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optimization</title> <meta name="description" content="Search results for: network optimization"> <meta name="keywords" content="network optimization"> <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 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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="network optimization"> <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> 7570</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: network optimization</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7570</span> Optimization of Interface Radio of Universal Mobile Telecommunication System Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=O.%20Mohamed%20Amine">O. Mohamed Amine</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Khireddine"> A. Khireddine</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Telecoms operators are always looking to meet their share of the other customers, they try to gain optimum utilization of the deployed equipment and network optimization has become essential. This project consists of optimizing UMTS network, and the study area is an urban area situated in the center of Algiers. It was initially questions to become familiar with the different communication systems (3G) and the optimization technique, its main components, and its fundamental characteristics radios were introduced. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=UMTS" title="UMTS">UMTS</a>, <a href="https://publications.waset.org/abstracts/search?q=UTRAN" title=" UTRAN"> UTRAN</a>, <a href="https://publications.waset.org/abstracts/search?q=WCDMA" title=" WCDMA"> WCDMA</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/52556/optimization-of-interface-radio-of-universal-mobile-telecommunication-system-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52556.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">383</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">7569</span> Sensor Network Routing Optimization by Simulating Eurygaster Life in Wheat Farms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fariborz%20Ahmadi">Fariborz Ahmadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamid%20Salehi"> Hamid Salehi</a>, <a href="https://publications.waset.org/abstracts/search?q=Khosrow%20Karimi"> Khosrow Karimi </a> </p> <p class="card-text"><strong>Abstract:</strong></p> A sensor network is set of sensor nodes that cooperate together to perform a predefined tasks. The important problem in this network is power consumption. So, in this paper one algorithm based on the eurygaster life is introduced to minimize power consumption by the nodes of these networks. In this method the search space of problem is divided into several partitions and each partition is investigated separately. The evaluation results show that our approach is more efficient in comparison to other evolutionary algorithm like genetic algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20computation" title="evolutionary computation">evolutionary computation</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=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor%20network%20optimization" title=" sensor network optimization"> sensor network optimization</a> </p> <a href="https://publications.waset.org/abstracts/41373/sensor-network-routing-optimization-by-simulating-eurygaster-life-in-wheat-farms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41373.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">7568</span> Multiple Query Optimization in Wireless Sensor Networks Using Data Correlation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elaheh%20Vaezpour">Elaheh Vaezpour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data sensing in wireless sensor networks is done by query deceleration the network by the users. In many applications of the wireless sensor networks, many users send queries to the network simultaneously. If the queries are processed separately, the network’s energy consumption will increase significantly. Therefore, it is very important to aggregate the queries before sending them to the network. In this paper, we propose a multiple query optimization framework based on sensors physical and temporal correlation. In the proposed method, queries are merged and sent to network by considering correlation among the sensors in order to reduce the communication cost between the sensors and the base station. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wireless%20sensor%20networks" title="wireless sensor networks">wireless sensor networks</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20query%20optimization" title=" multiple query optimization"> multiple query optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20correlation" title=" data correlation"> data correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=reducing%20energy%20consumption" title=" reducing energy consumption"> reducing energy consumption</a> </p> <a href="https://publications.waset.org/abstracts/73399/multiple-query-optimization-in-wireless-sensor-networks-using-data-correlation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73399.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">334</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">7567</span> Solving the Quadratic Programming Problem Using a Recurrent Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20A.%20Behroozpoor">A. A. Behroozpoor</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20M.%20Mazarei"> M. M. Mazarei </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a fuzzy recurrent neural network is proposed for solving the classical quadratic control problem subject to linear equality and bound constraints. The convergence of the state variables of the proposed neural network to achieve solution optimality is guaranteed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=REFERENCES%20%20%0D%0A%5B1%5D%09Xia" title="REFERENCES [1] Xia">REFERENCES [1] Xia</a>, <a href="https://publications.waset.org/abstracts/search?q=Y" title=" Y"> Y</a>, <a href="https://publications.waset.org/abstracts/search?q=A%20new%20neural%20network%20for%20solving%20linear%20and%20quadratic%20programming%20problems.%20IEEE%20Transactions%20on%20Neural%20Networks" title=" A new neural network for solving linear and quadratic programming problems. IEEE Transactions on Neural Networks"> A new neural network for solving linear and quadratic programming problems. IEEE Transactions on Neural Networks</a>, <a href="https://publications.waset.org/abstracts/search?q=7%286%29" title=" 7(6)"> 7(6)</a>, <a href="https://publications.waset.org/abstracts/search?q=1996" title=" 1996"> 1996</a>, <a href="https://publications.waset.org/abstracts/search?q=pp.1544%E2%80%931548.%0D%0A%5B2%5D%09Xia" title=" pp.1544–1548. [2] Xia"> pp.1544–1548. [2] Xia</a>, <a href="https://publications.waset.org/abstracts/search?q=Y." title=" Y."> Y.</a>, <a href="https://publications.waset.org/abstracts/search?q=%26%20Wang" title=" & Wang"> & Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=J" title=" J"> J</a>, <a href="https://publications.waset.org/abstracts/search?q=A%20recurrent%20neural%20network%20for%20solving%20nonlinear%20convex%20programs%20subject%20to%20linear%20constraints.%20IEEE%20Transactions%20on%20Neural%20Networks" title=" A recurrent neural network for solving nonlinear convex programs subject to linear constraints. IEEE Transactions on Neural Networks"> A recurrent neural network for solving nonlinear convex programs subject to linear constraints. IEEE Transactions on Neural Networks</a>, <a href="https://publications.waset.org/abstracts/search?q=16%282%29" title="16(2)">16(2)</a>, <a href="https://publications.waset.org/abstracts/search?q=2005" title=" 2005"> 2005</a>, <a href="https://publications.waset.org/abstracts/search?q=pp.%20379%E2%80%93386.%0D%0A%5B3%5D%09Xia" title=" pp. 379–386. [3] Xia"> pp. 379–386. [3] Xia</a>, <a href="https://publications.waset.org/abstracts/search?q=Y." title=" Y."> Y.