of a class-AB power amplifier operating in the 3.7 GHz – 4.2 GHz frequency range with an output power of 40 dBm at a gain of about 10 dB. A Wolfspeed CGH40010F GaN HEMT transistor, which operates from DC with a 28V supply, is employed in the amplifier's design to satisfy the specifications. Using the transistor nonlinear simulation model created by Cree Inc., an amplifier is designed and simulated in Keysight Advanced Design System (ADS) software. Keeping the issues of linearity and efficiency in mind, the biasing point is chosen for the class AB configuration. In biasing, the drain current is 48 mA and the gate-source voltage is −3.05 V. The frequency range for the design is chosen based on applications. Then, the stability analysis of the transistor is performed. The simulation results also show that the transistor becomes broadband stable when a resistor is added to its input side. Source-pull and load-pull simulation is used to extract source and load impedances for optimal power and efficiency. From the perspectives of efficiency and linearity, the installed PA performs exceptionally well across the entire frequency range. According to the simulation, the RF PA's flat gain fluctuated between 8.19 dB and 10.90 dB for the frequency range of 3.7 GHz to 4.2 GHz. At 3.8 GHz, the maximum power added efficiency (PAE) was found to be 64.52%, while the maximum drain efficiency is simulated to reach 67.78% at an output power of nearly 40 dBm.

关键词： Radio frequency performance evaluation Wireless communication Simulation Power amplifiers Linearity Transistors Power generation Gain Tuning

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Simulated annealing and genetic algorithms for the facility layout problem: A survey

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COMPUTATIONAL optimization AND APPLICATIONS 1997年第1期7卷 111-126页

作者： Mavridou, TD Pardalos, PM Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville USA

The facility layout problem (FLP) has many practical applications and is known to be NP-hard. During recent decades exact and heuristic approaches have been proposed in the literature to solve FLPs. In this paper we review the most recent developments regarding simulated annealing and genetic algorithms for solving facility layout problems approximately.

关键词： heuristics simulated annealing genetic algorithms facility layout problem parallel algorithms combinatorial optimization

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Framework for wrapping binary swarm optimizers to the hybrid parallel cooperative coevolving version

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS software TOOLS AND APPLICATIONS 2024年第2期27卷 1683-1697页

作者： Ipchi Sheshgelani, Mohammadreza Pashazadeh, Saeid Salehpoor, Pedram Univ Tabriz Fac Elect & Comp Engn Tabriz Iran

In recent decades with the increase in the complexity of the problems, the need for high-performance and scalable optimization tools has been inevitable. Among different phenomena introduced to optimization problems, naturally inspired algorithms are favored. Also, encountering large-scale problems, high-performance tools like parallel implementations should be needed. In order to tackle this problem, the framework has been proposed that can wrap any swarm algorithm into an outperformer parallel and hybrid version. Six accepted swarm algorithms are selected to evaluate performance and compare the wrapped version with standard versions. Six nonlinear high-dimension benchmark functions are used to test the proposed algorithms. The experimental results show that wrapped versions outperform standard versions with the measurement of average best fitness.

关键词： optimization Cooperative coevolution Parallel computing Hybrids Binary search space

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On the iterative solution of KKT systems in potential reduction software for large-scale quadratic problems

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COMPUTATIONAL optimization AND APPLICATIONS 2007年第1期38卷 27-45页

作者： Cafieri, S. D'Apuzzo, M. De Simone, V. di Serafino, D. Univ Naples 2 Dept Math I-81100 Caserta Italy

Iterative solvers appear to be very promising in the development of efficient software, based on Interior Point methods, for large-scale nonlinear optimization problems. In this paper we focus on the use of preconditioned iterative techniques to solve the KKT system arising at each iteration of a Potential Reduction method for convex Quadratic Programming. We consider the augmented system approach and analyze the behaviour of the Constraint Preconditioner with the Conjugate Gradient algorithm. Comparisons with a direct solution of the augmented system and with MOSEK show the effectiveness of the iterative approach on large-scale sparse problems.

关键词： Interior Point methods KKT system Constraint Preconditioner large-scale Quadratic Programming

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Sparse linear least squares problems in optimization

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COMPUTATIONAL optimization AND APPLICATIONS 1997年第1期7卷 89-110页

作者： Matstoms, P Department of Mathematics Linköping University Linköping Sweden

Numerical and computational aspects of direct methods for large and sparse least squares problems are considered. After a brief survey of the most often used methods, we summarize the important conclusions made from a numerical comparison in MATLAB. Significantly improved algorithms have during the last 10-15 years made sparse QR factorization attractive, and competitive to previously recommended alternatives. Of particular importance is the multifrontal approach, characterized by low fill-in, dense subproblems and naturally implemented parallelism. We describe a Householder multifrontal scheme and its implementation on sequential and parallel computers. Available software has in practice a great influence on the choice of numerical algorithms. Less appropriate algorithms are thus often used solely because of existing software packages. We briefly survey software packages for the solution of sparse linear least squares problems. Finally, we focus on various applications from optimization, leading to the solution of large and sparse linear least squares problems. In particular, we concentrate on the important case where the coefficient matrix is a fixed general sparse matrix with a variable diagonal matrix below. Inner point methods for constrained linear least squares problems give, for example, rise to such subproblems. Important gains can be made by taking advantage of structure. Closely related is also the choice of numerical method for these subproblems. We discuss why the less accurate normal equations tend to be sufficient in many applications.

