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1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)

作者： Saadat, Fateme Nasri, Mehdi Islamic Azad Univ Dept Elect Engn Sirjan Sci & Res Branch Sirjan Iran Islamic Azad Univ Dept Elect Engn Sirjan Branch Sirjan Iran Islamic Azad Univ Young Researchers & Elite Club Khomeinishahr Branch Khomeinishahr Iran

ISBN: (纸本)9781467387378

Biometric and multibiometric science play an important role in human authentication systems nowadays. Finger vein pattern is one of the most reliable and secure biometrics due to its invariability and safety from stealth. In this paper, a heuristic method is proposed for score level fusion of three different finger vein's patterns. In the proposed multibiometric system, gravitational search algorithm is used to tune the weights of sum fusion strategy. The performance of the method is evaluated using FAR, FRR and EER criteria. Experimental results confirm the superiority of the proposed method over classic fusion strategy in human identification.

关键词： biometric human identification finger vein gravitational search algorithm

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Multi-objective Fitness-proportional Attraction Approach with Weights

Multi-objective Fitness-proportional Attraction Approach wit...

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IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Laack, Patrick Zille, Heiner Mostaghim, Sanaz Otto Von Guericke Univ Inst Intelligent Cooperat Syst Magdeburg Germany

ISBN: (纸本)9781509006229

This paper proposes a new multi-objective optimization algorithm that is called Fitness-Proportional Attraction with Weights (F-PAW). In contrast to many other approaches, this work was inspired by physics rather than biology. It is based on concepts from several methods, including the attraction principle of gravity from the gravitational search algorithm (GSA), the weight sum approach from Multi-Objective Evolutionary algorithm based on Decomposition (MOEA/D) as well as particle swarm optimization methods. These and other algorithms that were providing inspiration are introduced during the text and their techniques are investigated for the use in F-PAW. The performance of F-PAW is compared to three well-known multi-objective algorithms through an experiment on 16 common test problems taken from the WFG and DTLZ benchmarks. The results indicate two conclusions. On the one side, the proposed approach with the weight sum obtains a good diversity. On the other side, the currently implemented local search is lacking reliability and speed.

关键词： Multi-objective optimization gravitational search algorithm Particle Swarm Optimization Evolutionary algorithms

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Automatic Generation Control of Multi-source Interconnected Power System Including DFIG Wind Turbine 1

Automatic Generation Control of Multi-source Interconnected ...

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1st IEEE International Conference on Power Electronics, Intelligent Control, and Energy Systems (ICPEICES)

作者： Preeti Sharma, Veena Naresh, R. Pulluri, Harish Natl Inst Technol Elect Engn Dept Hamirpur Himachal Prades India

ISBN: (纸本)9781467385879

This paper presents the solution of automatic generation control (AGC) problem of four-area interconnected power system including DFIG (doubly fed induction generator) wind turbine using gravitational search algorithm (GSA). The GSA is used to tune the gains of the speed as well as pitch angle controller of the DFIG wind turbine along with the proportional integrated derivative (PID) controlled interconnected system. To accelerate the performance of the basic GSA, the opposition based learning concept is embedded in GSA and is known as opposition learning based GSA (OGSA). The simulation results show the effectiveness of the OGSA optimized controllers in comparison to GSA in terms of faster convergence, settling time, overshoot and undershoot of the deviations in frequency and tie-line power. The variation of the wind penetration in the considered power system from 10% to 40% is used to show the competency of the proposed controller. The performance of the proposed controller is also shown for the large load perturbation in the control areas from 0.1 p.u MW to 0.4 p.u MW.

关键词： Frequency Control Wind Turbine Inertia Control Pitch Control gravitational search algorithm Opposition Based Learning

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Blind CFO Estimation Based on GSA for Interleaved OFDMA Uplink

Blind CFO Estimation Based on GSA for Interleaved OFDMA Upli...

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IEEE International Conference on Advanced Materials for Science and Engineering (IEEE-ICAMSE)

作者： Chang, Ann-Chen Wang, Yi-Ning Ling Tung Univ Dept Informat Technol Taichung 408 Taiwan

ISBN: (纸本)9781509038695

This paper deals with carrier frequency offset (CFO) estimation for interleaved orthogonal frequency division multiple access (OFDMA) uplink systems. Firstly, gravitational search algorithm (GSA) with center-symmetric trimmed correlation matrix and multiple signal classification criterion is presented for the purpose of efficient estimation. It has been shown that the estimate accuracy of the searching-based estimator strictly depends on the number of search grids used during the peaks searching process, which is time consuming and the required number of search grids is not clear to determine. However, the searching grid size is no need to know previously for the proposed GSA-based approach. Meanwhile, the advantage of inherent interleaved OFDMA signal structure also is exploited to conquer the problems of local optimization and the effect of ambiguous peaks for the proposed estimators. Finally, several simulation results are provided for illustration and comparison.