</a>, <a href="https://publications.waset.org/abstracts/search?q=H" title=" H"> H</a>, <a href="https://publications.waset.org/abstracts/search?q=Leung" title=" Leung"> Leung</a>, <a href="https://publications.waset.org/abstracts/search?q=%26%20J" title=" & J"> & J</a>, <a href="https://publications.waset.org/abstracts/search?q=Wang" title=" Wang"> Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=A%20projection%20neural%20network%20and%20its%20application%20to%20constrained%20optimization%20problems.%20IEEE%20Transactions%20Circuits%20and%20Systems-I" title=" A projection neural network and its application to constrained optimization problems. IEEE Transactions Circuits and Systems-I"> A projection neural network and its application to constrained optimization problems. IEEE Transactions Circuits and Systems-I</a>, <a href="https://publications.waset.org/abstracts/search?q=49%284%29" title=" 49(4)"> 49(4)</a>, <a href="https://publications.waset.org/abstracts/search?q=2002" title=" 2002"> 2002</a>, <a href="https://publications.waset.org/abstracts/search?q=pp.447%E2%80%93458.B.%20%0D%0A%5B4%5D%09Q.%20Liu" title=" pp.447–458.B. [4] Q. Liu"> pp.447–458.B. [4] Q. Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Z.%20Guo" title=" Z. Guo"> Z. Guo</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Wang" title=" J. Wang"> J. Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=A%20one-layer%20recurrent%20neural%20network%20for%20constrained%20seudoconvex%20optimization%20and%20its%20application%20for%20dynamic%20portfolio%20optimization.%20Neural%20Networks" title=" A one-layer recurrent neural network for constrained seudoconvex optimization and its application for dynamic portfolio optimization. Neural Networks"> A one-layer recurrent neural network for constrained seudoconvex optimization and its application for dynamic portfolio optimization. Neural Networks</a>, <a href="https://publications.waset.org/abstracts/search?q=26" title=" 26"> 26</a>, <a href="https://publications.waset.org/abstracts/search?q=2012" title=" 2012"> 2012</a>, <a href="https://publications.waset.org/abstracts/search?q=pp.%2099-109." title=" pp. 99-109. "> pp. 99-109. </a> </p> <a href="https://publications.waset.org/abstracts/19435/solving-the-quadratic-programming-problem-using-a-recurrent-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19435.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">643</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">7566</span> A Deep Learning Based Method for Faster 3D Structural Topology Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arya%20Prakash%20Padhi">Arya Prakash Padhi</a>, <a href="https://publications.waset.org/abstracts/search?q=Anupam%20Chakrabarti"> Anupam Chakrabarti</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajib%20Chowdhury"> Rajib Chowdhury</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Topology or layout optimization often gives better performing economic structures and is very helpful in the conceptual design phase. But traditionally it is being done in finite element-based optimization schemes which, although gives a good result, is very time-consuming especially in 3D structures. Among other alternatives machine learning, especially deep learning-based methods, have a very good potential in resolving this computational issue. Here convolutional neural network (3D-CNN) based variational auto encoder (VAE) is trained using a dataset generated from commercially available topology optimization code ABAQUS Tosca using solid isotropic material with penalization (SIMP) method for compliance minimization. The encoded data in latent space is then fed to a 3D generative adversarial network (3D-GAN) to generate the outcome in 64x64x64 size. Here the network consists of 3D volumetric CNN with rectified linear unit (ReLU) activation in between and sigmoid activation in the end. The proposed network is seen to provide almost optimal results with significantly reduced computational time, as there is no iteration involved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=3D%20generative%20adversarial%20network" title="3D generative adversarial network">3D generative adversarial network</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20topology%20optimization" title=" structural topology optimization"> structural topology optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=variational%20auto%20encoder" title=" variational auto encoder"> variational auto encoder</a> </p> <a href="https://publications.waset.org/abstracts/110331/a-deep-learning-based-method-for-faster-3d-structural-topology-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/110331.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">174</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">7565</span> Transmit Power Optimization for Cooperative Beamforming in Reverse-Link MIMO Ad-Hoc Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Younghyun%20Jeon">Younghyun Jeon</a>, <a href="https://publications.waset.org/abstracts/search?q=Seungjoo%20Maeng"> Seungjoo Maeng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the Ad-hoc network, the great interests regarding MIMO scheme leads to their combination, which is also utilized into its applicable network. We manage the field of the problem into Reverse-link MIMO Ad-hoc Network (RMAN) and propose the methodology to maximize the data rate with its power consumption using Node-Cooperative beamforming technique. Based on the result of mathematical optimization formulation, we design the algorithm to construct optimal orthogonal weight vector according to channel feedback and control its transmission power according to QoS-pricing value level. In simulation results, we show the validity of the proposed mathematical optimization result and algorithm which mean that the sum-rate of each link is converged into some point. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ad-hoc%20network" title="ad-hoc network">ad-hoc network</a>, <a href="https://publications.waset.org/abstracts/search?q=MIMO" title=" MIMO"> MIMO</a>, <a href="https://publications.waset.org/abstracts/search?q=cooperative%20beamforming" title=" cooperative beamforming"> cooperative beamforming</a>, <a href="https://publications.waset.org/abstracts/search?q=transmit%20power" title=" transmit power "> transmit power </a> </p> <a href="https://publications.waset.org/abstracts/57761/transmit-power-optimization-for-cooperative-beamforming-in-reverse-link-mimo-ad-hoc-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57761.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">398</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">7564</span> Design an Intelligent Fire Detection System Based on Neural Network and Particle Swarm Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Majid%20Arvan">Majid Arvan</a>, <a href="https://publications.waset.org/abstracts/search?q=Peyman%20Beygi"> Peyman Beygi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sina%20Rokhsati"> Sina Rokhsati</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In-time detection of fire in buildings is of great importance. Employing intelligent methods in data processing in fire detection systems leads to a significant reduction of fire damage at lowest cost. In this paper, the raw data obtained from the fire detection sensor networks in buildings is processed by using intelligent methods based on neural networks and the likelihood of fire happening is predicted. In order to enhance the quality of system, the noise in the sensor data is reduced by analyzing wavelets and applying SVD technique. Meanwhile, the proposed neural network is trained using particle swarm optimization (PSO). In the simulation work, the data is collected from sensor network inside the room and applied to the proposed network. Then the outputs are compared with conventional MLP network. The simulation results represent the superiority of the proposed method over the conventional one. