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Parameters optimization of support vector machines for imbalanced data using social ski driver algorithm

NEURAL COMPUTING & APPLICATIONS 2020年第11期32卷 6925-6938页

作者： Tharwat, Alaa Gabel, Thomas Frankfurt Univ Appl Sci Fac Comp Sci & Engn Frankfurt Germany

The parameters of support vector machines (SVMs) such as kernel parameters and the penalty parameter have a great influence on the accuracy and complexity of the classification models. In the past, different evolutionary optimization algorithms were employed for optimizing SVMs;in this paper, we propose a social ski-driver (SSD) optimization algorithm which is inspired from different evolutionary optimization algorithms for optimizing the parameters of SVMs, with the aim of improving the classification performance. To cope with the problem of imbalanced data which is one of the challenging problems for building robust classification models, the proposed algorithm (SSD-SVM) was enhanced to deal with imbalanced data. In this study, eight standard imbalanced datasets were used for testing our proposed algorithm. For verification, the results of the SSD-SVM algorithm are compared with grid search, which is a conventional method of searching parameter values, and particle swarm optimization (PSO). The experimental results show that the SSD-SVM algorithm is capable of finding near-optimal values of SVMs parameters. The results also demonstrated high classification performance compared to the PSO algorithm.

关键词： optimization algorithms Support vector machine (SVM) Parameter optimization Imbalanced data

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Level set based structural topology optimization for minimizing frequency response

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JOURNAL OF SOUND AND VIBRATION 2011年第24期330卷 5820-5834页

作者： Shu, Lei Wang, Michael Yu Fang, Zongde Ma, Zhengdong Wei, Peng Northwestern Polytech Univ Dept Mech & Automat Engn Xian 710072 Shaanxi Peoples R China Chinese Univ Hong Kong Dept Mech & Automat Engn Shatin Hong Kong Peoples R China Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA S China Univ Technol Sch Civil Engn & Transportat Guangzhou Guangdong Peoples R China

For the purpose of structure vibration reduction, a structural topology optimization for minimizing frequency response is proposed based on the level set method. The objective of the present study is to minimize the frequency response at the specified points or surfaces on the structure with an excitation frequency or a frequency range, subject to the given amount of the material over the admissible design domain. The sensitivity analysis with respect to the structural boundaries is carried out, while the Extended finite element method (X-FEM) is employed for solving the state equation and the adjoint equation. The optimal structure with smooth boundaries is obtained by the level set evolution with advection velocity, derived from the sensitivity analysis and the optimization algorithm. A number of numerical examples, in the frameworks of two-dimension (2D) and three-dimension (3D), are presented to demonstrate the feasibility and effectiveness of the proposed approach. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： optimization algorithms equations of state Smooth boundaries Topology optimization excitation frequency Sensitivity analysis Frameworks Level set frequency response

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Balancing Communication and Computation in Distributed optimization

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2019年第8期64卷 3141-3155页

作者： Berahas, Albert S. Bollapragada, Raghu Keskar, Nitish Shirish Wei, Ermin Lehigh Univ Dept Ind & Syst Engn Bethlehem PA 18015 USA Northwestern Univ Dept Ind Engn & Management Sci Evanston IL 60208 USA Northwestern Univ Evanston IL 60208 USA Salesforce Res Palo Alto CA 94301 USA Northwestern Univ Dept Elect Engn & Comp Sci Evanston IL 60208 USA

Methods for distributed optimization have received significant attention in recent years owing to their wide applicability in various domains including machine learning, robotics, and sensor networks. A distributed optimization method typically consists of two key components: communication and computation. More specifically, at every iteration (or every several iterations) of a distributed algorithm, each node in the network requires some form of information exchange with its neighboring nodes (communication) and the computation step related to a (sub)-gradient (computation). The standard way of judging an algorithm via only the number of iterations overlooks the complexity associated with each iteration. Moreover, various applications deploying distributed methods may prefer a different composition of communication and computation. Motivated by this discrepancy, in this paper, we propose an adaptive cost framework that adjusts the cost measure depending on the features of various applications. We present a flexible algorithmic framework, where communication and computation steps are explicitly decomposed to enable algorithm customization for various applications. We apply this framework to the well-known distributed gradient descent (DGD) method, and show that the resulting customized algorithms, which we call DGD(t), NEAR-DGD(t), and NEAR-DGD(+), compare favorably to their base algorithms, both theoretically and empirically. The proposed NEAR-DGD(+) algorithm is an exact first-order method where the communication and computation steps are nested, and when the number of communication steps is adaptively increased, the method converges to the optimal solution. We test the performance and illustrate the flexibility of the methods, as well as practical variants, on quadratic functions and classification problems that arise in machine learning, in terms of iterations, gradient evaluations, communications, and the proposed cost framework.

