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Improved immune computation for high-precision face recognition

SOFT COMPUTING 2017年第20期21卷 5989-5999页

作者： Gong, Tao Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Donghua Univ Minist Educ Engn Res Ctr Digitized Text & Fash Technol Shanghai 201620 Peoples R China

Dangerous persons can be monitored accurately and quickly using the high-precision recognition of the relative facial images via the image sensors. To increase the recognition rate of face recognition by improving the immune algorithm, the immune computation was redesigned for better face recognition to decrease the facial disturbances of the pose, illumination and expression (PIE), in this paper. The clonal selection algorithm was improved with the modification of the affinity and the algorithm workflow. The improved clonal selection algorithm searches the most similar antibody sample against the antigen of an unknown facial image, according to the affinity between the antigen and the antibody. The unknown facial images were recognized with this improved affinity and the uncertainty-based reasoning, so the affinity matching of the antibody with the unknown antigen was also improved. Experimental results show that this immune algorithm outperforms some state-of-the-art algorithms in the face recognition accuracy tests with such facial image databases as AR, Yale and CMU-PIE. So the proposed immune algorithm is useful and effective to improve the performance of face recognition in the image sensor network.

关键词： Face recognition PIE High precision clonal selection algorithm Improved affinity Unknown facial image Uncertainty-based reasoning

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Design of DC Motor Control System Based on clonal selection algorithm

Design of DC Motor Control System Based on Clonal Selection ...

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2nd International Conference on Manufacturing Science and Engineering

作者： Zhao, Pengbing Shi, Yaoyao Northwestern Polytech Univ Minist Educ Key Lab Contemporary Design & Integrated Mfg Tech Xian 710072 Shaanxi Peoples R China

ISBN: (纸本)9783037850398

According to the multivariate, strong coupling and nonlinearity of the DC motor speed control system, and the traditional PID control is difficult to achieve the higher speed performance. Combining the clonal selection algorithm (CSA) with the traditional PID control, a kind of intelligent PID control system was designed. The controller realizes the real-time and online adjustment of PID parameters, thus, the dynamic and static performance and robustness of the DC motor control system was improved. The simulation experiment was processed based on MATLAB environment. Simulation results show that this intelligent PID controller achieves better performance than the traditional PID controller.

关键词： clonal selection algorithm PID Control Parameter Adjustment DC Motor

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An Adaptive clonal selection algorithm with Stage Mutation Operation for Unrelated Parallel Machine Scheduling Problem with Sequence-dependent Setup Times

An Adaptive Clonal Selection Algorithm with Stage Mutation O...

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International Conference on Materials, Mechatronics and Automation (ICMMA 2011)

作者： Niu, Qun Zhou, Fang Zhou, Taijin Shanghai Univ Sch Mechatron Engn & Automat Shanghai Key Lab Power Stn Automat Technol Shanghai 200072 Peoples R China

ISBN: (纸本)9783037850176

This paper proposes an adaptive clonal selection algorithm (CSA) to solve the unrelated parallel machine scheduling problem (UPMSP) with sequence-dependent setup time constraints. The objective is to find the sequence which minimizes the makesepan. CSA is a newly discovered population-based evolutionary algorithm based on the clonal selection principle and the immune system. In order to improve the performance of CSA, a local search operation is adopted to strengthen the search ability. In addition, an adaptive clonal factor and a stage mutation operation are introduced to enhance the exploration and exploitation of the algorithm. The performance of the proposed adaptive clonal selection algorithm is compared with genetic algorithm (GA), Simulated Annealing (SA) and basic CSA on 320 randomly generated instances. The results demonstrate the superiority of the proposed method and confirm its potential to solve the UPMSP with sequence-dependent setup time constraints especially when the scale of the instances is very large.

关键词： scheduling unrelated parallel machine clonal selection algorithm

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Research of Hybrid Biogeography Based Optimization and clonal selection algorithm for Numerical Optimization

Research of Hybrid Biogeography Based Optimization and Clona...

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2nd International Conference on Swarm Intelligence (ICSI)

作者： Qu, Zheng Mo, Hongwei Harbin Engn Univ Automat Coll Harbin 150001 Peoples R China

ISBN: (纸本)9783642215155

The interest of hybridizing different nature inspired algorithms has been growing in recent years. As a relatively new algorithm in this field, Biogeography Based Optimization(BBO) shows great potential in solving numerical optimization problems and some practical problems like TSP. In this paper, we proposed an algorithm which combines Biogeography Based Optimization (BBO) and clonal selection algorithm (BBOCSA). Several benchmark functions are used for comparison among the hybrid and other nature inspired algorithms (BBO, CSA, PSO and GA). Simulation results show that clone selection can enhance the ability of exploration of BBO and the proposed hybrid algorithm has better performance than the other algorithms on some benchmarks.

关键词： Biogeography based optimization clonal selection algorithm Optimization

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A clonal selection algorithm TO INVESTIGATE DIVERSE SOLUTIONS FOR THE RELIABLE SERVER ASSIGNMENT PROBLEM

A CLONAL SELECTION ALGORITHM TO INVESTIGATE DIVERSE SOLUTION...

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International Conference on Evolutionary Computation Theory and Applications (ECTA 2011) / International Conference on Fuzzy Computation Theory and Applications (FCTA 2011)

作者： Konak, Abdullah Kulturel-Konak, Sadan Penn State Berks Reading PA 19610 USA

ISBN: (纸本)9789898425836

A reliable server assignment (RSA) problem in networks is defined as determining a deployment of identical servers on a network to maximize a measure of service availability. In this paper, a simulation optimization approach is introduced based on a Monte Carlo simulation and embedded into a clonal selection algorithm (CSA) to find diverse solutions for the RSA problem, which is important in simulation optimization. The experimental results show that the simulation embedded-CSA is an effective heuristic method to discover diverse solutions to the problem.