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intelligent%20fire%20detection" title="intelligent fire detection">intelligent fire detection</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=fire%20sensor%20network" title=" fire sensor network"> fire sensor network</a> </p> <a href="https://publications.waset.org/abstracts/55735/design-an-intelligent-fire-detection-system-based-on-neural-network-and-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55735.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">380</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">7563</span> Comparison between Continuous Genetic Algorithms and Particle Swarm Optimization for Distribution Network Reconfiguration</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Linh%20Nguyen%20Tung">Linh Nguyen Tung</a>, <a href="https://publications.waset.org/abstracts/search?q=Anh%20Truong%20Viet"> Anh Truong Viet</a>, <a href="https://publications.waset.org/abstracts/search?q=Nghien%20Nguyen%20Ba"> Nghien Nguyen Ba</a>, <a href="https://publications.waset.org/abstracts/search?q=Chuong%20Trinh%20Trong"> Chuong Trinh Trong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a reconfiguration methodology based on a continuous genetic algorithm (CGA) and particle swarm optimization (PSO) for minimizing active power loss and minimizing voltage deviation. Both algorithms are adapted using graph theory to generate feasible individuals, and the modified crossover is used for continuous variable of CGA. To demonstrate the performance and effectiveness of the proposed methods, a comparative analysis of CGA with PSO for network reconfiguration, on 33-node and 119-bus radial distribution system is presented. The simulation results have shown that both CGA and PSO can be used in the distribution network reconfiguration and CGA outperformed PSO with significant success rate in finding optimal distribution network configuration. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distribution%20network%20reconfiguration" title="distribution network reconfiguration">distribution network reconfiguration</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=continuous%20genetic%20algorithm" title=" continuous genetic algorithm"> continuous genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20loss%20reduction" title=" power loss reduction"> power loss reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=voltage%20deviation" title=" voltage deviation"> voltage deviation</a> </p> <a href="https://publications.waset.org/abstracts/101407/comparison-between-continuous-genetic-algorithms-and-particle-swarm-optimization-for-distribution-network-reconfiguration" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/101407.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">187</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">7562</span> An Introductory Study on Optimization Algorithm for Movable Sensor Network-Based Odor Source Localization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yossiri%20Ariyakul">Yossiri Ariyakul</a>, <a href="https://publications.waset.org/abstracts/search?q=Piyakiat%20Insom"> Piyakiat Insom</a>, <a href="https://publications.waset.org/abstracts/search?q=Poonyawat%20Sangiamkulthavorn"> Poonyawat Sangiamkulthavorn</a>, <a href="https://publications.waset.org/abstracts/search?q=Takamichi%20Nakamoto"> Takamichi Nakamoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the method of optimization algorithm for sensor network comprised of movable sensor nodes which can be used for odor source localization was proposed. A sensor node is composed of an odor sensor, an anemometer, and a wireless communication module. The odor intensity measured from the sensor nodes are sent to the processor to perform the localization based on optimization algorithm by which the odor source localization map is obtained as a result. The map can represent the exact position of the odor source or show the direction toward it remotely. The proposed method was experimentally validated by creating the odor source localization map using three, four, and five sensor nodes in which the accuracy to predict the position of the odor source can be observed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=odor%20sensor" title="odor sensor">odor sensor</a>, <a href="https://publications.waset.org/abstracts/search?q=odor%20source%20localization" title=" odor source localization"> odor source localization</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor%20network" title=" sensor network"> sensor network</a> </p> <a href="https://publications.waset.org/abstracts/76005/an-introductory-study-on-optimization-algorithm-for-movable-sensor-network-based-odor-source-localization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/76005.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">299</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">7561</span> Optimization of Vertical Axis Wind Turbine Based on Artificial Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Affanuddin%20H.%20Siddique">Mohammed Affanuddin H. Siddique</a>, <a href="https://publications.waset.org/abstracts/search?q=Jayesh%20S.%20Shukla"> Jayesh S. Shukla</a>, <a href="https://publications.waset.org/abstracts/search?q=Chetan%20B.%20Meshram"> Chetan B. Meshram</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The neural networks are one of the power tools of machine learning. After the invention of perceptron in early 1980's, the neural networks and its application have grown rapidly. Neural networks are a technique originally developed for pattern investigation. The structure of a neural network consists of neurons connected through synapse. Here, we have investigated the different algorithms and cost function reduction techniques for optimization of vertical axis wind turbine (VAWT) rotor blades. The aerodynamic force coefficients corresponding to the airfoils are stored in a database along with the airfoil coordinates. A forward propagation neural network is created with the input as aerodynamic coefficients and output as the airfoil co-ordinates. In the proposed algorithm, the hidden layer is incorporated into cost function having linear and non-linear error terms. In this article, it is observed that the ANNs (Artificial Neural Network) can be used for the VAWT’s optimization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=VAWT" title="VAWT">VAWT</a>, <a href="https://publications.waset.org/abstracts/search?q=ANN" title=" ANN"> ANN</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=inverse%20design" title=" inverse design"> inverse design</a> </p> <a href="https://publications.waset.org/abstracts/91997/optimization-of-vertical-axis-wind-turbine-based-on-artificial-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/91997.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">323</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">7560</span> Optimization and Retrofitting for an Egyptian Refinery Water Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Mousa">Mohamed Mousa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sacristies in the supply of freshwater, strict regulations on discharging wastewater and the support to encourage sustainable development by water minimization techniques leads to raise the interest of water reusing, regeneration, and recycling. Water is considered a vital element in chemical industries. In this study, an optimization model will be developed to determine the optimal design of refinery’s water network system via source interceptor sink that involves several network alternatives, then a Mixed-Integer Non-Linear programming (MINLP) was used to obtain the optimal network superstructure based on flowrates, the concentration of contaminants, etc. The main objective of the model is to reduce the fixed cost of piping installation interconnections, reducing the operating cots of all streams within the refiner’s water network, and minimize the concentration of pollutants to comply with the environmental regulations. A real case study for one of the Egyptian refineries was studied by GAMS / BARON global optimization platform, and the water network had been retrofitted and optimized, leading to saving around 195 m³/ hr. of freshwater with a total reduction reaches to 26 %. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=freshwater%20minimization" title="freshwater minimization">freshwater minimization</a>, <a href="https://publications.waset.org/abstracts/search?q=modelling" title=" modelling"> modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=GAMS" title=" GAMS"> GAMS</a>, <a href="https://publications.waset.org/abstracts/search?q=BARON" title=" BARON"> BARON</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20network%20design" title=" water network design"> water network design</a>, <a href="https://publications.waset.org/abstracts/search?q=wastewater%20reudction" title=" wastewater reudction"> wastewater reudction</a> </p> <a href="https://publications.waset.org/abstracts/139312/optimization-and-retrofitting-for-an-egyptian-refinery-water-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139312.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">232</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">7559</span> Network Analysis and Sex Prediction based on a full Human Brain Connectome</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Oleg%20Vlasovets">Oleg Vlasovets</a>, <a href="https://publications.waset.org/abstracts/search?q=Fabian%20Schaipp"> Fabian Schaipp</a>, <a href="https://publications.waset.org/abstracts/search?q=Christian%20L.%20Mueller"> Christian L. Mueller</a> </p> <p class="card-text"><strong>Abstract:</strong></p> we conduct a network analysis and predict the sex of 1000 participants based on ”connectome” - pairwise Pearson’s correlation across 436 brain parcels. We solve the non-smooth convex optimization problem, known under the name of Graphical Lasso, where the solution includes a low-rank component. With this solution and machine learning model for a sex prediction, we explain the brain parcels-sex connectivity patterns. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=network%20analysis" title="network analysis">network analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=neuroscience" title=" neuroscience"> neuroscience</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/146685/network-analysis-and-sex-prediction-based-on-a-full-human-brain-connectome" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146685.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">147</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">7558</span> Robot Movement Using the Trust Region Policy Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Romisaa%20Ali">Romisaa Ali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Policy Gradient approach is one of the deep reinforcement learning families that combines deep neural networks (DNN) with reinforcement learning RL to discover the optimum of the control problem through experience gained from the interaction between the robot and its surroundings. In contrast to earlier policy gradient algorithms, which were unable to handle these two types of error because of over-or under-estimation introduced by the deep neural network model, this article will discuss the state-of-the-art SOTA policy gradient technique, trust region policy optimization (TRPO), by applying this method in various environments compared to another policy gradient method, the Proximal Policy Optimization (PPO), to explain their robust optimization, using this SOTA to gather experience data during various training phases after observing the impact of hyper-parameters on neural network performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20neural%20networks" title="deep neural networks">deep neural networks</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=proximal%20policy%20optimization" title=" proximal policy optimization"> proximal policy optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=state-of-the-art" title=" state-of-the-art"> state-of-the-art</a>, <a href="https://publications.waset.org/abstracts/search?q=trust%20region%20policy%20optimization" title=" trust region policy optimization"> trust region policy optimization</a> </p> <a href="https://publications.waset.org/abstracts/158075/robot-movement-using-the-trust-region-policy-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158075.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">169</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">7557</span> Demand Forecasting Using Artificial Neural Networks Optimized by Particle Swarm Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daham%20Owaid%20Matrood">Daham Owaid Matrood</a>, <a href="https://publications.waset.org/abstracts/search?q=Naqaa%20Hussein%20Raheem"> Naqaa Hussein Raheem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Evolutionary algorithms and Artificial neural networks (ANN) are two relatively young research areas that were subject to a steadily growing interest during the past years. This paper examines the use of Particle Swarm Optimization (PSO) to train a multi-layer feed forward neural network for demand forecasting. We use in this paper weekly demand data for packed cement and towels, which have been outfitted by the Northern General Company for Cement and General Company of prepared clothes respectively. The results showed superiority of trained neural networks using particle swarm optimization on neural networks trained using error back propagation because their ability to escape from local optima. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title="artificial neural network">artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=demand%20forecasting" title=" demand forecasting"> demand forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=weight%20optimization" title=" weight optimization"> weight optimization</a> </p> <a href="https://publications.waset.org/abstracts/45069/demand-forecasting-using-artificial-neural-networks-optimized-by-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45069.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">451</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">7556</span> Execution Time Optimization of Workflow Network with Activity Lead-Time</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiaoping%20Qiu">Xiaoping Qiu</a>, <a href="https://publications.waset.org/abstracts/search?q=Binci%20You"> Binci You</a>, <a href="https://publications.waset.org/abstracts/search?q=Yue%20Hu"> Yue Hu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The executive time of the workflow network has an important effect on the efficiency of the business process. In this paper, the activity executive time is divided into the service time and the waiting time, then the lead time can be extracted from the waiting time. The executive time formulas of the three basic structures in the workflow network are deduced based on the activity lead time. Taken the process of e-commerce logistics as an example, insert appropriate lead time for key activities by using Petri net, and the executive time optimization model is built to minimize the waiting time with the time-cost constraints. Then the solution program-using VC++6.0 is compiled to get the optimal solution, which reduces the waiting time of key activities in the workflow, and verifies the role of lead time in the timeliness of e-commerce logistics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electronic%20business" title="electronic business">electronic business</a>, <a href="https://publications.waset.org/abstracts/search?q=execution%20time" title=" execution time"> execution time</a>, <a href="https://publications.waset.org/abstracts/search?q=lead%20time" title=" lead time"> lead time</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20model" title=" optimization model"> optimization model</a>, <a href="https://publications.waset.org/abstracts/search?q=petri%20net" title=" petri net"> petri net</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20workflow%20network" title=" time workflow network"> time workflow network</a> </p> <a href="https://publications.waset.org/abstracts/137019/execution-time-optimization-of-workflow-network-with-activity-lead-time" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137019.