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2008 IEEE Antennas and Propagation Society International Symposium (APSURSI 2008), vol.8

作者： M. Brignone G. Bozza A. Randazzo R. Aramini M. Piana M. Pastorino Department of Mathematics University of Genoa Genoa Italy Department of Biophysical and Electronic Engineering University of Genoa Genoa Italy Department of Computer Science University of Trento Trento Italy Department of Computer Science University of Verona Verona Italy

ISBN: (纸本)9781424420414

In the last decades, inverse scattering problems have been faced by several stochastic methods with satisfactory results, thanks to their global optimization capabilities and their ability to take into account a-priori information [1-3]. Nevertheless the computational burden required by these algorithms is often heavy. To overcome such a drawback, in this contribution a new hybrid approach is presented: a qualitative inversion technique is used to provide useful information about the location and shape of the scattering;these information are used to facilitate the convergence of a stochastic procedure, which is responsible for retrieving the point values of the dielectric permittivity. Namely, the proposed method consists of two steps: firstly the support of the scatterers is determined by the no-sampling Linear Sampling Method (nLSM), which is a fast inversion technique for visualizing the profile of a scatterer from measurements of the far-field data [4];then the dielectric parameters of interest are retrieved performing the minimization of a suitable cost function by means of an Ant Colony optimization algorithm (ACO). ACO is a recently introduced stochastic optimization algorithm, inspired by social behavior of ant colonies, to solve hard minimization problems [5,6]. In this paper the described technique will be applied to TM two-dimensional inverse scattering problems. The input data of this approach is the far-field matrix whose ij-entry is the far-field sample u{sub}∞(x{sub}i, d{sub}j) measured in the direction x{sub}i when the targets are illuminated by a plane wave impinging from direction d{sub}j.

关键词： Ant colony optimization Scattering inverse scattering Cost functions far-field optimization algorithms INVERSE SCATTERING PROBLEM ADMINISTRATIVE CONSENT ORDER Stochastic

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Future Trends in the Design of Memetic algorithms: the Case of the Linear Ordering Problem

arXiv

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arXiv 2024年

作者： Lugo, Lázaro Segura, Carlos Miranda, Gara Callejón Jalisco s/n Guanajuato36240 Mexico Departamento de Ingeniería Informática y de Sistemas Universidad de La Laguna Camino San Francisco de Paula 19 Canarias Santa Cruz de Tenerife38200 Spain

The way heuristic optimizers are designed has evolved over the decades, as computing power has increased. Such has been the case for the Linear Ordering Problem (LOP), a field in which trajectory-based strategies led the way during the 1990s, but which have now been surpassed by memetic schemes. This paper focuses on understanding how the design of LOP optimizers will change in the future, as computing power continues to increase, yielding two main contributions. On the one hand, a metaheuristic was designed that is capable of effectively exploiting a large amount of computational resources, specifically, computing power equivalent to what a recent core can output during runs lasting over four months. Our analyses show that as the power of the computational resources increases, it will be necessary to boost the capacities of the intensification methods applied in the memetic algorithms to keep the population from stagnating. And on the other, the best-known results for today’s most challenging set of instances (xLOLIB2) were significantly outperformed. New bounds were established in this benchmark, which provides a new frame of reference for future research. © 2024, CC BY-NC-ND.

关键词： optimization algorithms

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HOW MANY CONTINUOUS MEASUREMENTS ARE NEEDED TO LEARN A VECTOR?

arXiv

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arXiv 2024年

作者： Krieg, David Novak, Erich Ullrich, Mario University of Passau Germany Friedrich Schiller University Jena Germany Johannes Kepler University Linz Austria

One can recover vectors from Rm with arbitrary precision, using only ⌈log2(m+1)⌉+1 continuous measurements that are chosen adaptively. This surprising result is explained and discussed, and we present applications to ... 详细信息

关键词： optimization algorithms

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KKT-INFORMED NEURAL NETWORK

arXiv

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arXiv 2024年

作者： Femine, Carmine Delle Department of Data Intelligence for Energy and Industrial Processes Vicomtech Foundation Donostia-San Sebastián20009 Spain

A neural network-based approach for solving parametric convex optimization problems is presented, where the network estimates the optimal points given a batch of input parameters. The network is trained by penalizing violations of the Karush-Kuhn-Tucker (KKT) conditions, ensuring that its predictions adhere to these optimality criteria. Additionally, since the bounds of the parameter space are known, training batches can be randomly generated without requiring external data. This method trades guaranteed optimality for significant improvements in speed, enabling parallel solving of a class of optimization problems. © 2024, CC BY.

