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Algorithm</title> <meta name="description" content="Search results for: Distributed Algorithm"> <meta name="keywords" content="Distributed Algorithm"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Distributed Algorithm"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 5432</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Distributed Algorithm</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5432</span> Optimal Sizing and Placement of Distributed Generators for Profit Maximization Using Firefly Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Engy%20Adel%20Mohamed">Engy Adel Mohamed</a>, <a href="https://publications.waset.org/abstracts/search?q=Yasser%20Gamal-Eldin%20Hegazy"> Yasser Gamal-Eldin Hegazy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a firefly based algorithm for optimal sizing and allocation of distributed generators for profit maximization. Distributed generators in the proposed algorithm are of photovoltaic and combined heat and power technologies. Combined heat and power distributed generators are modeled as voltage controlled nodes while photovoltaic distributed generators are modeled as constant power nodes. The proposed algorithm is implemented in MATLAB environment and tested the unbalanced IEEE 37-node feeder. The results show the effectiveness of the proposed algorithm in optimal selection of distributed generators size and site in order to maximize the total system profit. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20generators" title="distributed generators">distributed generators</a>, <a href="https://publications.waset.org/abstracts/search?q=firefly%20algorithm" title=" firefly algorithm"> firefly algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=IEEE%2037-node%20feeder" title=" IEEE 37-node feeder"> IEEE 37-node feeder</a>, <a href="https://publications.waset.org/abstracts/search?q=profit%20maximization" title=" profit maximization"> profit maximization</a> </p> <a href="https://publications.waset.org/abstracts/6198/optimal-sizing-and-placement-of-distributed-generators-for-profit-maximization-using-firefly-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6198.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">442</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5431</span> A Novel Probablistic Strategy for Modeling Photovoltaic Based Distributed Generators </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Engy%20A.%20Mohamed">Engy A. Mohamed</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20G.%20Hegazy"> Y. G. Hegazy </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a novel algorithm for modeling photovoltaic based distributed generators for the purpose of optimal planning of distribution networks. The proposed algorithm utilizes sequential Monte Carlo method in order to accurately consider the stochastic nature of photovoltaic based distributed generators. The proposed algorithm is implemented in MATLAB environment and the results obtained are presented and discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=comulative%20distribution%20function" title="comulative distribution function">comulative distribution function</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed%20generation" title=" distributed generation"> distributed generation</a>, <a href="https://publications.waset.org/abstracts/search?q=Monte%20Carlo" title=" Monte Carlo"> Monte Carlo</a> </p> <a href="https://publications.waset.org/abstracts/28459/a-novel-probablistic-strategy-for-modeling-photovoltaic-based-distributed-generators" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28459.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">584</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5430</span> Optimal Planning of Dispatchable Distributed Generators for Power Loss Reduction in Unbalanced Distribution Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahmoud%20M.%20Othman">Mahmoud M. Othman</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20G.%20Hegazy"> Y. G. Hegazy</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Y.%20Abdelaziz"> A. Y. Abdelaziz </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a novel heuristic algorithm that aims to determine the best size and location of distributed generators in unbalanced distribution networks. The proposed heuristic algorithm can deal with the planning cases where power loss is to be optimized without violating the system practical constraints. The distributed generation units in the proposed algorithm is modeled as voltage controlled node with the flexibility to be converted to constant power factor node in case of reactive power limit violation. The proposed algorithm is implemented in MATLAB and tested on the IEEE 37 -node feeder. The results obtained show the effectiveness of the proposed algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20generation" title="distributed generation">distributed generation</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic%20approach" title=" heuristic approach"> heuristic approach</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=planning" title=" planning"> planning</a> </p> <a href="https://publications.waset.org/abstracts/31208/optimal-planning-of-dispatchable-distributed-generators-for-power-loss-reduction-in-unbalanced-distribution-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31208.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">524</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5429</span> A High-Level Co-Evolutionary Hybrid Algorithm for the Multi-Objective Job Shop Scheduling Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aydin%20Teymourifar">Aydin Teymourifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Gurkan%20Ozturk"> Gurkan Ozturk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a hybrid distributed algorithm has been suggested for the multi-objective job shop scheduling problem. Many new approaches are used at design steps of the distributed algorithm. Co-evolutionary structure of the algorithm and competition between different communicated hybrid algorithms, which are executed simultaneously, causes to efficient search. Using several machines for distributing the algorithms, at the iteration and solution levels, increases computational speed. The proposed algorithm is able to find the Pareto solutions of the big problems in shorter time than other algorithm in the literature. Apache Spark and Hadoop platforms have been used for the distribution of the algorithm. The suggested algorithm and implementations have been compared with results of the successful algorithms in the literature. Results prove the efficiency and high speed of the algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20algorithms" title="distributed algorithms">distributed algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=Apache%20Spark" title=" Apache Spark"> Apache Spark</a>, <a href="https://publications.waset.org/abstracts/search?q=Hadoop" title=" Hadoop"> Hadoop</a>, <a href="https://publications.waset.org/abstracts/search?q=job%20shop%20scheduling" title=" job shop scheduling"> job shop scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a> </p> <a href="https://publications.waset.org/abstracts/72317/a-high-level-co-evolutionary-hybrid-algorithm-for-the-multi-objective-job-shop-scheduling-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72317.