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">175</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">7555</span> Robotic Arm Control with Neural Networks Using Genetic Algorithm Optimization Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arbnor%20Pajaziti">Arbnor Pajaziti</a>, <a href="https://publications.waset.org/abstracts/search?q=Hasan%20Cana"> Hasan Cana</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the structural genetic algorithm is used to optimize the neural network to control the joint movements of robotic arm. The robotic arm has also been modeled in 3D and simulated in real-time in MATLAB. It is found that Neural Networks provide a simple and effective way to control the robot tasks. Computer simulation examples are given to illustrate the significance of this method. By combining Genetic Algorithm optimization method and Neural Networks for the given robotic arm with 5 D.O.F. the obtained the results shown that the base joint movements overshooting time without controller was about 0.5 seconds, while with Neural Network controller (optimized with Genetic Algorithm) was about 0.2 seconds, and the population size of 150 gave best results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=robotic%20arm" title="robotic arm">robotic arm</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</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=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/7408/robotic-arm-control-with-neural-networks-using-genetic-algorithm-optimization-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7408.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">523</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">7554</span> Improved Predictive Models for the IRMA Network Using Nonlinear Optimisation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vishwesh%20Kulkarni">Vishwesh Kulkarni</a>, <a href="https://publications.waset.org/abstracts/search?q=Nikhil%20Bellarykar"> Nikhil Bellarykar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cellular complexity stems from the interactions among thousands of different molecular species. Thanks to the emerging fields of systems and synthetic biology, scientists are beginning to unravel these regulatory, signaling, and metabolic interactions and to understand their coordinated action. Reverse engineering of biological networks has has several benefits but a poor quality of data combined with the difficulty in reproducing it limits the applicability of these methods. A few years back, many of the commonly used predictive algorithms were tested on a network constructed in the yeast Saccharomyces cerevisiae (S. cerevisiae) to resolve this issue. The network was a synthetic network of five genes regulating each other for the so-called in vivo reverse-engineering and modeling assessment (IRMA). The network was constructed in S. cereviase since it is a simple and well characterized organism. The synthetic network included a variety of regulatory interactions, thus capturing the behaviour of larger eukaryotic gene networks on a smaller scale. We derive a new set of algorithms by solving a nonlinear optimization problem and show how these algorithms outperform other algorithms on these datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=synthetic%20gene%20network" title="synthetic gene network">synthetic gene network</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20identification" title=" network identification"> network identification</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20modeling" title=" nonlinear modeling"> nonlinear modeling</a> </p> <a href="https://publications.waset.org/abstracts/94037/improved-predictive-models-for-the-irma-network-using-nonlinear-optimisation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94037.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">156</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">7553</span> An Improved Cuckoo Search Algorithm for Voltage Stability Enhancement in Power Transmission Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Reza%20Sirjani">Reza Sirjani</a>, <a href="https://publications.waset.org/abstracts/search?q=Nobosse%20Tafem%20Bolan"> Nobosse Tafem Bolan </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many optimization techniques available in the literature have been developed in order to solve the problem of voltage stability enhancement in power systems. However, there are a number of drawbacks in the use of previous techniques aimed at determining the optimal location and size of reactive compensators in a network. In this paper, an Improved Cuckoo Search algorithm is applied as an appropriate optimization algorithm to determine the optimum location and size of a Static Var Compensator (SVC) in a transmission network. The main objectives are voltage stability improvement and total cost minimization. The results of the presented technique are then compared with other available optimization techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cuckoo%20search%20algorithm" title="cuckoo search algorithm">cuckoo search algorithm</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%20system" title=" power system"> power system</a>, <a href="https://publications.waset.org/abstracts/search?q=var%20compensators" title=" var compensators"> var compensators</a>, <a href="https://publications.waset.org/abstracts/search?q=voltage%20stability" title=" voltage stability"> voltage stability</a> </p> <a href="https://publications.waset.org/abstracts/38354/an-improved-cuckoo-search-algorithm-for-voltage-stability-enhancement-in-power-transmission-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38354.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">551</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">7552</span> Analysis of Decentralized on Demand Cross Layer in Cognitive Radio Ad Hoc Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Sri%20Janani">A. Sri Janani</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Immanuel%20Arokia%20James"> K. Immanuel Arokia James</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cognitive radio ad hoc networks different unlicensed users may acquire different available channel sets. This non-uniform spectrum availability imposes special design challenges for broadcasting in CR ad hoc networks. Cognitive radio automatically detects available channels in wireless spectrum. This is a form of dynamic spectrum management. Cross-layer optimization is proposed, using this can allow far away secondary users can also involve into channel work. So it can increase the throughput and it will overcome the collision and time delay. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cognitive%20radio" title="cognitive radio">cognitive radio</a>, <a href="https://publications.waset.org/abstracts/search?q=cross%20layer%20optimization" title=" cross layer optimization"> cross layer optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=CR%20mesh%20network" title=" CR mesh network"> CR mesh network</a>, <a href="https://publications.waset.org/abstracts/search?q=heterogeneous%20spectrum" title=" heterogeneous spectrum"> heterogeneous spectrum</a>, <a href="https://publications.waset.org/abstracts/search?q=mesh%20topology" title=" mesh topology"> mesh topology</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20routing%20optimization%20technique" title=" random routing optimization technique"> random routing optimization technique</a> </p> <a href="https://publications.waset.org/abstracts/47391/analysis-of-decentralized-on-demand-cross-layer-in-cognitive-radio-ad-hoc-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47391.