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6th IEEE International Conference on Smart Grid (icSmartGrid)

作者： Costa, Tatiana A. Zvietcovich, Wilingthon G. Baracho, Francisco R. A. C. Damiao, Laerty J. S. Univ Fed Ouro Preto Dept Comp & Syst Joao Monlevade Brazil Univ Fed Ouro Preto Dept Elect Engn Joao Monlevade Brazil

ISBN: (纸本)9781538684849

This paper illustrates a method for solving the capacitor allocation problem with the purpose of reducing the active losses in an electricity network, which should operate with a voltage profile complying with the corresponding regulation. Allocation means determining the types, sizes, locations and control type of the capacitors to be installed in the power network. In this work, the optimization technique used to solve the capacitor allocation problem is the clonal algorithm, inspired by the behavior of the mammalian immune system. The algorithm starts with a high number of capacitors. Subsequently, such a quantity gradually decreases until a configuration with a minimum number of capacitors is found, as well as the indication of where each capacitor should be installed is determined. The objective is to ensure that the network operates with minimal losses and voltages within a pre-defined range. In order to validate the proposed methodology, the algorithm is applied to the IEEE 33-bus system. The results demonstrate the feasibility of the application of the technique.

关键词： clonal algorithm distribution systems electrical network optimization capacitor allocation

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Monocular 3D Human Motion Analysis Using Quantum-Behaved clonal algorithm 9

Monocular 3D Human Motion Analysis Using Quantum-Behaved Clo...

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2017 9th IEEE International Conference on Communication Software and Networks (ICCSN 2017)

作者： Jialin Yu Jifeng Sun Shengqing Liu Shasha Luo Hongshi Ou School of Electronic and Information Engineering South China University of Technology

ISBN: (纸本)9781509038237;9781509038220

3 D human motion analysis from a single viewpoint is an extremely challenge computer vision task due to the lack of depth information and complex human movements. To resolve these problems, based on the quantum computing and immune clonal operator, a novel evolution algorithm, called a quantumbehaved clonal algorithm(QBCA), is proposed for 3 D human motion analysis. Firstly, a 2 D part-based human detector(PBD) is used to compute the 2 D landmarks of key body joints. Then, human motion analysis is performed by optimizing a distance similarity function between the detected 2 D landmarks and 2 D projection of predicted 3 D joint points using QBCA. Moreover, our method not only has a good balance between exploitation and exploration, but also searches both local optimum solution and global optimum solution, simultaneously. Extensive results on PARSE and Human Eva dataset demonstrate the robustness and effectiveness of our proposed method.

关键词： 3D human motion analysis quantum computing clonal algorithm optimization human detection

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A Feature Selection Method for Improved clonal algorithm Towards Intrusion Detection

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2016年第5期30卷 1659013-1659013页

作者： Yin, Chunyong Ma, Luyu Feng, Lu Nanjing Univ Informat Sci & Technol Jiangsu Engn Ctr Network Monitoring Sch Comp & Software Nanjing 210044 Jiangsu Peoples R China

Intrusion detection is a kind of security mechanism which is used to detect attacks and intrusion behaviors. Due to the low accuracy and the high false positive rate of the existing clonal selection algorithms applied to intrusion detection, in this paper, we proposed a feature selection method for improved clonal algorithm. The improved method detects the intrusion behavior by selecting the best individual overall and clones them. Experimental results show that the feature selection algorithm is better than the traditional feature selection algorithm on the different classifiers, and it is shown that the final detection results are better than traditional clonal algorithm with 99.6% accuracy and 0.1% false positive rate.

关键词： Artificial immune clonal algorithm intrusion detection feature selection

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Band Selection in Hyperspectral Image with Chaotic Binary MOclonal algorithm

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SN Computer Science 2022年第5期3卷 410页

作者： Nanda, Satyasai Jagannath Department of Electronics and Communication Engineering Malaviya National Institute of Technology Jaipur JLN Marg Rajasthan Jaipur 302017 India

Hyperspectral image comprise of a three dimensional image cube. Two dimension represents the regular image collected on a specific band of frequency. There are hundreds of such correlated bands present in this three dimensional cube. To effectively process and extract significant information from the hyper-spectral image there is a need to eliminate some of the bands which carry repetitive information, sometimes noisy in nature. In IEEE iSES 2021 symposium, a binary multi-objective clonal algorithm is reported by the same author to address the band selection problem. In this paper, a modified version of the same algorithm is introduced by considering chaotic sequence based initialization instead of the random number based initialization. Entropy and Pearson correlation are used as the fitness function for multi-objective optimization. Simulation have been performed on hyperspectral images of Pavia University, Kennedy Space Centre. On the obtained band reduced images image segmentation is carried out using K-modes clustering. Comparison is carried out with the results obtained with random number based same algorithm, binary social spider algorithm, NSGA-II algorithm and principal component algorithm. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Band selection Chaotic sequence clonal algorithm Hyperspectral image Multi-objective optimization