关键词： optimization algorithms

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Direct Search Versus Simulated Annealing on Two High Dimensional Problems 11

Direct Search Versus Simulated Annealing on Two High Dimensi...

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Simulation Multiconference

作者： David R. Easterling Layne T. Watson Michael L. Madigan Departments of Computer Science Virginia Polytechnic Institute and State University Engineering Science & Mechanics Virginia Polytechnic Institute and State University

ISBN: (纸本)9781617828409

Two global optimization problems with high dimensionality and many local minima are investigated with two different optimization algorithms: DIRECT and simulated annealing. The problems include a difficult biomechanics problem with a great deal of experimental noise and a deterministic integer programming problem with a known global minimum.

关键词： High-performance computing Stochastic optimization Deterministic optimization Biomechanics Quadratic optimization Simulated annealing DIRECT simulated annealing High Performance Computing Biomechanics Mentoring direct search Global Minimum optimization algorithms

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Optimal Radome Design with Particle Swarm optimization

Optimal Radome Design with Particle Swarm Optimization

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2008 IEEE Antennas and Propagation Society International Symposium (APSURSI 2008), vol.6

作者： Hidetoshi Chiba Yoshio Inasawa Hiroaki Miyashita Yoshihiko Konishi Mitsubishi Electric Corporation Limited Kamakura Kanagawa Japan

ISBN: (纸本)9781424420414

This paper presents the optimal radome design using optimization algorithm. Design of a radome is, in general, a formidable problem, since the design philosophy needs to take into account the arbitrariness with respect to the shape of the radome and the incidence angle of the incident wave into the radome. To circumvent this problem, optimization algorithm appears to be a powerful tool. In this article, particle swarm optimization (PSO), which has recently drawn considerable attention in a wide range of applications, is employed for the design of a radome, in which the frequency characteristics of the transmission coefficient of the radome is adopted as the objective function, and the radome wall thickness and the shape of the radome are optimized. In addition, for the PSO algorithm, we introduce a concept analogous to "mutation" in GA so as to enhance the globality of the optimal solution, and call it as MPSO (Mutated PSO). We deal with MPSO, PSO and GA, and report the comparisons and characteristics of the optimized radome.

关键词： Radomes radome design parametric subharmonic oscillator Particle swarm optimization Methylprednisolone Suleptanate design theory frequency characteristic angle of incidence Gallium optimization algorithms TRANSMISSION COEFFICIENT

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A Systematic Approach to Extremum Seeking Based on Parameter Estimation

A Systematic Approach to Extremum Seeking Based on Parameter...

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2010 49th IEEE Conference on Decision and Control

作者： Dragan Nesic Alireza Mohammadi Chris Manzie Department of Electrical and Electronic Engineering University of Melbourne Parkville 3010 Victoria Australia Department of Mechanical Engineering University of Melbourne Parkville 3010 Victoria Australia

ISBN: (纸本)9781424477456

We present a systematic approach for design of extremum seeking (ES) controllers for a class of uncertain plants that are parameterized with unknown parameters. First, we present results for static plants and show how it is possible to combine, under certain general conditions, an arbitrary optimization method with an arbitrary parameter estimation method in order to obtain extremum seeking. Our main results also specify how controller needs to be tuned in order to achieve extremum seeking. Then, we consider dynamic plants and separate our results into the stable plant case and unstable plant case. For each of these cases, we present conditions on general plants, controllers, observers, parameter estimators and optimization algorithms that guarantee semi-global practical convergence to the extremum when controller parameters are tuned appropriately. Our results apply to general nonlinear plants with multiple inputs and multiple parameters.