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">359</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">7551</span> An Improved Discrete Version of Teaching–Learning-Based ‎Optimization for Supply Chain Network Design</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ehsan%20Yadegari">Ehsan Yadegari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> While there are several metaheuristics and exact approaches to solving the Supply Chain Network Design (SCND) problem, there still remains an unfilled gap in using the Teaching-Learning-Based Optimization (TLBO) algorithm. The algorithm has demonstrated desirable results with problems with complicated combinational optimization. The present study introduces a Discrete Self-Study TLBO (DSS-TLBO) with priority-based solution representation that can solve a supply chain network configuration model to lower the total expenses of establishing facilities and the flow of materials. The network features four layers, namely suppliers, plants, distribution centers (DCs), and customer zones. It is designed to meet the customer’s demand through transporting the material between layers of network and providing facilities in the best economic Potential locations. To have a higher quality of the solution and increase the speed of TLBO, a distinct operator was introduced that ensures self-adaptation (self-study) in the algorithm based on the four types of local search. In addition, while TLBO is used in continuous solution representation and priority-based solution representation is discrete, a few modifications were added to the algorithm to remove the solutions that are infeasible. As shown by the results of experiments, the superiority of DSS-TLBO compared to pure TLBO, genetic algorithm (GA) and firefly Algorithm (FA) was established. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=supply%20chain%20network%20design" title="supply chain network design">supply chain network design</a>, <a href="https://publications.waset.org/abstracts/search?q=teaching%E2%80%93learning-based%20optimization" title=" teaching–learning-based optimization"> teaching–learning-based optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=improved%20metaheuristics" title=" improved metaheuristics"> improved metaheuristics</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20solution%20representation" title=" discrete solution representation"> discrete solution representation</a> </p> <a href="https://publications.waset.org/abstracts/184885/an-improved-discrete-version-of-teaching-learning-based-optimization-for-supply-chain-network-design" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184885.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">52</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">7550</span> Pavement Maintenance and Rehabilitation Scheduling Using Genetic Algorithm Based Multi Objective Optimization Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ashwini%20Gowda%20K.%20S">Ashwini Gowda K. S</a>, <a href="https://publications.waset.org/abstracts/search?q=Archana%20M.%20R"> Archana M. R</a>, <a href="https://publications.waset.org/abstracts/search?q=Anjaneyappa%20V"> Anjaneyappa V</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents pavement maintenance and management system (PMMS) to obtain optimum pavement maintenance and rehabilitation strategies and maintenance scheduling for a network using a multi-objective genetic algorithm (MOGA). Optimal pavement maintenance & rehabilitation strategy is to maximize the pavement condition index of the road section in a network with minimum maintenance and rehabilitation cost during the planning period. In this paper, NSGA-II is applied to perform maintenance optimization; this maintenance approach was expected to preserve and improve the existing condition of the highway network in a cost-effective way. The proposed PMMS is applied to a network that assessed pavement based on the pavement condition index (PCI). The minimum and maximum maintenance cost for a planning period of 20 years obtained from the non-dominated solution was found to be 5.190x10¹⁰ ₹ and 4.81x10¹⁰ ₹, respectively. <p class="card-text"><strong>Keywords:</strong> <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=maintenance%20and%20rehabilitation" title=" maintenance and rehabilitation"> maintenance and rehabilitation</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20technique" title=" optimization technique"> optimization technique</a>, <a href="https://publications.waset.org/abstracts/search?q=pavement%20condition%20index" title=" pavement condition index"> pavement condition index</a> </p> <a href="https://publications.waset.org/abstracts/129811/pavement-maintenance-and-rehabilitation-scheduling-using-genetic-algorithm-based-multi-objective-optimization-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129811.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">149</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">7549</span> Nelder-Mead Parametric Optimization of Elastic Metamaterials with Artificial Neural Network Surrogate Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jiaqi%20Dong">Jiaqi Dong</a>, <a href="https://publications.waset.org/abstracts/search?q=Qing-Hua%20Qin"> Qing-Hua Qin</a>, <a href="https://publications.waset.org/abstracts/search?q=Yi%20Xiao"> Yi Xiao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Some of the most fundamental challenges of elastic metamaterials (EMMs) optimization can be attributed to the high consumption of computational power resulted from finite element analysis (FEA) simulations that render the optimization process inefficient. Furthermore, due to the inherent mesh dependence of FEA, minuscule geometry features, which often emerge during the later stages of optimization, induce very fine elements, resulting in enormously high time consumption, particularly when repetitive solutions are needed for computing the objective function. In this study, a surrogate modelling algorithm is developed to reduce computational time in structural optimization of EMMs. The surrogate model is constructed based on a multilayer feedforward artificial neural network (ANN) architecture, trained with prepopulated eigenfrequency data prepopulated from FEA simulation and optimized through regime selection with genetic algorithm (GA) to improve its accuracy in predicting the location and width of the primary elastic band gap. With the optimized ANN surrogate at the core, a Nelder-Mead (NM) algorithm is established and its performance inspected in comparison to the FEA solution. The ANNNM model shows remarkable accuracy in predicting the band gap width and a reduction of time consumption by 47%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title="artificial neural network">artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=mechanical%20metamaterials" title=" mechanical metamaterials"> mechanical metamaterials</a>, <a href="https://publications.waset.org/abstracts/search?q=Nelder-Mead%20optimization" title=" Nelder-Mead optimization"> Nelder-Mead optimization</a> </p> <a href="https://publications.waset.org/abstracts/110099/nelder-mead-parametric-optimization-of-elastic-metamaterials-with-artificial-neural-network-surrogate-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/110099.