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">363</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5428</span> Handshake Algorithm for Minimum Spanning Tree Construction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nassiri%20Khalid">Nassiri Khalid</a>, <a href="https://publications.waset.org/abstracts/search?q=El%20Hibaoui%20Abdelaaziz%20et%20Hajar%20Moha"> El Hibaoui Abdelaaziz et Hajar Moha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we introduce and analyse a probabilistic distributed algorithm for a construction of a minimum spanning tree on network. This algorithm is based on the handshake concept. Firstly, each network node is considered as a sub-spanning tree. And at each round of the execution of our algorithm, a sub-spanning trees are merged. The execution continues until all sub-spanning trees are merged into one. We analyze this algorithm by a stochastic process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Spanning%20tree" title="Spanning tree">Spanning tree</a>, <a href="https://publications.waset.org/abstracts/search?q=Distributed%20Algorithm" title=" Distributed Algorithm"> Distributed Algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Handshake%20Algorithm" title=" Handshake Algorithm"> Handshake Algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Matching" title=" Matching"> Matching</a>, <a href="https://publications.waset.org/abstracts/search?q=Probabilistic%20Analysis" title=" Probabilistic Analysis"> Probabilistic Analysis</a> </p> <a href="https://publications.waset.org/abstracts/17743/handshake-algorithm-for-minimum-spanning-tree-construction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17743.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">658</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5427</span> A Hybrid Distributed Algorithm for Multi-Objective Dynamic Flexible Job Shop Scheduling Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aydin%20Teymourifar">Aydin Teymourifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Gurkan%20Ozturk"> Gurkan Ozturk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a hybrid distributed algorithm has been suggested for multi-objective dynamic flexible job shop scheduling problem. The proposed algorithm is high level, in which several algorithms search the space on different machines simultaneously also it is a hybrid algorithm that takes advantages of the artificial intelligence, evolutionary and optimization methods. Distribution is done at different levels and new approaches are used for design of the algorithm. Apache spark and Hadoop frameworks have been used for the distribution of the algorithm. The Pareto optimality approach is used for solving the multi-objective benchmarks. The suggested algorithm that is able to solve large-size problems in short times has been compared with the successful algorithms of the literature. The results prove high speed and efficiency of the algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20algorithms" title="distributed algorithms">distributed algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=apache-spark" title=" apache-spark"> apache-spark</a>, <a href="https://publications.waset.org/abstracts/search?q=Hadoop" title=" Hadoop"> Hadoop</a>, <a href="https://publications.waset.org/abstracts/search?q=flexible%20dynamic%20job%20shop%20scheduling" title=" flexible dynamic job shop scheduling"> flexible dynamic job shop scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a> </p> <a href="https://publications.waset.org/abstracts/72319/a-hybrid-distributed-algorithm-for-multi-objective-dynamic-flexible-job-shop-scheduling-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72319.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">354</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5426</span> Considering the Reliability of Measurements Issue in Distributed Adaptive Estimation Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wael%20M.%20Bazzi">Wael M. Bazzi</a>, <a href="https://publications.waset.org/abstracts/search?q=Amir%20Rastegarnia"> Amir Rastegarnia</a>, <a href="https://publications.waset.org/abstracts/search?q=Azam%20Khalili"> Azam Khalili</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we consider the issue of reliability of measurements in distributed adaptive estimation problem. To this aim, we assume a sensor network with different observation noise variance among the sensors and propose new estimation method based on incremental distributed least mean-square (IDLMS) algorithm. The proposed method contains two phases: I) Estimation of each sensors observation noise variance, and II) Estimation of the desired parameter using the estimated observation variances. To deal with the reliability of measurements, in the second phase of the proposed algorithm, the step-size parameter is adjusted for each sensor according to its observation noise variance. As our simulation results show, the proposed algorithm considerably improves the performance of the IDLMS algorithm in the same condition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20filter" title="adaptive filter">adaptive filter</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed%20estimation" title=" distributed estimation"> distributed estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor%0D%0Anetwork" title=" sensor network"> sensor network</a>, <a href="https://publications.waset.org/abstracts/search?q=IDLMS%20algorithm" title=" IDLMS algorithm"> IDLMS algorithm</a> </p> <a href="https://publications.waset.org/abstracts/27648/considering-the-reliability-of-measurements-issue-in-distributed-adaptive-estimation-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27648.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">634</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5425</span> A Hybrid Distributed Algorithm for Solving Job Shop Scheduling Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aydin%20Teymourifar">Aydin Teymourifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Gurkan%20Ozturk"> Gurkan Ozturk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a distributed hybrid algorithm is proposed for solving the job shop scheduling problem. The suggested method executes different artificial neural networks, heuristics and meta-heuristics simultaneously on more than one machine. The neural networks are used to control the constraints of the problem while the meta-heuristics search the global space and the heuristics are used to prevent the premature convergence. To attain an efficient distributed intelligent method for solving big and distributed job shop scheduling problems, Apache Spark and Hadoop frameworks are used. In the algorithm implementation and design steps, new approaches are applied. Comparison between the proposed algorithm and other efficient algorithms from the literature shows its efficiency, which is able to solve large size problems in short time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20algorithms" title="distributed algorithms">distributed algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=Apache%20Spark" title=" Apache Spark"> Apache Spark</a>, <a href="https://publications.waset.org/abstracts/search?q=Hadoop" title=" Hadoop"> Hadoop</a>, <a href="https://publications.waset.org/abstracts/search?q=job%20shop%20scheduling" title=" job shop scheduling"> job shop scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a> </p> <a href="https://publications.waset.org/abstracts/72320/a-hybrid-distributed-algorithm-for-solving-job-shop-scheduling-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72320.