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algorithm</title> <meta name="description" content="Search results for: evolution algorithm"> <meta name="keywords" content="evolution 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="evolution 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> 5372</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: evolution algorithm</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5372</span> Engineering Optimization Using Two-Stage Differential Evolution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Y.%20Tseng">K. Y. Tseng</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Y.%20Wu"> C. Y. Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper employs a heuristic algorithm to solve engineering problems including truss structure optimization and optimal chiller loading (OCL) problems. Two different type algorithms, real-valued differential evolution (DE) and modified binary differential evolution (MBDE), are successfully integrated and then can obtain better performance in solving engineering problems. In order to demonstrate the performance of the proposed algorithm, this study adopts each one testing case of truss structure optimization and OCL problems to compare the results of other heuristic optimization methods. The result indicates that the proposed algorithm can obtain similar or better solution in comparing with previous studies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=differential%20evolution" title="differential evolution">differential evolution</a>, <a href="https://publications.waset.org/abstracts/search?q=Truss%20structure%20optimization" title=" Truss structure optimization"> Truss structure optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20chiller%20loading" title=" optimal chiller loading"> optimal chiller loading</a>, <a href="https://publications.waset.org/abstracts/search?q=modified%20binary%20differential%20evolution" title=" modified binary differential evolution"> modified binary differential evolution</a> </p> <a href="https://publications.waset.org/abstracts/109896/engineering-optimization-using-two-stage-differential-evolution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/109896.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">168</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">5371</span> Evolution under Length Constraints for Convolutional Neural Networks Architecture Design</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ousmane%20Youme">Ousmane Youme</a>, <a href="https://publications.waset.org/abstracts/search?q=Jean%20Marie%20Dembele"> Jean Marie Dembele</a>, <a href="https://publications.waset.org/abstracts/search?q=Eugene%20Ezin"> Eugene Ezin</a>, <a href="https://publications.waset.org/abstracts/search?q=Christophe%20Cambier"> Christophe Cambier</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, the convolutional neural networks (CNN) architectures designed by evolution algorithms have proven to be competitive with handcrafted architectures designed by experts. However, these algorithms need a lot of computational power, which is beyond the capabilities of most researchers and engineers. To overcome this problem, we propose an evolution architecture under length constraints. It consists of two algorithms: a search length strategy to find an optimal space and a search architecture strategy based on a genetic algorithm to find the best individual in the optimal space. Our algorithms drastically reduce resource costs and also keep good performance. On the Cifar-10 dataset, our framework presents outstanding performance with an error rate of 5.12% and only 4.6 GPU a day to converge to the optimal individual -22 GPU a day less than the lowest cost automatic evolutionary algorithm in the peer competition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CNN%20architecture" title="CNN architecture">CNN architecture</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=evolution%20algorithm" title=" evolution algorithm"> evolution algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=length%20constraints" title=" length constraints"> length constraints</a> </p> <a href="https://publications.waset.org/abstracts/154373/evolution-under-length-constraints-for-convolutional-neural-networks-architecture-design" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/154373.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">5370</span> Image Segmentation of Visual Markers in Robotic Tracking System Based on Differential Evolution Algorithm with Connected-Component Labeling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shu-Yu%20Hsu">Shu-Yu Hsu</a>, <a href="https://publications.waset.org/abstracts/search?q=Chen-Chien%20Hsu"> Chen-Chien Hsu</a>, <a href="https://publications.waset.org/abstracts/search?q=Wei-Yen%20Wang"> Wei-Yen Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Color segmentation is a basic and simple way for recognizing the visual markers in a robotic tracking system. In this paper, we propose a new method for color segmentation by incorporating differential evolution algorithm and connected component labeling to autonomously preset the HSV threshold of visual markers. To evaluate the effectiveness of the proposed algorithm, a ROBOTIS OP2 humanoid robot is used to conduct the experiment, where five most commonly used color including red, purple, blue, yellow, and green in visual markers are given for comparisons. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=color%20segmentation" title="color segmentation">color segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=differential%20evolution" title=" differential evolution"> differential evolution</a>, <a href="https://publications.waset.org/abstracts/search?q=connected%20component%20labeling" title=" connected component labeling"> connected component labeling</a>, <a href="https://publications.waset.org/abstracts/search?q=humanoid%20robot" title=" humanoid robot"> humanoid robot</a> </p> <a href="https://publications.waset.org/abstracts/34585/image-segmentation-of-visual-markers-in-robotic-tracking-system-based-on-differential-evolution-algorithm-with-connected-component-labeling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34585.