关键词： carrier frequency offset interleaved OFDMA gravitational search algorithm multiple signal classification

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Evaluating center-seeking and initialization bias: The case of particle swarm and gravitational search algorithms

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INFORMATION SCIENCES 2014年 278卷 802-821页

作者： Davarynejad, Mohsen van den Berg, Jan Rezaei, Jafar Delft Univ Technol Fac Technol Policy & Management NL-2600 GA Delft Netherlands Erasmus Med Ctr Daniel Hoed Canc Ctr Dept Radiat Oncol NL-3075 EA Rotterdam Netherlands Delft Univ Technol Fac Elect Engn Math & Comp Sci NL-2600 GA Delft Netherlands

Complex optimization problems that cannot be solved using exhaustive search require efficient search metaheuristics to find optimal solutions. In practice, metaheuristics suffer from various types of search bias, the understanding of which is of crucial importance, as it is directly pertinent to the problem of making the best possible selection of solvers. In this paper, two metrics are introduced: one for measuring center-seeking bias (CSB) and one for initialization region bias (IRB). The former is based on "xi-center offset", an alternative to "center offset", which is a common but inadequate approach to analyzing the center-seeking behavior of algorithms, as will be shown. The latter is proposed on the grounds of "region scaling". The introduced metrics are used to evaluate the bias of three algorithms while running on a test bed of optimization problems having their optimal solution at, or near, the center of the search space. The most prominent finding of this paper is considerable CSB and IRB in the gravitational search algorithm (GSA). In addition, a partial solution to the center-seeking and initialization region bias of GSA is proposed by introducing a "mass-dispersed" version of GSA, mdGSA. mdGSA promotes the global search capability of GSA. Its performance is verified using the same mathematical optimization problem, next to a gene regulatory network parameter identification problem. The results of these experiments demonstrate the capabilities of mdGSA in solving real-world optimization problems. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Center-seeking bias Initialization bias Particle swarm optimization gravitational search algorithm

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Fault diagnosis for missile autopilot based on GSA-SVM

Fault diagnosis for missile autopilot based on GSA-SVM

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2016 IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference(IMCEC 2016)

作者： Ping Song Yuzhu He Qingfeng Ma School of Instrumentation Science and Opto-electronics Engineering Beihang University

ISBN: (纸本)9781509001668

Due to the blind selection for the kernel function parameters and penalty factor parameters by Support Vector Machine(SVM),a method based on gravitational search algorithm(GSA) to optimize the SVM parameters was proposed and was used for the fault diagnosis of analog circuit in a missile *** kind of method can avoid falling into the local optimal problem during the process of optimizing the SVM parameters and then can achieve the global optimal *** experimental results showed that the SVM optimized by this method had a higher diagnosis precision and robustness than that optimized by particle swarm optimization(PAO) and genetic algorithm(GA).Moreover,this method proposed in the paper can complete the fault diagnosis well.

关键词： autopilot analog circuit fault diagnosis support vector machine gravitational search algorithm

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Solving a multi-stage multi-product solid supply chain network design problem by meta-heuristics

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SCIENTIA IRANICA 2016年第3期23卷 1429-1440页

作者： Mahmoodirad, A. Sanei, M. Islamic Azad Univ Cent Tehran Branch Dept Math Tehran Iran

This paper presents an effective optimization method based on meta-heuristics algorithms for the design of a multi-stage, multi-product solid supply chain network design problem. First, a mixed integer linear programming model is proposed. Second, because the problem is an NP-hard, three meta-heuristics algorithms, namely Differential Evolution (DE), Particle Swarm Optimization (PSO), and gravitational search algorithm (GSA), are developed for the first time for this kind of problem. To the best of our knowledge, neither DE, nor PSO, nor GSA have been considered for the multi-stage solid supply chain network design problems. Furthermore, the Taguchi experimental design method is used to adjust the parameters and operators of the proposed algorithms. Finally, to evaluate the impact of increasing the problem size on the performance of our proposed algorithms, different problem sizes are applied and the associated results are compared with each other. (C) 2016 Sharif University of Technology. All rights reserved.

