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Energy Efficient Task Scheduling in Cloud Environment

Energy Procedia 2017年 141卷 222-227页

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

关键词： Task Scheduling Cloud Computing CloudSim clonal selection algorithm

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The parallel resonance impedance detection method for parameter estimation of power line and transformer by using CSA, GA, and PSO

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2016年第1期24卷 12-23页

作者： Akbal, Bahadir Urkmez, Abdullah Selcuk Univ Fac Engn Dept Elect & Elect Engn Konya Turkey

Power line parameters are an important factor in relay applications and power quality studies. In the literature, the phasor measurement unit method and measuring of current and voltage at two ends of the power line were usually used to estimate the power line parameters. In this study, the parallel resonance impedance detection method was used to estimate the power line parameter to obtain input data. The real measurement values are used to obtain parallel resonance impedance in this method. The real measurement values include the measurement errors of the current and voltage transformer. Thus, the estimated parameter values are realistic. The electrical network with has 27 busbars that belongs to an organized industrial zone in Turkey was used for the application. Harmonic measurement of the power line of the electrical network was made to obtain parallel resonance impedance. The obtained parallel resonance impedance was used in the maximization problem. The maximization problem was defined as the estimated accuracy rate and was solved by using the clonal selection algorithm, genetic algorithm, and particle swarm optimization to make the most realistic parameter estimation. These methods can be defined as parameter estimators and are selected because they are all used widely in electrical engineering problem solutions. The results of these methods were compared with real parameter values, and accuracy of the estimate was determined for each method.

关键词： clonal selection algorithm electric network genetic algorithm particle swarm optimization parallel resonance impedance parameter estimation power line

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Co-operative control of a team of autonomous underwater vehicles in an obstacle-rich environment

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JOURNAL OF MARINE ENGINEERING AND TECHNOLOGY 2016年第3期15卷 135-151页

作者： Das, Bikramaditya Subudhi, Bidyadhar Pati, Bibhuti Bhusan VSS Univ Technol Dept Elect & Telecommun Engn Burla Odisha India Natl Inst Technol Dept Elect Engn Rourkela 769008 India VSS Univ Technol Dept Elect Engn Burla Odisha India

This paper presents the cooperative control of a team of autonomous underwater vehicles (AUVs) in the presence of obstacles under environmental disturbance. A leader-follower formation control based on optimisation algorithms using communication topology is designed to navigate towards the target in the presence of obstacles. clonal selection optimisation algorithm may be employed as team controller by providing the optimal position to hierarchical control strategy for each AUV incorporating learning, memory and affinity. But unfortunately clonal selection optimisation algorithm fails to avoid obstacles because some of the lymphocytes become memory cells of the system. The above prematurity may be overcome by using artificial potential fields and ant colony optimisation technique combined with clonal selection optimisation algorithm. The efficacy of the proposed optimisation is verified through MATLAB simulation and the result confirmed the robustness and proficiency of proposed technique over clonal selection optimisation algorithm.

关键词： Autonomous underwater vehicle clonal selection algorithm ant colony optimisation artificial potential fields

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Co-operative control coordination of a team of underwater vehicles with communication constraints

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2016年第4期38卷 463-481页

作者： Das, Bikramaditya Subudhi, Bidyadhar Pati, Bibhuti Bhusan Veer Surendra Sai Univ Technol Dept Elect Engn Burla 768018 Odisha India Natl Inst Technol Dept Elect Engn Rourkela India

Co-operative control architectures for implementation of motion plan and coordination strategies for a team of Autonomous Underwater Vehicles (AUVs) using different optimization techniques are presented in this paper. The paper addresses the leader-follower formation control of multiple non-holonomic AUVs where one AUV is designated as Leader and the other participating AUVs are treated as the followers. In this scenario, the leader operates according to its optimized path, while each follower is required to follow the leader accurately maintaining desired separations and orientation with respect to the leader. A communication strategy is also designed so that the AUVs can exchange information to obtain the designated waypoints that are sent from the leader following the area coverage problem. Optimization is necessary to determine the optimal path as functions of sensor nodes for control coordination of a team of AUVs satisfying the time minimization. Various optimization algorithms have been employed for control coordination and search strategies of co-operative motion control of a group of AUVs without incorporating learning and memory to evaluate the objective function. The clonal selection algorithm is found to be one of the best approaches to plan the formation leader motion utilizing the triangular sensor-based grid coverage technique. The efficacy of the proposed optimization approach to solve the co-operation motion of a group of AUVs is verified through MATLAB simulation studies and the results confirmed the robustness and efficiency of the proposed technique over existing search methodologies, such as Coordinate, Multi-directional, Simplex and Simultaneous Perturbation Stochastic Approximation.

