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Annual International Conference on Emerging Research Areas (AICERA) / International Conference on Microelectronics, Communication and Renewable Energy (ICMiCR)

作者： Krishnakumar, Neeraja Venugopalan, Rini Rajasekar, N. VIT Univ Sch Elect Engn Vellore Tamil Nadu India

ISBN: (纸本)9781467351492;9781467351508

The abundance and non-polluting nature of solar energy has aroused the interest of many researchers. This worldwide attention of photovoltaic panels has led to the need of generating accurate model of photovoltaic module before proceeding to the installation part. However, the modeling of solar PV characteristics is difficult since the manufacturer's datasheet provides only four values such as V-mp, I-mp, V-oc, and I-sc. For the accurate modeling of photovoltaic panels, the precise estimation of model parameters at different environmental conditions is very essential. Optimization technique is a very powerful tool to obtain the solution of complex non-linear problems. In this paper, the application of the bacterial foraging algorithm (BFA) for the accurate extraction of model parameters has been discussed. A systematic evaluation and performance comparison of BFA with other optimization techniques such as Genetic algorithm, artificial immune System etc. has been done and the best computational technique is determined based on certain criteria such as accuracy, consistency, speed of convergence etc. The computed data is compared with experimental data and the results are validated using two photovoltaic modules of different nature (multicrystalline and thin film).

关键词： Bacterial Foraging algorithm Genetic algorithm artificial immune algorithm Photovoltaic module

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A Hybrid Particle Swarm Optimization algorithm Based on immune Selection for Stochastic Loader Problem

A Hybrid Particle Swarm Optimization Algorithm Based on Immu...

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International Conference on Mechatronics and Industrial Informatics (ICMII 2013)

作者： Wang, Hong Zhao, Peiyi Shandong Inst Light Ind Coll Finance Jinan Shandong Peoples R China Shandong Prov Acad Educ Recruitment & Examinat Jinan Shandong Peoples R China

ISBN: (纸本)9783037856949

The Particle Swarm Optimization algorithm is a new computational method for the combinatorial optimization problem, it is simple and effective, but it does suffer from the premature convergence. For overcome this problem and finding the optimal solution of the Stochastic Loader problem, we presented a new hybrid Particle Swarm Optimization algorithm that combines with artificial immune algorithm, such as immune memory, antibody promotion and suppression, immune selection and so on. Numerical example illustrates the higher efficiency and reliability of the new hybrid PSO compared with the basic Particle Swarm Optimization algorithm.

关键词： stochastic loader problem particle swarm optimization artificial immune algorithm

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An Application of Charging Management for Mobile Robot Transportation in Laboratory Environments

An Application of Charging Management for Mobile Robot Trans...

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IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

作者： Liu, Hui Stoll, Norbert Junginger, Steffen Thurow, Kerstin Univ Rostock Inst Automat D-18055 Rostock Germany

ISBN: (纸本)9781467346214

In life science laboratories, mobile robots are adopted to do transportation tasks among distributed automated islands or rooms. To use those mobile robots effectively, a number of technology issues have to be considered, such as path planning of transportation and charging, localization, communication, etc. In this paper an application of charging management for mobile robot transportation is presented. In this application: (a) to localize the mobile robots accurately and fast in life science environments, a method using ceiling landmarks is adopted;(b) to measure the power status from all running robots, a power module for the server and client sides is designed;(c) to let mobile robots go charging automatically, an automated charging station is utilized;and (d) to select the best installing positions for the charging stations, an intelligent method based on the artificial immune algorithm (AIA) is proposed, which considers both of the distances among the expected working positions and their distribution of transportation tasks. A real case shows that the presented application is effective for mobile robot charging management in life science environments.

关键词： charging station laboratory transportation artificial immune algorithm life science engineering

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Based on Improvement Immunity algorithm Electrical Power System Optimal Power Flow Computation

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International Conference on Mechatronics and Control Engineering (ICMCE 2012)

作者： Li, Xiao Ezhou Univ Dept Comp Sci Ezhou Peoples R China

ISBN: (纸本)9783037855959

Proposed one kind of improvement artificial immunization algorithm calculates the electrical power system most superior tidal current, this algorithm maintained the basic immunity algorithm comprehensive search ability, also the concept which is apart from through the introduction vector causes the immunity algorithm theoretically to guarantee the understanding the multiplicity. Through the IEEE-30pitch point system computed result indicated this algorithm is feasible. And so on compares with the heredity algorithm, this algorithm overall situation search ability strong, the convergence rate is quick.