关键词： Supply chain network design Differential evolution Particle swarm optimization algorithm gravitational search algorithm Taguchi experimental Design

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Multi-response Optimization in Drilling of Carbon Fiber Reinforced Polymer Using Artificial Neural Network Correlated to Meta-heuristics algorithm

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Procedia Technology 2016年 25卷 955-962页

作者： K. Shunmugesh K. Panneerselvam Research Scholar Department of Production Engineering NIT Trichy Assistant Professor Department of Production Engineering NIT Trichy

This paper aims to optimize the drilling process parameters using artificial neural network (ANN) linked with the most popular meta-heuristics technique such as Hybrid Particle Swarm Optimization gravitational search algorithm (PSOGSA) and Genetic algorithm (GA). An aerospace grade T300 Carbon Fiber-Epoxy composite laminate of 8 mm thick was made of T300 Polyacrylonitrile (PAN) based Carbon Fiber and two part Epoxy resin was use for this study. The Carbon Fiber used is Bi-directional (BD) with a ply thickness of 0.25 mm and lay-up sequence of [60/90/0/90/90/60/0/60/60/60/60/45/90/90/0/45/60/90/60]. Drilling experiments were conducted on a composite laminate by varying the cutting speed (30, 40 and 50 m/min), feed rate (0.025, 0.05 and 0.1 mm/rev) and drill bit type (HSS, TiAlN and TiN). The experimental results in the form of thrust force, torque and surface roughness obtained are correlated with process parameters through artificial neural network (ANN) and optimized by PSOGSA and GA. The optimization results indicates that the proposed hybrid PSOGSA performances much better than the GA.

关键词： Drilling Carbon Fiber Reinforced Polymer Particle Swarm Optimization gravitational search algorithm Artificial Neural Network

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Solving Unit Commitment Problem Employing Proposed Hybrid BBO-discrete Hopfield Neural Network

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Research Journal of Applied Sciences, Engineering and Technology 2016年第3期12卷 328-338页

作者： J. Chitra S.N. Deepa Department of EEE Anna University Regional Campus Coimbatore Tamil Nadu India

A novel hybrid approach is developed based on the hybridization of Biogeography Based Optimization and Discrete Hopfield Neural Network. BBO algorithm is employed to tune for the optimal weights of discrete Hopfield Neural Network leading to the minimization of energy function. The proposed hybrid BBO-DHNN is implemented for 10, 20, 40 and 60 units power system under consideration. Based on the simulation results presented, it is clearly noted that the proposed HBDHNN approach results in better solutions for the unit commitment problem considered and this in turn reduces the computational burden to a significant extent. The proposed approaches are developed in MATLAB environment version 7.8.0.347 and executed in a PC with Intel core 2 Duo processor with 2.27 GHz speed and 2 GB RAM with 64 bit operating system.

关键词： Artificial neural network biogeography based optimization discrete hopfield neural network gravitational search algorithm unit commitment problem

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Stabilization and trajectory tracking control for underactuated quadrotor helicopter subject to wind-gust disturbance

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2015年第5期40卷 1531-1553页

作者： Basri, Mohd Ariffanan Mohd Husain, Abdul Rashid Danapalasingam, Kumeresan A. Univ Teknol Malaysia Fac Elect Engn Dept Control & Mechatron Engn Skudai 81310 Johor Malaysia

The control of quadrotor helicopter has been a great challenge for control engineers and researchers since quadrotor is an underactuated and a highly unstable nonlinear system. In this paper, the dynamic model of quadrotor has been derived and a so-called robust optimal backstepping control (ROBC) is designed to address its stabilization and trajectory tracking problem in the existence of external disturbances. The robust controller is achieved by incorporating a prior designed optimal backstepping control (OBC) with a switching function. The control law design utilizes the switching function in order to attenuate the effects caused by external disturbances. In order to eliminate the chattering phenomenon, the sign function is replaced by the saturation function. A new heuristic algorithm namely gravitational search algorithm (GSA) has been employed in designing the OBC. The proposed method is evaluated on a quadrotor simulation environment to demonstrate the effectiveness and merits of the theoretical development. Simulation results show that the proposed ROBC scheme can achieve favorable control performances compared to the OBC for autonomous quadrotor helicopter in the presence of external disturbances.

关键词： Quadrotor helicopter robust control backstepping control gravitational search algorithm

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