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">387</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5424</span> Sync Consensus Algorithm: Trying to Reach an Agreement at Full Speed</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuri%20Zinchenko">Yuri Zinchenko</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, distributed storage systems have been used more and more in various aspects of everyday life. They provide such necessary properties as Scalability, Fault Tolerance, Durability, and others. At the same time, not only reliable but also fast data storage remains one of the most pressing issues in this area. That brings us to the consensus algorithm as one of the most important components that has a great impact on the functionality of a distributed system. This paper is the result of an analysis of several well-known consensus algorithms, such as Paxos and Raft. The algorithm it offers, called Sync, promotes, but does not insist on simultaneous writing to the nodes (which positively affects the overall writing speed) and tries to minimize the system's inactive time. This allows nodes to reach agreement on the system state in a shorter period, which is a critical factor for distributed systems. Also when developing Sync, a lot of attention was paid to such criteria as simplicity and intuitiveness, the importance of which is difficult to overestimate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sync" title="sync">sync</a>, <a href="https://publications.waset.org/abstracts/search?q=consensus%20algorithm" title=" consensus algorithm"> consensus algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed%20system" title=" distributed system"> distributed system</a>, <a href="https://publications.waset.org/abstracts/search?q=leader-based" title=" leader-based"> leader-based</a>, <a href="https://publications.waset.org/abstracts/search?q=synchronization." title=" synchronization."> synchronization.</a> </p> <a href="https://publications.waset.org/abstracts/179045/sync-consensus-algorithm-trying-to-reach-an-agreement-at-full-speed" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/179045.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">62</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5423</span> An Algorithm for Herding Cows by a Swarm of Quadcopters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jeryes%20Danial">Jeryes Danial</a>, <a href="https://publications.waset.org/abstracts/search?q=Yosi%20Ben%20Asher"> Yosi Ben Asher</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Algorithms for controlling a swarm of robots is an active research field, out of which cattle herding is one of the most complex problems to solve. In this paper, we derive an independent herding algorithm that is specifically designed for a swarm of quadcopters. The algorithm works by devising flight trajectories that cause the cows to run-away in the desired direction and hence herd cows that are distributed in a given field towards a common gathering point. Unlike previously proposed swarm herding algorithms, this algorithm does not use a flocking model but rather stars each cow separately. The effectiveness of this algorithm is verified experimentally using a simulator. We use a special set of experiments attempting to demonstrate that the herding times of this algorithm correspond to field diameter small constant regardless of the number of cows in the field. This is an optimal result indicating that the algorithm groups the cows into intermediate groups and herd them as one forming ever closing bigger groups. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=swarm" title="swarm">swarm</a>, <a href="https://publications.waset.org/abstracts/search?q=independent" title=" independent"> independent</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed" title=" distributed"> distributed</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithm" title=" algorithm"> algorithm</a> </p> <a href="https://publications.waset.org/abstracts/134795/an-algorithm-for-herding-cows-by-a-swarm-of-quadcopters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/134795.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">176</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5422</span> Distributed Coverage Control by Robot Networks in Unknown Environments Using a Modified EM Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammadhosein%20Hasanbeig">Mohammadhosein Hasanbeig</a>, <a href="https://publications.waset.org/abstracts/search?q=Lacra%20Pavel"> Lacra Pavel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we study a distributed control algorithm for the problem of unknown area coverage by a network of robots. The coverage objective is to locate a set of targets in the area and to minimize the robots’ energy consumption. The robots have no prior knowledge about the location and also about the number of the targets in the area. One efficient approach that can be used to relax the robots’ lack of knowledge is to incorporate an auxiliary learning algorithm into the control scheme. A learning algorithm actually allows the robots to explore and study the unknown environment and to eventually overcome their lack of knowledge. The control algorithm itself is modeled based on game theory where the network of the robots use their collective information to play a non-cooperative potential game. The algorithm is tested via simulations to verify its performance and adaptability. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20control" title="distributed control">distributed control</a>, <a href="https://publications.waset.org/abstracts/search?q=game%20theory" title=" game theory"> game theory</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-agent%20learning" title=" multi-agent learning"> multi-agent learning</a>, <a href="https://publications.waset.org/abstracts/search?q=reinforcement%20learning" title=" reinforcement learning"> reinforcement learning</a> </p> <a href="https://publications.waset.org/abstracts/61090/distributed-coverage-control-by-robot-networks-in-unknown-environments-using-a-modified-em-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61090.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">457</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5421</span> A Fast Parallel and Distributed Type-2 Fuzzy Algorithm Based on Cooperative Mobile Agents Model for High Performance Image Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fat%C3%A9ma%20Zahra%20Benchara">Fatéma Zahra Benchara</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Youssfi"> Mohamed Youssfi</a>, <a href="https://publications.waset.org/abstracts/search?q=Omar%20Bouattane"> Omar Bouattane</a>, <a href="https://publications.waset.org/abstracts/search?q=Hassan%20Ouajji"> Hassan Ouajji</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Ouadi%20Bensalah"> Mohamed Ouadi Bensalah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this paper is to present a distributed implementation of the Type-2 Fuzzy algorithm in a parallel and distributed computing environment based on mobile agents. The proposed algorithm is assigned to be implemented on a SPMD (Single Program Multiple Data) architecture which is based on cooperative mobile agents as AVPE (Agent Virtual Processing Element) model in order to improve the processing resources needed for performing the big data image segmentation. In this work we focused on the application of this algorithm in order to process the big data MRI (Magnetic Resonance Images) image of size (n x m). It is encapsulated on the Mobile agent team leader in order to be split into (m x n) pixels one per AVPE. Each AVPE perform and exchange the segmentation results and maintain asynchronous communication with their team leader until the convergence of this algorithm. Some interesting experimental results are obtained in terms of accuracy and efficiency analysis of the proposed implementation, thanks to the mobile agents several interesting skills introduced in this distributed computational model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20type-2%20fuzzy%20algorithm" title="distributed type-2 fuzzy algorithm">distributed type-2 fuzzy algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20agents" title=" mobile agents"> mobile agents</a>, <a href="https://publications.waset.org/abstracts/search?q=parallel%20and%20distributed%20computing" title=" parallel and distributed computing"> parallel and distributed computing</a> </p> <a href="https://publications.waset.org/abstracts/24162/a-fast-parallel-and-distributed-type-2-fuzzy-algorithm-based-on-cooperative-mobile-agents-model-for-high-performance-image-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24162.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">428</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5420</span> The Quotation-Based Algorithm for Distributed Decision Making</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gennady%20P.