关键词： Parameter estimation Controllers multi-input optimization algorithms optimization method

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Exploring the Performance-Reproducibility Trade-off in Quality-Diversity

arXiv

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arXiv 2024年

作者： Flageat, Manon Janmohamed, Hannah Lim, Bryan Cully, Antoine Adaptive and Intelligent Robotics Lab Department of Computing Imperial College London United Kingdom

Quality-Diversity (QD) algorithms have exhibited promising results across many domains and applications. However, uncertainty in fitness and behaviour estimations of solutions remains a major challenge when QD is used in complex real-world applications. While several approaches have been proposed to improve the performance in uncertain applications, many fail to address a key challenge: determining how to prioritise solutions that perform consistently under uncertainty, in other words, solutions that are reproducible. Most prior methods improve fitness and reproducibility jointly, ignoring the possibility that they could be contradictory objectives. For example, in robotics, solutions may reliably walk at 90% of the maximum velocity in uncertain environments, while solutions that walk faster are also more prone to falling over. As this is a trade-off, neither one of these two solutions is "better" than the other. Thus, algorithms cannot intrinsically select one solution over the other, but can only enforce given preferences over these two contradictory objectives. In this paper, we formalise this problem as the performance-reproducibility trade-off for uncertain QD. We propose four new a-priori QD algorithms that find optimal solutions for given preferences over the trade-offs. We also propose an a-posteriori QD algorithm for when these preferences cannot be defined in advance. Our results show that our approaches successfully find solutions that satisfy given preferences. Importantly, by simply accounting for this trade-off, our approaches perform better than existing uncertain QD methods. This suggests that considering the performance-reproducibility trade-off unlocks important stepping stones that are usually missed when only performance is optimised. © 2024, CC BY.

关键词： optimization algorithms

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Sampling approach to sparse approximation problem: determining degrees of freedom by simulated annealing

Sampling approach to sparse approximation problem: determini...

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European Signal Processing Conference

作者： Tomoyuki Obuchi Yoshiyuki Kabashima Department of Mathematical and Computing Science Tokyo Institute of Technology

ISBN: (纸本)9781509018918

The approximation of a high-dimensional vector by a small combination of column vectors selected from a fixed matrix has been actively debated in several different disciplines. In this paper, a sampling approach based on the Monte Carlo method is presented as an efficient solver for such problems. Especially, the use of simulated annealing (SA), a metaheuristic optimization algorithm, for determining degrees of freedom (the number of used columns) by cross validation is focused on and tested. Test on a synthetic model indicates that our SA-based approach can find a nearly optimal solution for the approximation problem and, when combined with the CV framework, it can optimize the generalization ability. Its utility is also confirmed by application to a real-world supernova data set.

关键词： Degree of freedom simulated annealing approximation Monte Carlo technique optimization algorithms cross-validation optimal solution column vector

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Performance of Uniaxial Multilayer Cylinders and Spheres Used for Invisible Cloak Realization

Performance of Uniaxial Multilayer Cylinders and Spheres Use...

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European Conference on Antennas and Propagation

作者： Branimir Ivsic Tin Komljenovic Zvonimir Sipus University of Zagreb Faculty of Electrical Engineering and Computing Department of Wireless Communications

ISBN: (纸本)9781457702501

The paper presents the analysis of cylindrical and spherical structures used for invisible cloak realization. Different cylindrical and spherical cloak realizations were analyzed in terms of bistatic scattering cross section. In addition, optimization algorithm was connected with the analysis routine to find the constitutive parameters of the cloak that reduce both backscattering and forward scattering. The results show that it is possible to obtain adequate invisibility performance by covering the PEC sphere with only 2 thin anisotropic layers.

关键词： Spheres invisibility performance Spheroidal structure optimization algorithms Cylindrical backscattering Cylinders Cylinders Cross section bistatic scattering forward scattering multilayers swine enteric calicivirus

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