关键词： least squares problem sparse matrices QR factorization multifrontal method

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***: A high-performance and Flexible Toolbox for Frank-Wolfe algorithms and Conditional Gradients

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INFORMS JOURNAL ON COMPUTING 2022年第5期34卷 2611-2620页

作者： Besancon, Mathieu Carderera, Alejandro Pokutta, Sebastian Zuse Inst Berlin D-14195 Berlin Germany Georgia Inst Technol Atlanta GA 30308 USA Tech Univ Berlin D-10623 Berlin Germany

We present ***, an open-source implementation of several popular Frank-Wolfe and conditional gradients variants for first-order constrained optimization. The package is designed with flexibility and high performance in mind, allowing for easy extension and relying on few assumptions regarding the user-provided functions. It supports Julia's unique multiple dispatch feature, and it interfaces smoothly with generic linear optimization formulations using ***.

关键词： first-order methods optimization software nonlinear programming

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Numerical experiences with new truncated Newton methods in large scale unconstrained optimization

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COMPUTATIONAL optimization AND APPLICATIONS 1997年第1期7卷 71-87页

作者： Lucidi, S Roma, M Dipartimento di Informatica e Sistemistica Università di Roma “La Sapienza” Roma Italy

Recently, in [12] a very general class of truncated Newton methods has been proposed for solving large scale unconstrained optimization problems. In this work we present the results of an extensive numerical experience obtained by different algorithms which belong to the preceding class. This numerical study, besides investigating which are the best algorithmic choices of the proposed approach, clarifies some significant points which underlies every truncated Newton based algorithm.

关键词： large scale unconstrained optimization truncated Newton methods negative curvature direction curvilinear linesearch Lanczos method

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A Generator of Multiextremal Test Classes With Known Solutions for Black-Box-Constrained Global optimization

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2022年第6期26卷 1261-1270页

作者： Sergeyev, Yaroslav D. D. Kvasov, Dmitri E. E. Mukhametzhanov, Marat S. S. Univ Calabria DIMES Arcavacata Di Rende 87036 Italy IITMM Lobachevsky State Univ Nizhnii Novgorod 603950 Russia Italian Natl Res Council Inst High Performance Computingand Networking I-87036 Arcavacata Di Rende Italy

A generator of classes of multidimensional test problems for benchmarking continuous constrained global optimization methods is proposed. It is based on the generator of test classes for global optimization proposed in 2003 by Gaviano, Kvasov, Lera, and Sergeyev and extends the previous generation procedure from the box-constrained case to the case of nonlinear constraints. The user has the possibility to fix the difficulty of tests in an intuitive way by choosing several types of constraints. A detailed information (including the global solution) for each of 100 problems in each generated class is provided to the user. The generator is particularly suited for testing black-box optimization algorithms that normally address low or medium dimensional problems with hard to evaluate objective functions.

关键词： optimization Generators Linear programming Benchmark testing Visualization software algorithms software Benchmarking optimization software black-box global optimization GKLS-generator known global minima problems with nonlinear constraints

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Parameter optimization of software reliability models using improved differential evolution algorithm

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MATHEMATICS AND COMPUTERS IN SIMULATION 2020年 177卷 46-62页

作者： Yaghoobi, Tahere Payame Noor Univ Dept Comp Engn & Informat Technol Tehran Iran

Differential evolution (DE) is known as a strong and simple optimization method able to work with non-differential, nonlinear, and multimodal functions. This paper proposes a modified differential evolution (MDE) algorithm for solving a high dimensional nonlinear optimization problem. The issue is finding maximum likelihood estimation (MLE) for the parameters of a non-homogeneous Poisson process (NHPP) software reliability model. We make two modifications to DE: a mutation scheme based on a new affine combination of three points for increasing the exploration power of the algorithm, and another is a uniform scaling crossover scheme to increase the exploitation ability of the algorithm. The performance of the proposed scheme is empirically validated using five software reliability models on three software failure datasets. Analysis of research findings indicates that the proposed scheme enhances the convergence speed of the DE algorithm, and preserves the quality of the solution. A comparison with two other peer algorithms is also shown the superiority of the proposed algorithm.

关键词： Parameter estimation software reliability model Maximum likelihood estimation Differential evolution

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Broyden's quasi-Newton methods for a nonlinear system of equations and unconstrained optimization: a review and open problems

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optimization METHODS & software 2014年第5期29卷 937-954页

作者： Al-Baali, Mehiddin Spedicato, Emilio Maggioni, Francesca Sultan Qaboos Univ Dept Math & Stat Muscat 123 Oman Bergamo Univ Dept Management Econ & Quantitat Methods Bergamo Italy

Quasi-Newton methods were introduced by Charles Broyden [A class of methods for solving nonlinear simultaneous equations, Math Comp. 19 (1965), pp. 577-593] as an alternative to Newton's method for solving nonlinear algebraic systems;in 1970 Broyden [The convergence of a class of double rank minimization algorithms, IMA J Appl Math. 6, part I and II (1970), pp. 76-90, 222-231] extended them to nonlinear unconstrained optimization as a generalization of the DFP method which is proposed by Davidon [Variable metric method for minimization (revised), Technical Report ANL-5990, Argonne National Laboratory, USA, 1959] and investigated by Fletcher and Powell [A rapidly convergent descent method for minimization, Comput J. 6 (1963), pp. 163-168]. Such methods (in particular, the BFGS (Broyden-Fletcher-Goldfarb-Shanno) method) are very useful in practice and have been subject to substantial theoretical analysis, albeit some problems are still open. In this paper we describe properties of these methods as derived by Broyden and then further developed by other researchers, especially with reference to improvement of their computational performance.

关键词： nonlinear algebraic equations unconstrained optimization quasi-Newton methods modified methods line search technique ABS methods finite termination optimal conditioning

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