关键词： Autonomous Underwater Vehicle Optimization Simultaneous Perturbation Stochastic Approximation clonal selection algorithm triangular sensor-based grid

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A Modified Flower Pollination algorithm for Global Optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 57卷 192-203页

作者： Nabil, Emad Cairo Univ Fac Comp & Informat 5 Dr Ahmed Zewail St Giza 12673 Egypt

Expert and intelligent systems try to simulate intelligent human experts in solving complex real-world problems. The domain of problems varies from engineering and industry to medicine and education. In most situations, the system is required to take decisions based on multiple inputs, but the search space is usually very huge so that it will be very hard to use the traditional algorithms to take a decision;at this point, the metaheuristic algorithms can be used as an alternative tool to find near-optimal solutions. Thus, inventing new metaheuristic techniques and enhancing the current algorithms is necessary. In this paper, we introduced an enhanced variant of the Flower Pollination algorithm (FPA). We hybridized the standard FPA with the clonal selection algorithm (CSA) and tested the new algorithm by applying it to 23 optimization benchmark problems. The proposed algorithm is compared with five famous optimization algorithms, namely, Simulated Annealing, Genetic algorithm, Flower Pollination algorithm, Bat algorithm, and Firefly algorithm. The results show that the proposed algorithm is able to find more accurate solutions than the standard FPA and the other four techniques. The superiority of the proposed algorithm nominates it for being a part of intelligent and expert systems. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Nature-inspired algorithms clonal selection algorithm Flower Pollination algorithm Global Optimization

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An artificial immune system with continuous-learning for voltage disturbance diagnosis in electrical distribution systems

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 56卷 131-142页

作者： Lima, Fernando P. A. Lopes, Mara L. M. Lotufo, Anna Diva P. Minussi, Carlos R. Univ Estadual Paulista UNESP FEIS Dept Elect Engn Ave Brasil 56POB 31 BR-15385000 Ilha Solteira SP Brazil

This paper presents a new artificial immune algorithm with continuous-learning, which is inspired by the biological immune system, to realize the voltage diagnosis in electrical distribution systems. This conception allows one to compose a diagnosis system that can continuously learn without reinitialization when new disturbances occur due to the evolution of the electrical system. Two artificial immune algorithms, which are the negative selection algorithm and the clonal selection algorithm, are used for the pattern recognition process and the learning process, respectively. The principal application of this new method aids the operation during failures, supervises the protection system, and can evolve with the power systems to continuously acquire new knowledge. This new methodology has a direct impact in the area of diagnosis in electrical systems, as well as, in the pattern recognition problem, because the main contribution and novelty of this method is the continuous learning capability, which enables the system to learn unknown patterns without having to restart the knowledge. This is the major advantage of this methodology. To evaluate the efficiency and performance of this new method, failure simulations were performed in a real distribution system with 134 buses using the EMTP software. The results show robustness and efficiency. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Diagnosis Voltage disturbances Artificial immune systems Negative selection algorithm clonal selection algorithm Continuous-learning Electrical distribution systems

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Towards Improved clonal selection algorithm by Increasing the Diversity Degree of Antibody

Towards Improved Clonal Selection Algorithm by Increasing th...

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2011 6th IEEE Joint International Information Technology and Artificial Intelligence Conference(ITAIC 2011)

作者： Xiaowen Xu,Weibin Chen College of Computer Science and Technology HUAQIAO UNIVERSITY Xiamen,361000,China

Premature convergence is known as the most important problem in clonal selection algorithm(CSA).In this article,we study this problem and propose a novel algorithm to increase the diversity degree of antibody,which is known as an effective way to improve the performance of *** it is quite simple,our simulation with Traveling Salesman Problem(TSP) shows that our work is effective to prevent CSA from stopping search too early as in its original *** compared with three existing works,our work demonstrates obvious gain within the same number of generations,and the actual time cost is ignorable.

关键词： clonal selection algorithm diversity of antibody convergence mutation

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A new methodology to cluster derivative product modules: an application

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2016年第23期54卷 7091-7099页

作者： Aydin, Merve Ulutas, Berna Haktanirlar Eskisehir Osmangazi Univ Dept Ind Engn Eskisehir Turkey Eskisehir Osmangazi Univ Eskisehir Turkey

Companies are trying several ways to offer competitive and highly differentiated products. The goal for the product platform is to share elements for common functions and to differentiate each product in the family by satisfying different requirements as much as possible. This study focuses on the product variety and short product life cycles that result from the increase and diversification in consumer needs and expectations. Proposed methodology aims to maximise the use of common product modules by considering platform-based derivative products and modular product design approaches to minimise the planning complexity in supply chain, manufacturing and service for derivative products. Functional and technical features of the products are determined in the first step. Then, design structure matrix is formed. After defining product components, similarity matrix for derivative products is formed. A clustering algorithm based on clonal selection is used to generate critical product modules. Data from a home appliance manufacturer are used to assess three versions of a product by also considering the production process. The grouping enabled to shorten the release time of a new derivative product to the market.

关键词： clonal selection algorithm clustering algorithm commonality index design structure matrix modular design

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Immuno-hybrid algorithm: a novel hybrid approach for GRN reconstruction

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3 BIOTECH 2016年第2期6卷 1-10页

作者： Jereesh, A. S. Govindan, V. K. Cochin Univ Sci & Technol Dept Comp Sci Cochin Kerala India Indian Inst Informat Technol Pala Dept Comp Sci & Engn Kottayam Kerala India

Bio-inspired algorithms are widely used to optimize the model parameters of GRN. In this paper, focus is given to develop improvised versions of bio-inspired algorithm for the specific problem of reconstruction of gene regulatory network. The approach is applied to the data set that was developed by the DNA microarray technology through biological experiments in the lab. This paper introduced a novel hybrid method, which combines the clonal selection algorithm and BFGS Quasi-Newton algorithm. The proposed approach implemented for real world E. coli data set and identified most of the relations. The results are also compared with the existing methods and proven to be efficient.

关键词： clonal selection algorithm Immuno-hybrid algorithm Optimization algorithm Gene regulatory network DNA microarray BFGS Quasi-Newton

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The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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