关键词： optimal power flow artificial immune algorithm evolutionary algorithm

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Application of An Improved immune algorithm in Generation Expansion Planning

Application of An Improved Immune Algorithm in Generation Ex...

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International Conference on Sustainable Energy and Environmental Engineering (ICSEEE 2012)

作者： Li, Xiao Ezhou Univ Dept Comp Sci Ezhou Peoples R China

ISBN: (纸本)9783037856345

Proposed one kind of improvement artificial immunization algorithm calculates the electrical power system generation expansion planning, this algorithm maintained the basic immunity algorithm comprehensive search ability, also the concept which is apart from through the introduction vector causes the immunity algorithm theoretically to guarantee the understanding the multiplicity. Through the simulation experiment computed result indicated this algorithm is feasible. And so on compares with the heredity algorithm, this algorithm overall situation search ability strong, the convergence rate is quick.

关键词： generation expansion planning artificial immune algorithm evolutionary algorithm

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Fast Optimizing Parameters algorithm for Least Squares Support Vector Machine Based on artificial immune algorithm

Fast Optimizing Parameters Algorithm for Least Squares Suppo...

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2009 9th International Conference on Electronic Measurement & Instruments(第九届电子测量与仪器国际会议 ICEMI'2009)

作者： Fugang Yang School of information & electronics engineering Shandong Institute of Business and Technologyi Yan'tai 264005

ISBN: (纸本)9781424438631;9781424438624

When Least Squares Support Vector Machine (LS-SVM) is used to classify on large datasets,training samples to get the optimal model parameters is a timeconsuming and memory consumption process. To reduce training time and computational complexity,we develop a novel algorithm for selecting LS-SVM meta-parameter values based on ideas from principle of artificial immune. By analyzing LS-SVM parameters on the classification accuracy,we find there are many parameters combinations that make the same classification accuracy;What's more,once one of the parameters fixed and the other changes in a certain range,their combinations do not affect the classification accuracy. We regard LS-SVM parameters as antibody genes and design reasonable coding scheme for them. Then we employ artificial immune algorithm to search the optimal model parameters of LS-SVM. We provide experiments to demonstrate the performance of LS-SVM. Results show that the proposed algorithm greatly enhances parameters optimizing efficiency while keeping the approximately same classification accuracy with the some other existent methods such as multi-fold cross-validation and grid-search.

关键词： Least Squares Support Vector Machine artificial immune algorithm Parameters Optimization

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Comments on "Solving a capacitated fixed-charge transportation problem by artificial immune and genetic algorithms with a Prufer number representation" by Molla-Alizadeh-Zavardehi, S. et al. Expert Systems with Applications (2011)

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第12期39卷 11321-11322页

作者： El-Sherbiny, Mahmoud M. Cairo Univ ISSR Dept Operat Res Cairo Egypt

In this work presented are some comments concerning the paper titled Spanning tree-based artificial immune and genetic algorithms with a Prufer number for solving a capacitated fixed charge transportation problem was proposed by Molla-Alizadeh-Zavardehi, S. et al., which was published in Expert Systems with Applications 38 (2011) 10462-10474. The comments are related to the mathematical model of the capacitated fixed charge problem, transportation graph of the example, and the total cost of the example. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Fixed charge transportation artificial immune algorithm Genetic algorithms Spanning tree Prufer number

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Bacterial Foraging algorithm based parameter estimation of solar PV model

Bacterial Foraging Algorithm based parameter estimation of s...