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Fast clonal algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2008年第1期21卷 106-128页

作者： Khilwani, Nitesh Prakash, Anoop Shankar, Ravi Tiwari, M. K. Natl Inst Foundry & Forge Technol Dept Forge Technol Ranchi 834003 Jharkhand India Natl Inst Foundry & Forge Technol Dept Met & Mat Engn Ranchi 834003 Bihar India Natl Inst Foundry & Forge Technol Dept Mfg Engn Ranchi 834003 Bihar India Indian Inst Technol Dept Management Studies Delhi India

The aim of this paper is to design an efficient and fast clonal algorithm for solving various numerical and combinatorial real-world optimization problems effectively and speedily, irrespective of its complexity. The idea is to accurately read the inherent drawbacks of existing immune algorithms (IAs) and propose new techniques to resolve them. The basic features of IAs dealt in this paper arc: hypermutation mechanism, clonal expansion, immune memory and several other features related to initialization and selection of candidate solution present in a population set. Dealing with the above-mentioned features we have proposed a fast clonal algorithm (FCA) incorporating a parallel mutation operator comprising of Gaussian and Cauchy mutation strategy. In addition, a new concept has been proposed for initialization. selection and clonal expansion process. The concept of existing immune memory has also been modified by using the elitist mechanism. Finally, to test the efficacy of proposed algorithm in terms of search quality, computational cost, robustness and efficiency, quantitative analyses have been performed in this paper. In addition, empirical analyses have been executed to prove the superiority of proposed strategies. To demonstrate the applicability of proposed algorithm over real-world problems, Machine-loading problem of flexible manufacturing system (FMS) is worked out and matched with the results present in literature. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： clonal algorithm Gaussian mutation Cauchy mutation Chaotic generator Machine-loading problem

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The Application of Change Structure Fuzzy Neural Network Based on clonal algorithm at AGC System

The Application of Change Structure Fuzzy Neural Network Bas...

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7th World Congress on Intelligent Control and Automation

作者： Wang, Yan Sun, Lei Univ Jinan Sch Control Sci & Engn Jinan 250022 Peoples R China

ISBN: (纸本)9781424421138

Aiming at the insufficiency of genetic algorithm optimize function parameter, the paper designs the controller based on the clonal algorithm to control the AGC system, the clonal algorithm combines the gauss variation and the Cauchy variation. The emulation result displays that the means be true of the AGC system and be able to procure better controllability.

关键词： genetic algorithm clonal algorithm gauss variation Cauchy variation AGC system

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The Application of Change Structure Fuzzy Neural Network Based on clonal algorithm at AGC System

The Application of Change Structure Fuzzy Neural Network Bas...

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7th World Congress on Intelligent Control and Automation (WCICA 2008), vol.22

作者： Yan Wang Lei Sun School of Control Science and Engineering University of Ji''nan Jinan Shandong China

关键词： Genetic algorithm clonal algorithm Gauss variation Cauchy variation AGC system

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Artificial Immune System Based Combined Economic and Emission Dispatch

Artificial Immune System Based Combined Economic and Emissio...

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IEEE Region 10 Conference (TENCON 2008)

作者： Geetha, R. Bhuvaneswari, R. Subramanian, S. Annamalai Univ Annamalainagar 608002 Tamil Nadu India

ISBN: (纸本)9781424424085

This paper presents a novel optimization approach to Combined Economic and Emission Dispatch (CEED) problem using Artificial Immune System (AIS). The approach utilizes the clonal selection principle and evolutionary approach wherein cloning of antibodies is performed followed by hyper mutation. The developed AIS optimization technique uses the total operating cost as the objective function and is represented as the affinity measure. Through genetic evolution, the antibodies with high affinity measure are produced and the best individuals become the solution. The proposed algorithm has been tested with three and six-unit systems and the results are compared with other prevalent approaches. The simulation results reveal that the developed technique is easy to implement, has converged within an acceptable execution time and yields highly optimal solution for CEED problem with minimum total operating cost and minimum emission.

关键词： Artificial Immune System clonal algorithm Combined Economic and Emission Dispatch

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