%20Ginkul">Gennady P. Ginkul</a>, <a href="https://publications.waset.org/abstracts/search?q=Sergey%20Yu.%20Soloviov"> Sergey Yu. Soloviov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The article proposes to use so-called "quotation-based algorithm" for simulation of decision making process in distributed expert systems and multi-agent systems. The idea was adopted from the techniques for group decision-making. It is based on the assumption that one expert system to perform its logical inference may use rules from another expert system. The application of the algorithm was demonstrated on the example in which the consolidated decision is the decision that requires minimal quotation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=backward%20chaining%20inference" title="backward chaining inference">backward chaining inference</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed%20expert%20systems" title=" distributed expert systems"> distributed expert systems</a>, <a href="https://publications.waset.org/abstracts/search?q=group%20decision%20making" title=" group decision making"> group decision making</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-agent%20systems" title=" multi-agent systems"> multi-agent systems</a> </p> <a href="https://publications.waset.org/abstracts/61196/the-quotation-based-algorithm-for-distributed-decision-making" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61196.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">375</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5419</span> Research on Control Strategy of Differential Drive Assisted Steering of Distributed Drive Electric Vehicle</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=J.%20Liu">J. Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Z.%20P.%20Yu"> Z. P. Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Xiong"> L. Xiong</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20Feng"> Y. Feng</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20He"> J. He</a> </p> <p class="card-text"><strong>Abstract:</strong></p> According to the independence, accuracy and controllability of the driving/braking torque of the distributed drive electric vehicle, a control strategy of differential drive assisted steering was designed. Firstly, the assisted curve under different speed and steering wheel torque was developed and the differential torques were distributed to the right and left front wheels. Then the steering return ability assisted control algorithm was designed. At last, the joint simulation was conducted by CarSim/Simulink. The result indicated: the differential drive assisted steering algorithm could provide enough steering drive-assisted under low speed and improve the steering portability. Along with the increase of the speed, the provided steering drive-assisted decreased. With the control algorithm, the steering stiffness of the steering system increased along with the increase of the speed, which ensures the driver’s road feeling. The control algorithm of differential drive assisted steering could avoid the understeer under low speed effectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=differential%20assisted%20steering" title="differential assisted steering">differential assisted steering</a>, <a href="https://publications.waset.org/abstracts/search?q=control%20strategy" title=" control strategy"> control strategy</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed%20drive%20electric%20vehicle" title=" distributed drive electric vehicle"> distributed drive electric vehicle</a>, <a href="https://publications.waset.org/abstracts/search?q=driving%2Fbraking%20torque" title=" driving/braking torque"> driving/braking torque</a> </p> <a href="https://publications.waset.org/abstracts/11277/research-on-control-strategy-of-differential-drive-assisted-steering-of-distributed-drive-electric-vehicle" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11277.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">478</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5418</span> A Unique Multi-Class Support Vector Machine Algorithm Using MapReduce</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aditi%20Viswanathan">Aditi Viswanathan</a>, <a href="https://publications.waset.org/abstracts/search?q=Shree%20Ranjani"> Shree Ranjani</a>, <a href="https://publications.waset.org/abstracts/search?q=Aruna%20Govada"> Aruna Govada</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With data sizes constantly expanding, and with classical machine learning algorithms that analyze such data requiring larger and larger amounts of computation time and storage space, the need to distribute computation and memory requirements among several computers has become apparent. Although substantial work has been done in developing distributed binary SVM algorithms and multi-class SVM algorithms individually, the field of multi-class distributed SVMs remains largely unexplored. This research seeks to develop an algorithm that implements the Support Vector Machine over a multi-class data set and is efficient in a distributed environment. For this, we recursively choose the best binary split of a set of classes using a greedy technique. Much like the divide and conquer approach. Our algorithm has shown better computation time during the testing phase than the traditional sequential SVM methods (One vs. One, One vs. Rest) and out-performs them as the size of the data set grows. This approach also classifies the data with higher accuracy than the traditional multi-class algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20algorithm" title="distributed algorithm">distributed algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=MapReduce" title=" MapReduce"> MapReduce</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-class" title=" multi-class"> multi-class</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a> </p> <a href="https://publications.waset.org/abstracts/17433/a-unique-multi-class-support-vector-machine-algorithm-using-mapreduce" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17433.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">401</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5417</span> Screen Method of Distributed Cooperative Navigation Factors for Unmanned Aerial Vehicle Swarm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Can%20Zhang">Can Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Qun%20Li"> Qun Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Yonglin%20Lei"> Yonglin Lei</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhi%20Zhu"> Zhi Zhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Dong%20Guo"> Dong Guo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Aiming at the problem of factor screen in distributed collaborative navigation of dense UAV swarm, an efficient distributed collaborative navigation factor screen method is proposed. The method considered the balance between computing load and positioning accuracy. The proposed algorithm utilized the factor graph model to implement a distributed collaborative navigation algorithm. The GNSS information of the UAV itself and the ranging information between the UAVs are used as the positioning factors. In this distributed scheme, a local factor graph is established for each UAV. The positioning factors of nodes with good geometric position distribution and small variance are selected to participate in the navigation calculation. To demonstrate and verify the proposed methods, the simulation and experiments in different scenarios are performed in this research. Simulation results show that the proposed scheme achieves a good balance between the computing load and positioning accuracy in the distributed cooperative navigation calculation of UAV swarm. This proposed algorithm has important theoretical and practical value for both industry and academic areas. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=screen%20method" title="screen method">screen method</a>, <a href="https://publications.waset.org/abstracts/search?q=cooperative%20positioning%20system" title=" cooperative positioning system"> cooperative positioning system</a>, <a href="https://publications.waset.org/abstracts/search?q=UAV%20swarm" title=" UAV swarm"> UAV swarm</a>, <a href="https://publications.waset.org/abstracts/search?q=factor%20graph" title=" factor graph"> factor graph</a>, <a href="https://publications.waset.org/abstracts/search?q=cooperative%20navigation" title=" cooperative navigation"> cooperative navigation</a> </p> <a href="https://publications.waset.org/abstracts/166690/screen-method-of-distributed-cooperative-navigation-factors-for-unmanned-aerial-vehicle-swarm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/166690.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">79</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5416</span> Identification of Soft Faults in Branched Wire Networks by Distributed Reflectometry and Multi-Objective Genetic Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Soumaya%20Sallem">Soumaya Sallem</a>, <a href="https://publications.waset.org/abstracts/search?q=Marc%20Olivas"> Marc Olivas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This contribution presents a method for detecting, locating, and characterizing soft faults in a complex wired network. The proposed method is based on multi-carrier reflectometry MCTDR (Multi-Carrier Time Domain Reflectometry) combined with a multi-objective genetic algorithm. In order to ensure complete network coverage and eliminate diagnosis ambiguities, the MCTDR test signal is injected at several points on the network, and the data is merged between different reflectometers (sensors) distributed on the network. An adapted multi-objective genetic algorithm is used to merge data in order to obtain more accurate faults location and characterization. The proposed method performances are evaluated from numerical and experimental results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wired%20network" title="wired network">wired network</a>, <a href="https://publications.waset.org/abstracts/search?q=reflectometry" title=" reflectometry"> reflectometry</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20distributed%20diagnosis" title=" network distributed diagnosis"> network distributed diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20genetic%20algorithm" title=" multi-objective genetic algorithm"> multi-objective genetic algorithm</a> </p> <a href="https://publications.waset.org/abstracts/139182/identification-of-soft-faults-in-branched-wire-networks-by-distributed-reflectometry-and-multi-objective-genetic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139182.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">193</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5415</span> Distributed System Computing Resource Scheduling Algorithm Based on Deep Reinforcement Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yitao%20Lei">Yitao Lei</a>, <a href="https://publications.waset.org/abstracts/search?q=Xingxiang%20Zhai"> Xingxiang Zhai</a>, <a href="https://publications.waset.org/abstracts/search?q=Burra%20Venkata%20Durga%20Kumar"> Burra Venkata Durga Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As the quantity and complexity of computing in large-scale software systems increase, distributed system computing becomes increasingly important. The distributed system realizes high-performance computing by collaboration between different computing resources. If there are no efficient resource scheduling resources, the abuse of distributed computing may cause resource waste and high costs. However, resource scheduling is usually an NP-hard problem, so we cannot find a general solution. However, some optimization algorithms exist like genetic algorithm, ant colony optimization, etc. The large scale of distributed systems makes this traditional optimization algorithm challenging to work with. Heuristic and machine learning algorithms are usually applied in this situation to ease the computing load. As a result, we do a review of traditional resource scheduling optimization algorithms and try to introduce a deep reinforcement learning method that utilizes the perceptual ability of neural networks and the decision-making ability of reinforcement learning. Using the machine learning method, we try to find important factors that influence the performance of distributed system computing and help the distributed system do an efficient computing resource scheduling. This paper surveys the application of deep reinforcement learning on distributed system computing resource scheduling proposes a deep reinforcement learning method that uses a recurrent neural network to optimize the resource scheduling, and proposes the challenges and improvement directions for DRL-based resource scheduling algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=resource%20scheduling" title="resource scheduling">resource scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20reinforcement%20learning" title=" deep reinforcement learning"> deep reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed%20system" title=" distributed system"> distributed system</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a> </p> <a href="https://publications.waset.org/abstracts/152538/distributed-system-computing-resource-scheduling-algorithm-based-on-deep-reinforcement-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152538.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">111</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5414</span> Genetic Algorithm Based Node Fault Detection and Recovery in Distributed Sensor Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20Nalini">N. Nalini</a>, <a href="https://publications.waset.org/abstracts/search?q=Lokesh%20B.%20Bhajantri"> Lokesh B. Bhajantri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Distributed Sensor Networks, the sensor nodes are prone to failure due to energy depletion and some other reasons. In this regard, fault tolerance of network is essential in distributed sensor environment. Energy efficiency, network or topology control and fault-tolerance are the most important issues in the development of next-generation Distributed Sensor Networks (DSNs). This paper proposes a node fault detection and recovery using Genetic Algorithm (GA) in DSN when some of the sensor nodes are faulty. The main objective of this work is to provide fault tolerance mechanism which is energy efficient and responsive to network using GA, which is used to detect the faulty nodes in the network based on the energy depletion of node and link failure between nodes. The proposed fault detection model is used to detect faults at node level and network level faults (link failure and packet error). Finally, the performance parameters for the proposed scheme are evaluated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20sensor%20networks" title="distributed sensor networks">distributed sensor networks</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20detection%20and%20recovery" title=" fault detection and recovery"> fault detection and recovery</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20technology" title=" information technology"> information technology</a> </p> <a href="https://publications.waset.org/abstracts/8901/genetic-algorithm-based-node-fault-detection-and-recovery-in-distributed-sensor-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8901.