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">128</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">7548</span> Optimization of Reliability and Communicability of a Random Two-Dimensional Point Patterns Using Delaunay Triangulation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sopheak%20Sorn">Sopheak Sorn</a>, <a href="https://publications.waset.org/abstracts/search?q=Kwok%20Yip%20Szeto"> Kwok Yip Szeto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Reliability is one of the important measures of how well the system meets its design objective, and mathematically is the probability that a complex system will perform satisfactorily. When the system is described by a network of N components (nodes) and their L connection (links), the reliability of the system becomes a network design problem that is an NP-hard combinatorial optimization problem. In this paper, we address the network design problem for a random point set’s pattern in two dimensions. We make use of a Voronoi construction with each cell containing exactly one point in the point pattern and compute the reliability of the Voronoi’s dual, i.e. the Delaunay graph. We further investigate the communicability of the Delaunay network. We find that there is a positive correlation and a negative correlation between the homogeneity of a Delaunay's degree distribution with its reliability and its communicability respectively. Based on the correlations, we alter the communicability and the reliability by performing random edge flips, which preserve the number of links and nodes in the network but can increase the communicability in a Delaunay network at the cost of its reliability. This transformation is later used to optimize a Delaunay network with the optimum geometric mean between communicability and reliability. We also discuss the importance of the edge flips in the evolution of real soap froth in two dimensions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Communicability" title="Communicability">Communicability</a>, <a href="https://publications.waset.org/abstracts/search?q=Delaunay%20triangulation" title=" Delaunay triangulation"> Delaunay triangulation</a>, <a href="https://publications.waset.org/abstracts/search?q=Edge%20Flip" title=" Edge Flip"> Edge Flip</a>, <a href="https://publications.waset.org/abstracts/search?q=Reliability" title=" Reliability"> Reliability</a>, <a href="https://publications.waset.org/abstracts/search?q=Two%20dimensional%20network" title=" Two dimensional network"> Two dimensional network</a>, <a href="https://publications.waset.org/abstracts/search?q=Voronio" title=" Voronio"> Voronio</a> </p> <a href="https://publications.waset.org/abstracts/21555/optimization-of-reliability-and-communicability-of-a-random-two-dimensional-point-patterns-using-delaunay-triangulation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21555.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">419</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">7547</span> Optimizing the Probabilistic Neural Network Training Algorithm for Multi-Class Identification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdelhadi%20Lotfi">Abdelhadi Lotfi</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelkader%20Benyettou"> Abdelkader Benyettou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, a training algorithm for probabilistic neural networks (PNN) is presented. The algorithm addresses one of the major drawbacks of PNN, which is the size of the hidden layer in the network. By using a cross-validation training algorithm, the number of hidden neurons is shrunk to a smaller number consisting of the most representative samples of the training set. This is done without affecting the overall architecture of the network. Performance of the network is compared against performance of standard PNN for different databases from the UCI database repository. Results show an important gain in network size and performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification" title="classification">classification</a>, <a href="https://publications.waset.org/abstracts/search?q=probabilistic%20neural%20networks" title=" probabilistic neural networks"> probabilistic neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20optimization" title=" network optimization"> network optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a> </p> <a href="https://publications.waset.org/abstracts/104139/optimizing-the-probabilistic-neural-network-training-algorithm-for-multi-class-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104139.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">262</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">7546</span> Wireless Sensor Networks Optimization by Using 2-Stage Algorithm Based on Imperialist Competitive Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamid%20R.%20Lashgarian%20Azad">Hamid R. Lashgarian Azad</a>, <a href="https://publications.waset.org/abstracts/search?q=Seyed%20N.%20Shetab%20Boushehri"> Seyed N. Shetab Boushehri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wireless sensor networks (WSN) have become progressively popular due to their wide range of applications. Wireless Sensor Network is made of numerous tiny sensor nodes that are battery-powered. It is a very significant problem to maximize the lifetime of wireless sensor networks. In this paper, we propose a two-stage protocol based on an imperialist competitive algorithm (2S-ICA) to solve a sensor network optimization problem. The energy of the sensors can be greatly reduced and the lifetime of the network reduced by long communication distances between the sensors and the sink. We can minimize the overall communication distance considerably, thereby extending the lifetime of the network lifetime through connecting sensors into a series of independent clusters using 2SICA. Comparison results of the proposed protocol and LEACH protocol, which is common to solving WSN problems, show that our protocol has a better performance in terms of improving network life and increasing the number of transmitted data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wireless%20sensor%20network" title="wireless sensor network">wireless sensor network</a>, <a href="https://publications.waset.org/abstracts/search?q=imperialist%20competitive%20algorithm" title=" imperialist competitive algorithm"> imperialist competitive algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=LEACH%20protocol" title=" LEACH protocol"> LEACH protocol</a>, <a href="https://publications.waset.org/abstracts/search?q=k-means%20clustering" title=" k-means clustering"> k-means clustering</a> </p> <a href="https://publications.waset.org/abstracts/171802/wireless-sensor-networks-optimization-by-using-2-stage-algorithm-based-on-imperialist-competitive-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171802.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">103</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">7545</span> Optimisation of the Input Layer Structure for Feedforward Narx Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zongyan%20Li">Zongyan Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Matt%20Best"> Matt Best</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an optimization method for reducing the number of input channels and the complexity of the feed-forward NARX neural network (NN) without compromising the accuracy of the NN model. By utilizing the correlation analysis method, the most significant regressors are selected to form the input layer of the NN structure. An application of vehicle dynamic model identification is also presented in this paper to demonstrate the optimization technique and the optimal input layer structure and the optimal number of neurons for the neural network is investigated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=correlation%20analysis" title="correlation analysis">correlation analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=F-ratio" title=" F-ratio"> F-ratio</a>, <a href="https://publications.waset.org/abstracts/search?q=levenberg-marquardt" title=" levenberg-marquardt"> levenberg-marquardt</a>, <a href="https://publications.waset.org/abstracts/search?q=MSE" title=" MSE"> MSE</a>, <a href="https://publications.waset.org/abstracts/search?q=NARX" title=" NARX"> NARX</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=optimisation" title=" optimisation"> optimisation</a> </p> <a href="https://publications.waset.org/abstracts/23195/optimisation-of-the-input-layer-structure-for-feedforward-narx-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23195.