关键词： Communication distributed optimization network optimization optimization algorithms

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Intelligent OLSR Routing Protocol optimization for VANETs

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2012年第4期61卷 1884-1894页

作者： Toutouh, Jamal Garcia-Nieto, Jose Alba, Enrique Univ Malaga Dept Lenguajes & Ciencias Comp E-29071 Malaga Spain

Recent advances in wireless technologies have given rise to the emergence of vehicular ad hoc networks (VANETs). In such networks, the limited coverage of WiFi and the high mobility of the nodes generate frequent topology changes and network fragmentations. For these reasons, and taking into account that there is no central manager entity, routing packets through the network is a challenging task. Therefore, offering an efficient routing strategy is crucial to the deployment of VANETs. This paper deals with the optimal parameter setting of the optimized link state routing (OLSR), which is a well-known mobile ad hoc network routing protocol, by defining an optimization problem. This way, a series of representative metaheuristic algorithms (particle swarm optimization, differential evolution, genetic algorithm, and simulated annealing) are studied in this paper to find automatically optimal configurations of this routing protocol. In addition, a set of realistic VANET scenarios (based in the city of Malaga) have been defined to accurately evaluate the performance of the network under our automatic OLSR. In the experiments, our tuned OLSR configurations result in better quality of service (QoS) than the standard request for comments (RFC 3626), as well as several human experts, making it amenable for utilization in VANET configurations.

关键词： Metaheuristics optimization algorithms optimized link state routing (OLSR) vehicular ad hoc networks (VANET)

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An Efficient Solution to Structured optimization Problems using Recursive Matrices

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COMPUTER GRAPHICS FORUM 2019年第8期38卷 33-39页

作者： Rueckert, D. Stamminger, M. Univ Erlangen Nurnberg Erlangen Germany

We present a linear algebra framework for structured matrices and general optimization problems. The matrices and matrix operations are defined recursively to efficiently capture complex structures and enable advanced compiler optimization. In addition to common dense and sparse matrix types, we define mixed matrices, which allow every element to be of a different type. Using mixed matrices, the low- and high-level structure of complex optimization problems can be encoded in a single type. This type is then analyzed at compile time by a recursive linear solver that picks the optimal algorithm for the given problem. For common computer vision problems, our system yields a speedup of 3-5 compared to other optimization frameworks. The BLAS performance is benchmarked against the MKL library. We achieve a significant speedup in block-SPMV and block-SPMM. This work is implemented and released open-source as a header-only extension to the C+ + math library Eigen.

关键词： CCS Concepts center dot Computing methodologies -> Symbolic and algebraic algorithms Linear algebra algorithms optimization algorithms

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ON THE CONVERGENCE OF GLOBAL METHODS IN MULTIEXTREMAL optimization

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JOURNAL OF optimization THEORY AND APPLICATIONS 1987年第2期54卷 253-271页

作者： HORST, R TUY, H INST MATH HANOIVIETNAM

A general class of derivative-free optimization procedures is presented including the corresponding convergence theory. This theory turns out to be very constructive, in the sense that the convergence conditions not only can be verified easily for many existing algorithms, but also allow one to construct new procedures. It is shown that popular methods such as branch-and-bound concepts, Pintér's general class of procedures, the algorithms of Pijavskii, Shubert, and Mladineo, and the approach of Zheng and Galperin can not only be subsumed under this class of methods, but also partly be improved by regarding them within the framework presented.

关键词： Global optimization multiextremal optimization optimization algorithms nonlinear programming branch-and-bound methods Lipschitzian optimization

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A Convex optimization Approach for Computing Correlated Choice Probabilities With Many Alternatives

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2019年第1期64卷 190-205页

作者： Ahipasaoglu, Selin Damla Li, Xiaobo Natarajan, Karthik Singapore Univ Technol & Design Engn Syst & Design Singapore 487372 Singapore Natl Univ Singapore Dept Ind Syst Engn & Management Singapore 117576 Singapore

A popular discrete choice model that incorporates correlation information is themultinomial probit (MNP) model where the random utilities of the alternatives are chosen from a multivariate normal distribution. Computing the choice probabilities is challenging in the MNP model when the number of alternatives is large. Mishra et al. (IEEE Transactions on Automatic Control, 2012) have proposed a semidefinite optimization approach to compute choice probabilities for the distribution of the random utilities that maximizes expected agent utility given only the mean, variance, and covariance information. Their model is referred to as the cross moment (CMM) model. Computing the choice probabilities with many alternatives is challenging in the CMM model, since one needs to solve large-scale semidefinite programs. We develop a simpler formulation as a representative agent model by maximizing over the choice probabilities in the unit simplex where the objective function is the sum of the expected utilities and a strongly concave perturbation function. By characterizing the perturbation function for the CMM model and its gradient, we develop a simple first-order gradient method with inexact line search to compute choice probabilities. We establish local linear convergence of this algorithm under mild assumptions on the choice probabilities. An implication of our results is that inverting the choice probabilities to compute the mean utilities is straightforward given any positive-definite covariance matrix. Numerical experiments show that this method can compute choice probabilities for a large number of alternatives within a reasonable amount of time while explicitly capturing the correlation information. Comparisons with simulationmethods for MNP and semidefinite programming methods for CMM indicate the efficacy of the method.