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">452</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5413</span> A Highly Efficient Broadcast Algorithm for Computer Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ganesh%20Nandakumaran">Ganesh Nandakumaran</a>, <a href="https://publications.waset.org/abstracts/search?q=Mehmet%20Karaata"> Mehmet Karaata</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A wave is a distributed execution, often made up of a broadcast phase followed by a feedback phase, requiring the participation of all the system processes before a particular event called decision is taken. Wave algorithms with one initiator such as the 1-wave algorithm have been shown to be very efficient for broadcasting messages in tree networks. Extensions of this algorithm broadcasting a sequence of waves using a single initiator have been implemented in algorithms such as the m-wave algorithm. However as the network size increases, having a single initiator adversely affects the message delivery times to nodes further away from the initiator. As a remedy, broadcast waves can be allowed to be initiated by multiple initiator nodes distributed across the network to reduce the completion time of broadcasts. These waves initiated by one or more initiator processes form a collection of waves covering the entire network. Solutions to global-snapshots, distributed broadcast and various synchronization problems can be solved efficiently using waves with multiple concurrent initiators. In this paper, we propose the first stabilizing multi-wave sequence algorithm implementing waves started by multiple initiator processes such that every process in the network receives at least one sequence of broadcasts. Due to being stabilizing, the proposed algorithm can withstand transient faults and do not require initialization. We view a fault as a transient fault if it perturbs the configuration of the system but not its program. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20computing" title="distributed computing">distributed computing</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-node%20broadcast" title=" multi-node broadcast"> multi-node broadcast</a>, <a href="https://publications.waset.org/abstracts/search?q=propagation%20of%20information%20with%20feedback%20and%20cleaning%20%28PFC%29" title=" propagation of information with feedback and cleaning (PFC)"> propagation of information with feedback and cleaning (PFC)</a>, <a href="https://publications.waset.org/abstracts/search?q=stabilization" title=" stabilization"> stabilization</a>, <a href="https://publications.waset.org/abstracts/search?q=wave%20algorithms" title=" wave algorithms"> wave algorithms</a> </p> <a href="https://publications.waset.org/abstracts/22312/a-highly-efficient-broadcast-algorithm-for-computer-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22312.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">504</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5412</span> Distribution Network Optimization by Optimal Placement of Photovoltaic-Based Distributed Generation: A Case Study of the Nigerian Power System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Edafe%20Lucky%20Okotie">Edafe Lucky Okotie</a>, <a href="https://publications.waset.org/abstracts/search?q=Emmanuel%20Osawaru%20Omosigho"> Emmanuel Osawaru Omosigho</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper examines the impacts of the introduction of distributed energy generation (DEG) technology into the Nigerian power system as an alternative means of energy generation at distribution ends using Otovwodo 15 MVA, 33/11kV injection substation as a case study. The overall idea is to increase the generated energy in the system, improve the voltage profile and reduce system losses. A photovoltaic-based distributed energy generator (PV-DEG) was considered and was optimally placed in the network using Genetic Algorithm (GA) in Mat. Lab/Simulink environment. The results of simulation obtained shows that the dynamic performance of the network was optimized with DEG-grid integration. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20energy%20generation%20%28DEG%29" title="distributed energy generation (DEG)">distributed energy generation (DEG)</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm%20%28GA%29" title=" genetic algorithm (GA)"> genetic algorithm (GA)</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20quality" title=" power quality"> power quality</a>, <a href="https://publications.waset.org/abstracts/search?q=total%20load%20demand" title=" total load demand"> total load demand</a>, <a href="https://publications.waset.org/abstracts/search?q=voltage%20profile" title=" voltage profile"> voltage profile</a> </p> <a href="https://publications.waset.org/abstracts/165680/distribution-network-optimization-by-optimal-placement-of-photovoltaic-based-distributed-generation-a-case-study-of-the-nigerian-power-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165680.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">84</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5411</span> Loss Minimization by Distributed Generation Allocation in Radial Distribution System Using Crow Search Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Nageswara%20Rao">M. Nageswara Rao</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20S.%20N.%20K.%20Chaitanya"> V. S. N. K. Chaitanya</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Amarendranath"> K. Amarendranath</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an optimal allocation and sizing of Distributed Generation (DG) in Radial Distribution Network (RDN) for total power loss minimization and enhances the voltage profile of the system. The two main important part of this study first is to find optimal allocation and second is optimum size of DG. The locations of DGs are identified by Analytical expressions and crow search algorithm has been employed to determine the optimum size of DG. In this study, the DG has been placed on single and multiple allocations.CSA is a meta-heuristic algorithm inspired by the intelligent behavior of the crows. Crows stores their excess food in different locations and memorizes those locations to retrieve it when it is needed. They follow each other to do thievery to obtain better food source. This analysis is tested on IEEE 33 bus and IEEE 69 bus under MATLAB environment and the results are compared with existing methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analytical%20expression" title="analytical expression">analytical expression</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed%20generation" title=" distributed generation"> distributed generation</a>, <a href="https://publications.waset.org/abstracts/search?q=crow%20search%20algorithm" title=" crow search algorithm"> crow search algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20loss" title=" power loss"> power loss</a>, <a href="https://publications.waset.org/abstracts/search?q=voltage%20profile" title=" voltage profile"> voltage profile</a> </p> <a href="https://publications.waset.org/abstracts/104210/loss-minimization-by-distributed-generation-allocation-in-radial-distribution-system-using-crow-search-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104210.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">235</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5410</span> A General Variable Neighborhood Search Algorithm to Minimize Makespan of the Distributed Permutation Flowshop Scheduling Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=G.%20M.%20Komaki">G. M. Komaki</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Mobin"> S. Mobin</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Teymourian"> E. Teymourian</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Sheikh"> S. Sheikh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper addresses minimizing the makespan of the distributed permutation flow shop scheduling problem. In this problem, there are several parallel identical factories or flowshops each with series of similar machines. Each job should be allocated to one of the factories and all of the operations of the jobs should be performed in the allocated factory. This problem has recently gained attention and due to NP-Hard nature of the problem, metaheuristic algorithms have been proposed to tackle it. Majority of the proposed algorithms require large computational time which is the main drawback. In this study, a general variable neighborhood search algorithm (GVNS) is proposed where several time-saving schemes have been incorporated into it. Also, the GVNS uses the sophisticated method to change the shaking procedure or perturbation depending on the progress of the incumbent solution to prevent stagnation of the search. The performance of the proposed algorithm is compared to the state-of-the-art algorithms based on standard benchmark instances. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20permutation%20flow%20shop" title="distributed permutation flow shop">distributed permutation flow shop</a>, <a href="https://publications.waset.org/abstracts/search?q=scheduling" title=" scheduling"> scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=makespan" title=" makespan"> makespan</a>, <a href="https://publications.waset.org/abstracts/search?q=general%20variable%20neighborhood%20search%20algorithm" title=" general variable neighborhood search algorithm"> general variable neighborhood search algorithm</a> </p> <a href="https://publications.waset.org/abstracts/37025/a-general-variable-neighborhood-search-algorithm-to-minimize-makespan-of-the-distributed-permutation-flowshop-scheduling-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37025.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">354</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5409</span> Diffusion Adaptation Strategies for Distributed Estimation Based on the Family of Affine Projection Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Shams%20Esfand%20Abadi">Mohammad Shams Esfand Abadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Ranjbar"> Mohammad Ranjbar</a>, <a href="https://publications.waset.org/abstracts/search?q=Reza%20Ebrahimpour"> Reza Ebrahimpour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work presents the distributed processing solution problem in a diffusion network based on the adapt then combine (ATC) and combine then adapt (CTA)selective partial update normalized least mean squares (SPU-NLMS) algorithms. Also, we extend this approach to dynamic selection affine projection algorithm (DS-APA) and ATC-DS-APA and CTA-DS-APA are established. The purpose of ATC-SPU-NLMS and CTA-SPU-NLMS algorithm is to reduce the computational complexity by updating the selected blocks of weight coefficients at every iteration. In CTA-DS-APA and ATC-DS-APA, the number of the input vectors is selected dynamically. Diffusion cooperation strategies have been shown to provide good performance based on these algorithms. The good performance of introduced algorithm is illustrated with various experimental results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=selective%20partial%20update" title="selective partial update">selective partial update</a>, <a href="https://publications.waset.org/abstracts/search?q=affine%20projection" title=" affine projection"> affine projection</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20selection" title=" dynamic selection"> dynamic selection</a>, <a href="https://publications.waset.org/abstracts/search?q=diffusion" title=" diffusion"> diffusion</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20distributed%20networks" title=" adaptive distributed networks"> adaptive distributed networks</a> </p> <a href="https://publications.waset.org/abstracts/20231/diffusion-adaptation-strategies-for-distributed-estimation-based-on-the-family-of-affine-projection-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20231.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">707</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5408</span> Distribution System Planning with Distributed Generation and Capacitor Placements</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nattachote%20Rugthaicharoencheep">Nattachote Rugthaicharoencheep</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a feeder reconfiguration problem in distribution systems. The objective is to minimize the system power loss and to improve bus voltage profile. The optimization problem is subjected to system constraints consisting of load-point voltage limits, radial configuration format, no load-point interruption, and feeder capability limits. A method based on genetic algorithm, a search algorithm based on the mechanics of natural selection and natural genetics, is proposed to determine the optimal pattern of configuration. The developed methodology is demonstrated by a 33-bus radial distribution system with distributed generations and feeder capacitors. The study results show that the optimal on/off patterns of the switches can be identified to give the minimum power loss while respecting all the constraints. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=network%20reconfiguration" title="network reconfiguration">network reconfiguration</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed%20generation%20capacitor%20placement" title=" distributed generation capacitor placement"> distributed generation capacitor placement</a>, <a href="https://publications.waset.org/abstracts/search?q=loss%20reduction" title=" loss reduction"> loss reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a> </p> <a href="https://publications.waset.org/abstracts/93008/distribution-system-planning-with-distributed-generation-and-capacitor-placements" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93008.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">176</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5407</span> Optimal Placement and Sizing of Distributed Generation in Microgrid for Power Loss Reduction and Voltage Profile Improvement</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ferinar%20Moaidi">Ferinar Moaidi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahdi%20Moaidi"> Mahdi Moaidi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Environmental issues and the ever-increasing in demand of electrical energy make it necessary to have distributed generation (DG) resources in the power system. In this research, in order to realize the goals of reducing losses and improving the voltage profile in a microgrid, the allocation and sizing of DGs have been used. The proposed Genetic Algorithm (GA) is described from the array of artificial intelligence methods for solving the problem. The algorithm is implemented on the IEEE 33 buses network. This study is presented in two scenarios, primarily to illustrate the effect of location and determination of DGs has been done to reduce losses and improve the voltage profile. On the other hand, decisions made with the one-level assumptions of load are not universally accepted for all levels of load. Therefore, in this study, load modelling is performed and the results are presented for multi-levels load state. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20generation" title="distributed generation">distributed generation</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=microgrid" title=" microgrid"> microgrid</a>, <a href="https://publications.waset.org/abstracts/search?q=load%20modelling" title=" load modelling"> load modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=loss%20reduction" title=" loss reduction"> loss reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=voltage%20improvement" title=" voltage improvement"> voltage improvement</a> </p> <a href="https://publications.waset.org/abstracts/102530/optimal-placement-and-sizing-of-distributed-generation-in-microgrid-for-power-loss-reduction-and-voltage-profile-improvement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/102530.