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">371</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">7544</span> A Multi-Objective Evolutionary Algorithm of Neural Network for Medical Diseases Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sultan%20Noman%20Qasem">Sultan Noman Qasem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an evolutionary algorithm for solving multi-objective optimization problems-based artificial neural network (ANN). The multi-objective evolutionary algorithm used in this study is genetic algorithm while ANN used is radial basis function network (RBFN). The proposed algorithm named memetic elitist Pareto non-dominated sorting genetic algorithm-based RBFNN (MEPGAN). The proposed algorithm is implemented on medical diseases problems. The experimental results indicate that the proposed algorithm is viable, and provides an effective means to design multi-objective RBFNs with good generalization capability and compact network structure. This study shows that MEPGAN generates RBFNs coming with an appropriate balance between accuracy and simplicity, comparing to the other algorithms found in literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function%20network" title="radial basis function network">radial basis function network</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20learning" title=" hybrid learning"> hybrid learning</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</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/15843/a-multi-objective-evolutionary-algorithm-of-neural-network-for-medical-diseases-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15843.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">563</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">7543</span> Optimization of Structures Subjected to Earthquake</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alireza%20%20Lavaei">Alireza Lavaei</a>, <a href="https://publications.waset.org/abstracts/search?q=Alireza%20%20Lohrasbi"> Alireza Lohrasbi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammadali%20M.%20Shahlaei"> Mohammadali M. Shahlaei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To reduce the overall time of structural optimization for earthquake loads two strategies are adopted. In the first strategy, a neural system consisting self-organizing map and radial basis function neural networks, is utilized to predict the time history responses. In this case, the input space is classified by employing a self-organizing map neural network. Then a distinct RBF neural network is trained in each class. In the second strategy, an improved genetic algorithm is employed to find the optimum design. A 72-bar space truss is designed for optimal weight using exact and approximate analysis for the El Centro (S-E 1940) earthquake loading. The numerical results demonstrate the computational advantages and effectiveness of the proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimization" title="optimization">optimization</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=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=self-organizing%20map" title=" self-organizing map"> self-organizing map</a> </p> <a href="https://publications.waset.org/abstracts/53234/optimization-of-structures-subjected-to-earthquake" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53234.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">311</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">7542</span> Geospatial Network Analysis Using Particle Swarm Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Varun%20Singh">Varun Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Mainak%20Bandyopadhyay"> Mainak Bandyopadhyay</a>, <a href="https://publications.waset.org/abstracts/search?q=Maharana%20Pratap%20Singh"> Maharana Pratap Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The shortest path (SP) problem concerns with finding the shortest path from a specific origin to a specified destination in a given network while minimizing the total cost associated with the path. This problem has widespread applications. Important applications of the SP problem include vehicle routing in transportation systems particularly in the field of in-vehicle Route Guidance System (RGS) and traffic assignment problem (in transportation planning). Well known applications of evolutionary methods like Genetic Algorithms (GA), Ant Colony Optimization, Particle Swarm Optimization (PSO) have come up to solve complex optimization problems to overcome the shortcomings of existing shortest path analysis methods. It has been reported by various researchers that PSO performs better than other evolutionary optimization algorithms in terms of success rate and solution quality. Further Geographic Information Systems (GIS) have emerged as key information systems for geospatial data analysis and visualization. This research paper is focused towards the application of PSO for solving the shortest path problem between multiple points of interest (POI) based on spatial data of Allahabad City and traffic speed data collected using GPS. Geovisualization of results of analysis is carried out in GIS. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title="particle swarm optimization">particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=GIS" title=" GIS"> GIS</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20data" title=" traffic data"> traffic data</a>, <a href="https://publications.waset.org/abstracts/search?q=outliers" title=" outliers"> outliers</a> </p> <a href="https://publications.waset.org/abstracts/13181/geospatial-network-analysis-using-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13181.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">483</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">7541</span> Forecasting Optimal Production Program Using Profitability Optimization by Genetic Algorithm and Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Galal%20H.%20Senussi">Galal H. Senussi</a>, <a href="https://publications.waset.org/abstracts/search?q=Muamar%20Benisa"> Muamar Benisa</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanja%20Vasin"> Sanja Vasin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In our business field today, one of the most important issues for any enterprises is cost minimization and profit maximization. Second issue is how to develop a strong and capable model that is able to give us desired forecasting of these two issues. Many researches deal with these issues using different methods. In this study, we developed a model for multi-criteria production program optimization, integrated with Artificial Neural Network. The prediction of the production cost and profit per unit of a product, dealing with two obverse functions at same time can be extremely difficult, especially if there is a great amount of conflict information about production parameters. Feed-Forward Neural Networks are suitable for generalization, which means that the network will generate a proper output as a result to input it has never seen. Therefore, with small set of examples the network will adjust its weight coefficients so the input will generate a proper output. This essential characteristic is of the most important abilities enabling this network to be used in variety of problems spreading from engineering to finance etc. From our results as we will see later, Feed-Forward Neural Networks has a strong ability and capability to map inputs into desired outputs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=project%20profitability" title="project profitability">project profitability</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</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=Pareto%20set" title=" Pareto set"> Pareto set</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a> </p> <a href="https://publications.waset.org/abstracts/9868/forecasting-optimal-production-program-using-profitability-optimization-by-genetic-algorithm-and-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9868.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">445</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=network%20optimization&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=network%20optimization&page=3">3</a></li> <li class="page-item"><a class="page-link" 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