关键词： Network reliability Reliable server assignment clonal selection algorithm Artificial immune systems

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Prediction of Photovoltaic Panel Power Output using Artificial Neural Networks Learned by Heuristic algorithms: A Comparative Study 2

Prediction of Photovoltaic Panel Power Output using Artifici...

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2017 International Conference on Computer Science and Engineering (UBMK)

作者： Dandil, Emre Gurgen, Erol Bilecik Seyh Edebali Univ Comp Engn Dept Bilecik Turkey Bilecik Seyh Edebali Univ Inst Sci & Technol Bilecik Turkey

ISBN: (纸本)9781538609309

The prediction of power outputs generated from photovoltaic (PV) systems at different times is necessary for reliable and economical use of solar panels. The prediction of the power output is also very important in terms of factors such as installation of solar panels, guidance of electricity companies, energy management and distribution. In this study, we propose an Artificial Neural Network (ANN) model learned by heuristic algorithms to predict the power outputs obtained from PV panels monthly. It has been seen that ANN trained by Particle Swarm Optimization (PSO) are more successful than methods trained by the Back-Propagation(BP) and clonal selection algorithm (CSA) for prediction of the power outputs obtained from PV panels placed at six different tilt angles.

关键词： Photovoltaic panel power prediction ANN back-propagation PSO clonal selection algorithm

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Energy Efficient Task Scheduling in Cloud Environment 4

Energy Efficient Task Scheduling in Cloud Environment

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4th International Conference on Power and Energy Systems Engineering, (CPESE)

作者： Jena, R. K. Inst Management Technol Nagpur Maharashtra India

Cloud computing is a style of computing in which dynamically scalable and other virtualized resources are provided as a service over the Internet. The energy consumption and makespan associated with the resources allocated should be taken into account. This paper focuses on task scheduling using clonal selection algorithm (TSCSA) to optimize energy and processing time. The result obtained by TSCSA was simulated by an open source cloud platform (CloudSim). Finally, the results were compared to existing scheduling algorithms and found that the proposed algorithm (TSCSA) provide an optimal balance results for multiple objectives (C) 2017 The Authors. Published by Elsevier Ltd.

关键词： Task Scheduling Cloud Computing CloudSim clonal selection algorithm

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Unsupervised feature selection based on maximum information and minimum redundancy for hyperspectral images

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PATTERN RECOGNITION 2016年 51卷 295-309页

作者： Feng, Jie Jiao, Licheng Liu, Fang Sun, Tao Zhang, Xiangrong Xidian Univ Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

Unsupervised feature selection plays an important role in hyperspectral image processing. It is a very challenge issue to select an effective feature subset with the unavailability of class labels. To select the features maximally preserving the information of original features, a maximum joint mutual information (MJMI) criterion is defined. Since the high-order distribution involved in MJMI is hard to calculate, a maximum information and minimum redundancy (MIMR) criterion is derived as the low-order approximation of MJMI. From information theory, many classical unsupervised feature selection criteria can also be considered as the low-order approximations of MJMI. Compared with them, MIMR requires more relaxed approximation condition. Moreover, a new clonal selection algorithm (CSA) in artificial immune system is devised to optimize the selected features with the guidance of MIMR. Experimental results on several hyperspectral datasets demonstrate that the proposed method obtains better feature subsets compared with classical unsupervised feature selection methods. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Unsupervised feature selection Hyperspectral images Maximum information and minimum redundancy Information theory clonal selection algorithm

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Extracting Information in Agricultural Data Using Fuzzy-Rough Sets Hybridization and clonal selection Theory Inspired algorithms

Extracting Information in Agricultural Data Using Fuzzy-Roug...

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11th International Conference on Natural Computation (ICNC) / 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Lasisi, Ayodele Ghazali, Rozaida Deris, Mustafa Mat Herawan, Tutut Lasisi, Fola Univ Tun Hussein Onn Malaysia Fac Comp Sci & Informat Technol Batu Pahat 86400 Johor Malaysia Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur 50603 Malaysia Obafemi Awolowo Univ Dept Agr Engn Ife Nigeria

Mining agricultural data with artificial immune system (AIS) algorithms, particularly the clonal selection algorithm (clonalG) and artificial immune recognition system (AIRS), form the bedrock of this paper. The fuzzy-rough feature selection (FRFS) and vaguely quantified rough set (VQRS) feature selection are coupled with clonalG and AIRS for improved detection and computational efficiencies. Comparative simulations with sequential minimal optimization and multi-layer perceptron reveal that the clonalG and AIRS produced significant results. Their respective FRFS and VQRS upgrades namely, FRFS-clonalG, FRFS-AIRS, VQRS-clonalG, and VQRS-AIRS, are able to generate the highest detection rates and lowest false alarm rates. Thus, gathering useful information with the AIS models can help to enhance productivity related to agriculture.

关键词： clonal selection algorithm artificial immune recognition system fuzzy-rough set vaguely quantified rough set

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Energy Efficient Task Scheduling in Cloud Environment

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Energy Procedia 2017年 141卷 222-227页

作者： R.K. Jena Institute of Management Technology Nagpur India

关键词： Task Scheduling Cloud Computing CloudSim clonal selection algorithm

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