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">605</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">5369</span> An Algorithm to Compute the State Estimation of a Bilinear Dynamical Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdullah%20Eqal%20Al%20Mazrooei">Abdullah Eqal Al Mazrooei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we introduce a mathematical algorithm which is used for estimating the states in the bilinear systems. This algorithm uses a special linearization of the second-order term by using the best available information about the state of the system. This technique makes our algorithm generalizes the well-known Kalman estimators. The system which is used here is of the bilinear class, the evolution of this model is linear-bilinear in the state of the system. Our algorithm can be used with linear and bilinear systems. We also here introduced a real application for the new algorithm to prove the feasibility and the efficiency for it. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=estimation%20algorithm" title="estimation algorithm">estimation algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=bilinear%20systems" title=" bilinear systems"> bilinear systems</a>, <a href="https://publications.waset.org/abstracts/search?q=Kakman%20filter" title=" Kakman filter"> Kakman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=second%20order%20linearization" title=" second order linearization"> second order linearization</a> </p> <a href="https://publications.waset.org/abstracts/51466/an-algorithm-to-compute-the-state-estimation-of-a-bilinear-dynamical-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51466.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">486</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">5368</span> Evolution of Multimodulus Algorithm Blind Equalization Based on Recursive Least Square Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sardar%20Ameer%20Akram%20Khan">Sardar Ameer Akram Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Shahzad%20Amin%20Sheikh"> Shahzad Amin Sheikh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Blind equalization is an important technique amongst equalization family. Multimodulus algorithms based on blind equalization removes the undesirable effects of ISI and cater ups the phase issues, saving the cost of rotator at the receiver end. In this paper a new algorithm combination of recursive least square and Multimodulus algorithm named as RLSMMA is proposed by providing few assumption, fast convergence and minimum Mean Square Error (MSE) is achieved. The excellence of this technique is shown in the simulations presenting MSE plots and the resulting filter results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=blind%20equalizations" title="blind equalizations">blind equalizations</a>, <a href="https://publications.waset.org/abstracts/search?q=constant%20modulus%20algorithm" title=" constant modulus algorithm"> constant modulus algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-modulus%20algorithm" title=" multi-modulus algorithm"> multi-modulus algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=recursive%20%20least%20square%20algorithm" title=" recursive least square algorithm"> recursive least square algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=quadrature%20amplitude%20modulation%20%28QAM%29" title=" quadrature amplitude modulation (QAM)"> quadrature amplitude modulation (QAM)</a> </p> <a href="https://publications.waset.org/abstracts/24704/evolution-of-multimodulus-algorithm-blind-equalization-based-on-recursive-least-square-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24704.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">644</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">5367</span> Development of a General Purpose Computer Programme Based on Differential Evolution Algorithm: An Application towards Predicting Elastic Properties of Pavement</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sai%20Sankalp%20Vemavarapu">Sai Sankalp Vemavarapu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper discusses the application of machine learning in the field of transportation engineering for predicting engineering properties of pavement more accurately and efficiently. Predicting the elastic properties aid us in assessing the current road conditions and taking appropriate measures to avoid any inconvenience to commuters. This improves the longevity and sustainability of the pavement layer while reducing its overall life-cycle cost. As an example, we have implemented differential evolution (DE) in the back-calculation of the elastic modulus of multi-layered pavement. The proposed DE global optimization back-calculation approach is integrated with a forward response model. This approach treats back-calculation as a global optimization problem where the cost function to be minimized is defined as the root mean square error in measured and computed deflections. The optimal solution which is elastic modulus, in this case, is searched for in the solution space by the DE algorithm. The best DE parameter combinations and the most optimum value is predicted so that the results are reproducible whenever the need arises. The algorithm’s performance in varied scenarios was analyzed by changing the input parameters. The prediction was well within the permissible error, establishing the supremacy of DE. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cost%20function" title="cost function">cost function</a>, <a href="https://publications.waset.org/abstracts/search?q=differential%20evolution" title=" differential evolution"> differential evolution</a>, <a href="https://publications.waset.org/abstracts/search?q=falling%20weight%20deflectometer" title=" falling weight deflectometer"> falling weight deflectometer</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=global%20optimization" title=" global optimization"> global optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=metaheuristic%20algorithm" title=" metaheuristic algorithm"> metaheuristic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=multilayered%20pavement" title=" multilayered pavement"> multilayered pavement</a>, <a href="https://publications.waset.org/abstracts/search?q=pavement%20condition%20assessment" title=" pavement condition assessment"> pavement condition assessment</a>, <a href="https://publications.waset.org/abstracts/search?q=pavement%20layer%20moduli%20back%20calculation" title=" pavement layer moduli back calculation"> pavement layer moduli back calculation</a> </p> <a href="https://publications.waset.org/abstracts/100787/development-of-a-general-purpose-computer-programme-based-on-differential-evolution-algorithm-an-application-towards-predicting-elastic-properties-of-pavement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/100787.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">164</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">5366</span> Offset Dependent Uniform Delay Mathematical Optimization Model for Signalized Traffic Network Using Differential Evolution Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tahseen%20Saad">Tahseen Saad</a>, <a href="https://publications.waset.org/abstracts/search?q=Halim%20Ceylan"> Halim Ceylan</a>, <a href="https://publications.waset.org/abstracts/search?q=Jonathan%20Weaver"> Jonathan Weaver</a>, <a href="https://publications.waset.org/abstracts/search?q=Osman%20Nuri%20%C3%87elik"> Osman Nuri Çelik</a>, <a href="https://publications.waset.org/abstracts/search?q=Onur%20Gungor%20Sahin"> Onur Gungor Sahin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A new concept of uniform delay offset dependent mathematical optimization problem is derived as the main objective for this study using a differential evolution algorithm. To control the coordination problem, which depends on offset selection and to estimate uniform delay based on the offset choice in a traffic signal network. The assumption is the periodic sinusoidal function for arrival and departure patterns. The cycle time is optimized at the entry links and the optimized value is used in the non-entry links as a common cycle time. The offset optimization algorithm is used to calculate the uniform delay at each link. The results are illustrated by using a case study and are compared with the canonical uniform delay model derived by Webster and the highway capacity manual’s model. The findings show new model minimizes the total uniform delay to almost half compared to conventional models. The mathematical objective function is robust. The algorithm convergence time is fast. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=area%20traffic%20control" title="area traffic control">area traffic control</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20flow" title=" traffic flow"> traffic flow</a>, <a href="https://publications.waset.org/abstracts/search?q=differential%20evolution" title=" differential evolution"> differential evolution</a>, <a href="https://publications.waset.org/abstracts/search?q=sinusoidal%20periodic%20function" title=" sinusoidal periodic function"> sinusoidal periodic function</a>, <a href="https://publications.waset.org/abstracts/search?q=uniform%20delay" title=" uniform delay"> uniform delay</a>, <a href="https://publications.waset.org/abstracts/search?q=offset%20variable" title=" offset variable"> offset variable</a> </p> <a href="https://publications.waset.org/abstracts/154334/offset-dependent-uniform-delay-mathematical-optimization-model-for-signalized-traffic-network-using-differential-evolution-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/154334.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">275</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">5365</span> An Improved Many Worlds Quantum Genetic Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Li%20Dan">Li Dan</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhao%20Junsuo"> Zhao Junsuo</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhang%20Wenjun"> Zhang Wenjun </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Aiming at the shortcomings of the Quantum Genetic Algorithm such as the multimodal function optimization problems easily falling into the local optimum, and vulnerable to premature convergence due to no closely relationship between individuals, the paper presents an Improved Many Worlds Quantum Genetic Algorithm (IMWQGA). The paper using the concept of Many Worlds; using the derivative way of parallel worlds’ parallel evolution; putting forward the thought which updating the population according to the main body; adopting the transition methods such as parallel transition, backtracking, travel forth. In addition, the algorithm in the paper also proposes the quantum training operator and the combinatorial optimization operator as new operators of quantum genetic algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=quantum%20genetic%20algorithm" title="quantum genetic algorithm">quantum genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=many%20worlds" title=" many worlds"> many worlds</a>, <a href="https://publications.waset.org/abstracts/search?q=quantum%20training%20operator" title=" quantum training operator"> quantum training operator</a>, <a href="https://publications.waset.org/abstracts/search?q=combinatorial%20optimization%20operator" title=" combinatorial optimization operator"> combinatorial optimization operator</a> </p> <a href="https://publications.waset.org/abstracts/16842/an-improved-many-worlds-quantum-genetic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16842.