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">143</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5406</span> An Experimental Testbed Using Virtual Containers for Distributed Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Parth%20Patel">Parth Patel</a>, <a href="https://publications.waset.org/abstracts/search?q=Ying%20Zhu"> Ying Zhu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Distributed systems have become ubiquitous, and they continue their growth through a range of services. With advances in resource virtualization technology such as Virtual Machines (VM) and software containers, developers no longer require high-end servers to test and develop distributed software. Even in commercial production, virtualization has streamlined the process of rapid deployment and service management. This paper introduces a distributed systems testbed that utilizes virtualization to enable distributed systems development on commodity computers. The testbed can be used to develop new services, implement theoretical distributed systems concepts for understanding, and experiment with virtual network topologies. We show its versatility through two case studies that utilize the testbed for implementing a theoretical algorithm and developing our own methodology to find high-risk edges. The results of using the testbed for these use cases have proven the effectiveness and versatility of this testbed across a range of scenarios. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20systems" title="distributed systems">distributed systems</a>, <a href="https://publications.waset.org/abstracts/search?q=experimental%20testbed" title=" experimental testbed"> experimental testbed</a>, <a href="https://publications.waset.org/abstracts/search?q=peer-to-peer%20networks" title=" peer-to-peer networks"> peer-to-peer networks</a>, <a href="https://publications.waset.org/abstracts/search?q=virtual%20container%20technology" title=" virtual container technology"> virtual container technology</a> </p> <a href="https://publications.waset.org/abstracts/133579/an-experimental-testbed-using-virtual-containers-for-distributed-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133579.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">146</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5405</span> A Firefly Based Optimization Technique for Optimal Planning of Voltage Controlled Distributed Generators </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20M.%20Othman">M. M. Othman</a>, <a href="https://publications.waset.org/abstracts/search?q=Walid%20El-Khattam"> Walid El-Khattam</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20G.%20Hegazy"> Y. G. Hegazy</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Y.%20Abdelaziz"> A. Y. Abdelaziz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a method for finding the optimal location and capacity of dispatchable DGs connected to the distribution feeders for optimal planning for a specified power loss without violating the system practical constraints. The distributed generation units in the proposed algorithm is modeled as voltage controlled node with the flexibility to be converted to constant power node in case of reactive power limit violation. The proposed algorithm is implemented in MATLAB and tested on the IEEE 37-nodes feeder. The results that are validated by comparing it with results obtained from other competing methods show the effectiveness, accuracy and speed of the proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20generators" title="distributed generators">distributed generators</a>, <a href="https://publications.waset.org/abstracts/search?q=firefly%20technique" title=" firefly technique"> firefly technique</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20loss" title=" power loss"> power loss</a> </p> <a href="https://publications.waset.org/abstracts/6598/a-firefly-based-optimization-technique-for-optimal-planning-of-voltage-controlled-distributed-generators" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6598.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">533</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5404</span> The Parallelization of Algorithm Based on Partition Principle for Association Rules Discovery</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khadidja%20Belbachir">Khadidja Belbachir</a>, <a href="https://publications.waset.org/abstracts/search?q=Hafida%20Belbachir"> Hafida Belbachir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> subsequently the expansion of the physical supports storage and the needs ceaseless to accumulate several data, the sequential algorithms of associations’ rules research proved to be ineffective. Thus the introduction of the new parallel versions is imperative. We propose in this paper, a parallel version of a sequential algorithm “Partition”. This last is fundamentally different from the other sequential algorithms, because it scans the data base only twice to generate the significant association rules. By consequence, the parallel approach does not require much communication between the sites. The proposed approach was implemented for an experimental study. The obtained results, shows a great reduction in execution time compared to the sequential version and Count Distributed algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=association%20rules" title="association rules">association rules</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed%20data%20mining" title=" distributed data mining"> distributed data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=partition" title=" partition"> partition</a>, <a href="https://publications.waset.org/abstracts/search?q=parallel%20algorithms" title=" parallel algorithms"> parallel algorithms</a> </p> <a href="https://publications.waset.org/abstracts/34591/the-parallelization-of-algorithm-based-on-partition-principle-for-association-rules-discovery" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34591.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">415</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5403</span> A Golay Pair Based Synchronization Algorithm for Distributed Multiple-Input Multiple-Output System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Weizhi%20Zhong">Weizhi Zhong</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaoyi%20Lu"> Xiaoyi Lu</a>, <a href="https://publications.waset.org/abstracts/search?q=Lei%20Xu"> Lei Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to solve the problem of inaccurate synchronization for distributed multiple-input multiple-output (MIMO) system in multipath environment, a golay pair aided timing synchronization method is proposed in this paper. A new synchronous training sequence based on golay pair is designed. By utilizing the aperiodic auto-correlation complementary property of the new training sequence, the fine timing point is obtained at the receiver. Simulation results show that, compared with the tradition timing synchronization approaches, the proposed algorithm can provide high accuracy in synchronization, especially under multipath condition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20MIMO%20system" title="distributed MIMO system">distributed MIMO system</a>, <a href="https://publications.waset.org/abstracts/search?q=golay%20pair" title=" golay pair"> golay pair</a>, <a href="https://publications.waset.org/abstracts/search?q=multipath" title=" multipath"> multipath</a>, <a href="https://publications.waset.org/abstracts/search?q=synchronization" title=" synchronization"> synchronization</a> </p> <a href="https://publications.waset.org/abstracts/72014/a-golay-pair-based-synchronization-algorithm-for-distributed-multiple-input-multiple-output-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72014.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">247</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Distributed%20Algorithm&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Distributed%20Algorithm&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Distributed%20Algorithm&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Distributed%20Algorithm&page=5">5</a></li> <li class="page-item"><a class="page-link" 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