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Annual International Conference on Emerging Research Areas

作者： Neeraja Krishnakumar Rini Venugopalan N. Rajasekar School of Electrical Engineering VIT University

ISBN: (纸本)9781467351508

The abundance and non-polluting nature of solar energy has aroused the interest of many researchers. This worldwide attention of photovoltaic panels has led to the need of generating accurate model of photovoltaic module before proceeding to the installation part. However, the modeling-of solar PV characteristics is difficult since the manufacturer's datasheet provides only four values such as V_(mp), I_(mp), V_(oc), and I_(sc). For the accurate modeling of photovoltaic panels, the precise estimation of model parameters at different environmental conditions is very essential. Optimization technique is a very powerful tool to obtain the solution of complex non-linear problems. In this paper, the application of the bacterial foraging algorithm (BFA) for the accurate extraction of model parameters has been discussed. A systematic evaluation and performance comparison of BFA with other optimization techniques such as Genetic algorithm, artificial immune System etc. has been done and the best computational technique is determined based on certain criteria such as accuracy, consistency, speed of convergence etc. The computed data is compared with experimental data and the results are validated using two photovoltaic modules of different nature (multicrystalline and thin film).

关键词： Bacterial Foraging algorithm Genetic algorithm artificial immune algorithm Photovoltaic module

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Modern development of an Adaptive Non-Intrusive Appliance Load Monitoring system in electricity energy conservation

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APPLIED ENERGY 2012年 96卷 55-73页

作者： Tsai, Men-Shen Lin, Yu-Hsiu Natl Taipei Univ Technol Grad Inst Automat Technol Taipei Taiwan Natl Taipei Univ Technol Grad Inst Mech & Elect Engn Taipei Taiwan

Traditionally, electricity energy consumption of a residence can be monitored through the watt-hour meter installed at the panel. However, the power consumption of individual appliances cannot be identified directly. To track and control the energy consumption of each appliance, many meters are needed. This approach results in high financial costs. To reduce the installation costs, the Non-Intrusive Appliance Load Monitoring (NIALM) approach was proposed recently. In this paper, a novel Adaptive Non-Intrusive Appliance Load Monitoring (ANIALM) system that integrates appliance energizing and de-energizing transient feature extraction methods with soft-computing techniques is developed to keep track of the energy consumption of each appliance. The energizing and de-energizing transient responses of appliances can be captured through the analyses of ANIALM. Two recognizers, k-Nearest Neighbor Rule (k-NNR) and Back-Propagation artificial Neural Network (BP-ANN), are used to identify different types of appliances and their operation status under different single-load and multiple-load operation scenarios. The artificial immune algorithm (AIA) with the Fisher criterion is employed to adaptively adjust the feature parameters in order to improve the identification performance of recognizers when a new type of appliance is added for monitoring. From the experimental results obtained in different actual environments, the proposed ANIALM system is confirmed that it is able to identify the operation status of appliances. Also, although the generalization of both recognizers is similar and excellent, the k-NNR recognizer used by the ANIALM system is preferred from the aspects of recognizers' identification mechanism and training performance due to its simplicity in computation and implementation. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： artificial immune algorithm Feature extraction k-Nearest Neighbor Rule Non-Intrusive Appliance Load Monitoring Power signatures Smart grid

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Detection and power quality disturbance classification based on wavelet packet decomposition and modified immune optimization algorithm

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INTERNATIONAL JOURNAL OF KNOWLEDGE-BASED AND INTELLIGENT ENGINEERING SYSTEMS 2012年第4期16卷 261-277页

作者： Biswal, B. Mishra, S. Biswal, P. K. Prabhakar, Telagarapu GMR Inst Technol Rajam 532127 Srikakulam India Indian Inst Technol New Delhi India Internat Inst Informat Technol Bhubaneswar Orissa India

In this paper, a new approach to time-frequency analysis and pattern recognition of non-stationary power signals is proposed. In this paper, visual localization, detection and classification of non-stationary power signals are achieved using wavelet packet decomposition. Automatic pattern recognition is carried out through Modified immune Optimization algorithm (MIOA) based Reformulated Fuzzy C-means algorithm. Time-frequency analysis and feature extraction from the non-stationary power signals are done by wavelet packet decomposition (WPD). Various non-stationary power signal waveforms are processed through wavelet packet decomposition to generate time-frequency contours for extracting relevant features for pattern classification. The extracted features are clustered using Reformulated Fuzzy C-means algorithm and finally the algorithm is extended using artificial immune and Modified immune Optimization algorithm (MIOA) respectively to refine the cluster centers. Results of simulation and analysis demonstrate that the proposed MIOA method achieves higher classification rate, better convergence property.

关键词： Non-stationary power signals Power Quality (PQ) Wavelet Packet Decomposition (WPD) artificial immune algorithm Reformulated Fuzzy C-means algorithm

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