关键词： Choice probability optimization optimization algorithms stochastic systems

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A novel teaching-learning based optimization approach for design of broad-band anti-reflection coatings

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SWARM AND EVOLUTIONARY COMPUTATION 2017年 34卷 68-74页

作者： Patel, Sanjaykumar J. Toshniwal, Aditi Kheraj, Vipul SV Natl Inst Technol Dept Appl Phys Surat 395007 Gujarat India

The design of thin-film multilayered anti-reflection (AR) coating is quite an intricate task due to highly nonlinear and complex dimensional search space, which includes many local minima. In this paper, a novel teaching-learning based optimization (TLBO) approach is employed to design ultra-low reflective coating over a broad wavelength-band using multilayer thin-film structures for optoelectronic devices. The algorithm is implemented using LabVIEW as a programming tool. Various design specific input parameters such as scanning range of wavelengths, step-size, angle of incidence, number of layers, the name and sequence of coating materials etc. are required to be fed by the user on the graphical user interface. The algorithm minimizes the average reflectivity computed over given wavelength range by tuning the thickness of layers in the multilayer stack. The reliability and evolution of design solution with iterations have been systematically investigated for different learner-sizes. Finally, using the optimized learner size and desired number of iterations, the optimum AR design is obtained in terms of the thickness of each layer for the multilayer AR coating. The effectiveness of the TLBO approach has been compared with that of an established algorithm, i.e. genetic algorithm (GA), by means of Wilcoxon singed ranked test. It is concluded that the TLBO can be a very efficient, simpler and relatively faster approach to address complex optimization problems such as broad-band AR coating designs.

关键词： Anti-reflection coating optimization algorithms Teaching-learning based optimization Thin-films and 1abVIEW

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Comparative implementation of the benchmark Dejong 5 function using flower pollination algorithm and the African buffalo optimization

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CURRENT SCIENCE 2019年第5期117卷 871-877页

作者： Odili, Julius Beneoluchi Noraziah, A. Anchor Univ Lagos Dept Math Sci Lagos Nigeria Univ Malaysia Pahang Fac Comp Syst & Software Engn Kuantan 26300 Malaysia Univ Malaysia Pahang IBM Ctr Excellence Kuantan 26300 Malaysia

This communication presents experimental research findings on the application of the flower pollination algorithm (FPA) and the African buffalo optimization (ABO) to implement the complex and fairly popular benchmark Dejong 5 function. The study aims to unravel the untapped potential of FPA and the ABO in providing good solutions to optimization problems. In addition, it explores the Dejong 5 function with the hope of attracting the attention of the research community to evaluate the capacity of the two comparative algorithms as well as the Dejong 5 function. We conclude from this study that in implementing FPA and ABO for solving the benchmark Dejong 5 problem, a population of 10 search agents and using 1000 iterations can produce effective and efficient outcomes.

关键词： Benchmark comparative implementation iteration optimization algorithms search agents test functions

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Hierarchical optimization for the Double-Sided Ring Structure of the Collector System Planning of Large Offshore Wind Farms

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2017年第3期8卷 1029-1039页

作者： Wei, Shurong Zhang, Lu Xu, Yao Fu, Yang Li, Fangxing Shanghai Univ Elect Power Dept Elect Power Engn Shanghai 200090 Peoples R China Purdue Univ Northwest Dept Elect & Comp Engn Westville IN 46391 USA Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA

High reliability double-sided ring collector systems have been applied in practice for long distance and off the coast large-capacity offshore wind farms. To reduce investment and improve reliability, it is important to optimize and evaluate the double-sided ring collector systems. In this paper, a capital cost optimization model is developed to solve the mutual coupling effects among the system structure, power flow, and short circuit current in the collector system constraints. Based on the characteristics of the offshore electrical collector systems, the optimization model is divided into the offshore substation layer, the wind turbine layer, and the submarine cable layer. The fuzzy clustering algorithm, single parent genetic algorithm, and multiple traveling salesman solution technique are integrated and applied to solve the hierarchical optimization model. Finally, the proposed model and the optimization algorithms are tested in a real, large-scale offshore wind farm. The simulation results are compared with the radial collector systems from an economics and reliability point of view. The simulation results show that the double-sided ring design will achieve more profit with higher reliability in the long run. The results can also provide a benchmark for the collector system design of large-scale offshore wind farms.

关键词： Collector system double-sided ring structure hierarchical optimization offshore wind farm optimization algorithms

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