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">743</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">5364</span> Radial Basis Surrogate Model Integrated to Evolutionary Algorithm for Solving Computation Intensive Black-Box Problems </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdulbaset%20Saad">Abdulbaset Saad</a>, <a href="https://publications.waset.org/abstracts/search?q=Adel%20Younis"> Adel Younis</a>, <a href="https://publications.waset.org/abstracts/search?q=Zuomin%20Dong"> Zuomin Dong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For design optimization with high-dimensional expensive problems, an effective and efficient optimization methodology is desired. This work proposes a series of modification to the Differential Evolution (DE) algorithm for solving computation Intensive Black-Box Problems. The proposed methodology is called Radial Basis Meta-Model Algorithm Assisted Differential Evolutionary (RBF-DE), which is a global optimization algorithm based on the meta-modeling techniques. A meta-modeling assisted DE is proposed to solve computationally expensive optimization problems. The Radial Basis Function (RBF) model is used as a surrogate model to approximate the expensive objective function, while DE employs a mechanism to dynamically select the best performing combination of parameters such as differential rate, cross over probability, and population size. The proposed algorithm is tested on benchmark functions and real life practical applications and problems. The test results demonstrate that the proposed algorithm is promising and performs well compared to other optimization algorithms. The proposed algorithm is capable of converging to acceptable and good solutions in terms of accuracy, number of evaluations, and time needed to converge. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=differential%20evolution" title="differential evolution">differential evolution</a>, <a href="https://publications.waset.org/abstracts/search?q=engineering%20design" title=" engineering design"> engineering design</a>, <a href="https://publications.waset.org/abstracts/search?q=expensive%20computations" title=" expensive computations"> expensive computations</a>, <a href="https://publications.waset.org/abstracts/search?q=meta-modeling" title=" meta-modeling"> meta-modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function" title=" radial basis function"> radial basis function</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/48247/radial-basis-surrogate-model-integrated-to-evolutionary-algorithm-for-solving-computation-intensive-black-box-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48247.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">396</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">5363</span> Direct Torque Control of Induction Motor Employing Differential Evolution Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=T.%20Vamsee%20Kiran">T. Vamsee Kiran</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Gopi"> A. Gopi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The undesired torque and flux ripple may occur in conventional direct torque control (DTC) induction motor drive. DTC can improve the system performance at low speeds by continuously tuning the regulator by adjusting the Kp, Ki values. In this differential evolution (DE) is proposed to adjust the parameters (Kp, Ki) of the speed controller in order to minimize torque ripple, flux ripple, and stator current distortion.The DE based PI controller has resulted is maintaining a constant speed of the motor irrespective of the load torque fluctuations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=differential%20evolution" title="differential evolution">differential evolution</a>, <a href="https://publications.waset.org/abstracts/search?q=direct%20torque%20control" title=" direct torque control"> direct torque control</a>, <a href="https://publications.waset.org/abstracts/search?q=PI%20controller" title=" PI controller"> PI controller</a> </p> <a href="https://publications.waset.org/abstracts/29123/direct-torque-control-of-induction-motor-employing-differential-evolution-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29123.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">431</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">5362</span> Software Architecture Optimization Using Swarm Intelligence Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arslan%20Ellahi">Arslan Ellahi</a>, <a href="https://publications.waset.org/abstracts/search?q=Syed%20Amjad%20Hussain"> Syed Amjad Hussain</a>, <a href="https://publications.waset.org/abstracts/search?q=Fawaz%20Saleem%20Bokhari"> Fawaz Saleem Bokhari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Optimization of software architecture can be done with respect to a quality attributes (QA). In this paper, there is an analysis of multiple research papers from different dimensions that have been used to classify those attributes. We have proposed a technique of swarm intelligence Meta heuristic ant colony optimization algorithm as a contribution to solve this critical optimization problem of software architecture. We have ranked quality attributes and run our algorithm on every QA, and then we will rank those on the basis of accuracy. At the end, we have selected the most accurate quality attributes. Ant colony algorithm is an effective algorithm and will perform best in optimizing the QA’s and ranking them. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=complexity" title="complexity">complexity</a>, <a href="https://publications.waset.org/abstracts/search?q=rapid%20evolution" title=" rapid evolution"> rapid evolution</a>, <a href="https://publications.waset.org/abstracts/search?q=swarm%20intelligence" title=" swarm intelligence"> swarm intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=dimensions" title=" dimensions"> dimensions</a> </p> <a href="https://publications.waset.org/abstracts/94992/software-architecture-optimization-using-swarm-intelligence-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94992.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">261</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">5361</span> Cyclic Evolution of a Two Fluid Diffusive Universe</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Subhayan%20Maity">Subhayan Maity</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Complete scenario of cosmic evolution from emergent phase to late time acceleration (i.e. non-singular ever expanding Universe) is a popular preference in the recent cosmology. Yet one can’t exclude the idea that other type of evolution pattern of the Universe may also be possible. Especially, the bouncing scenario is becoming a matter of interest now a days. The present work is an exhibition of such a different pattern of cosmic evolution where the evolution of Universe has been shown as a cyclic thermodynamic process. Under diffusion mechanism (non-equilibrium thermodynamic process), the cosmic evolution has been modelled as [ emergent - accelerated expansion - decelerated expansion - decelerated contraction - accelerated contraction - emergent] . <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=non-equilibrium%20thermodynamics" title="non-equilibrium thermodynamics">non-equilibrium thermodynamics</a>, <a href="https://publications.waset.org/abstracts/search?q=non%20singular%20evolution%20of%20universe" title=" non singular evolution of universe"> non singular evolution of universe</a>, <a href="https://publications.waset.org/abstracts/search?q=cyclic%20evolution" title=" cyclic evolution"> cyclic evolution</a>, <a href="https://publications.waset.org/abstracts/search?q=diffusive%20fluid" title=" diffusive fluid"> diffusive fluid</a> </p> <a href="https://publications.waset.org/abstracts/146121/cyclic-evolution-of-a-two-fluid-diffusive-universe" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146121.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">140</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">5360</span> Continuous Differential Evolution Based Parameter Estimation Framework for Signal Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ammara%20Mehmood">Ammara Mehmood</a>, <a href="https://publications.waset.org/abstracts/search?q=Aneela%20Zameer"> Aneela Zameer</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Asif%20Zahoor%20Raja"> Muhammad Asif Zahoor Raja</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Faisal%20Fateh"> Muhammad Faisal Fateh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, the strength of bio-inspired computational intelligence based technique is exploited for parameter estimation for the periodic signals using Continuous Differential Evolution (CDE) by deﬁning an error function in the mean square sense. Multidimensional and nonlinear nature of the problem emerging in sinusoidal signal models along with noise makes it a challenging optimization task, which is dealt with robustness and effectiveness of CDE to ensure convergence and avoid trapping in local minima. In the proposed scheme of Continuous Differential Evolution based Signal Parameter Estimation (CDESPE), unknown adjustable weights of the signal system identification model are optimized utilizing CDE algorithm. The performance of CDESPE model is validated through statistics based various performance indices on a sufficiently large number of runs in terms of estimation error, mean squared error and Thiel’s inequality coefﬁcient. Efficacy of CDESPE is examined by comparison with the actual parameters of the system, Genetic Algorithm based outcomes and from various deterministic approaches at different signal-to-noise ratio (SNR) levels. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=parameter%20estimation" title="parameter estimation">parameter estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=bio-inspired%20computing" title=" bio-inspired computing"> bio-inspired computing</a>, <a href="https://publications.waset.org/abstracts/search?q=continuous%20differential%20evolution%20%28CDE%29" title=" continuous differential evolution (CDE)"> continuous differential evolution (CDE)</a>, <a href="https://publications.waset.org/abstracts/search?q=periodic%20signals" title=" periodic signals"> periodic signals</a> </p> <a href="https://publications.waset.org/abstracts/72735/continuous-differential-evolution-based-parameter-estimation-framework-for-signal-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72735.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">302</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">5359</span> New Iterative Algorithm for Improving Depth Resolution in Ionic Analysis: Effect of Iterations Number</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20Dahraoui">N. Dahraoui</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Boulakroune"> M. Boulakroune</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Benatia"> D. Benatia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the improvement by deconvolution of the depth resolution in Secondary Ion Mass Spectrometry (SIMS) analysis is considered. Indeed, we have developed a new Tikhonov-Miller deconvolution algorithm where a priori model of the solution is included. This is a denoisy and pre-deconvoluted signal obtained from: firstly, by the application of wavelet shrinkage algorithm, secondly by the introduction of the obtained denoisy signal in an iterative deconvolution algorithm. In particular, we have focused the light on the effect of the iterations number on the evolution of the deconvoluted signals. The SIMS profiles are multilayers of Boron in Silicon matrix. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DRF" title="DRF">DRF</a>, <a href="https://publications.waset.org/abstracts/search?q=in-depth%20resolution" title=" in-depth resolution"> in-depth resolution</a>, <a href="https://publications.waset.org/abstracts/search?q=multiresolution%20deconvolution" title=" multiresolution deconvolution"> multiresolution deconvolution</a>, <a href="https://publications.waset.org/abstracts/search?q=SIMS" title=" SIMS"> SIMS</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20shrinkage" title=" wavelet shrinkage"> wavelet shrinkage</a> </p> <a href="https://publications.waset.org/abstracts/22225/new-iterative-algorithm-for-improving-depth-resolution-in-ionic-analysis-effect-of-iterations-number" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22225.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">418</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">5358</span> DEA-Based Variable Structure Position Control of DC Servo Motor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ladan%20Maijama%E2%80%99a">Ladan Maijama’a</a>, <a href="https://publications.waset.org/abstracts/search?q=Jibril%20D.%20Jiya"> Jibril D. Jiya</a>, <a href="https://publications.waset.org/abstracts/search?q=Ejike%20C.%20Anene"> Ejike C. Anene</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents Differential Evolution Algorithm (DEA) based Variable Structure Position Control (VSPC) of Laboratory DC servomotor (LDCSM). DEA is employed for the optimal tuning of Variable Structure Control (VSC) parameters for position control of a DC servomotor. The VSC combines the techniques of Sliding Mode Control (SMC) that gives the advantages of small overshoot, improved step response characteristics, faster dynamic response and adaptability to plant parameter variations, suppressed influences of disturbances and uncertainties in system behavior. The results of the simulation responses of the VSC parameters adjustment by DEA were performed in Matlab Version 2010a platform and yield better dynamic performance compared with the untuned VSC designed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=differential%20evolution%20algorithm" title="differential evolution algorithm">differential evolution algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=laboratory%20DC%20servomotor" title=" laboratory DC servomotor"> laboratory DC servomotor</a>, <a href="https://publications.waset.org/abstracts/search?q=sliding%20mode%20control" title=" sliding mode control"> sliding mode control</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20structure%20control" title=" variable structure control"> variable structure control</a> </p> <a href="https://publications.waset.org/abstracts/37242/dea-based-variable-structure-position-control-of-dc-servo-motor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37242.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">5357</span> A New Evolutionary Algorithm for Multi-Objective Cylindrical Spur Gear Design Optimization </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hammoudi%20Abderazek">Hammoudi Abderazek</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present paper introduces a modified adaptive mixed differential evolution (MAMDE) to select the main geometry parameters of specific cylindrical spur gear. The developed algorithm used the self-adaptive mechanism in order to update the values of mutation and crossover factors. The feasibility rules are used in the selection phase to improve the search exploration of MAMDE. Moreover, the elitism is performed to keep the best individual found in each generation. For the constraints handling the normalization method is used to treat each constraint design equally. The finite element analysis is used to confirm the optimization results for the maximum bending resistance. The simulation results reached in this paper indicate clearly that the proposed algorithm is very competitive in precision gear design optimization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithm" title="evolutionary algorithm">evolutionary algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=spur%20gear" title=" spur gear"> spur gear</a>, <a href="https://publications.waset.org/abstracts/search?q=tooth%20profile" title=" tooth profile"> tooth profile</a>, <a href="https://publications.waset.org/abstracts/search?q=meta-heuristics" title=" meta-heuristics"> meta-heuristics</a> </p> <a href="https://publications.waset.org/abstracts/124030/a-new-evolutionary-algorithm-for-multi-objective-cylindrical-spur-gear-design-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/124030.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">131</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">5356</span> Co-Evolutionary Fruit Fly Optimization Algorithm and Firefly Algorithm for Solving Unconstrained Optimization Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20M.%20Rizk-Allah">R. M. Rizk-Allah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents co-evolutionary fruit fly optimization algorithm based on firefly algorithm (CFOA-FA) for solving unconstrained optimization problems. The proposed algorithm integrates the merits of fruit fly optimization algorithm (FOA), firefly algorithm (FA) and elite strategy to refine the performance of classical FOA. Moreover, co-evolutionary mechanism is performed by applying FA procedures to ensure the diversity of the swarm. Finally, the proposed algorithm CFOA- FA is tested on several benchmark problems from the usual literature and the numerical results have demonstrated the superiority of the proposed algorithm for finding the global optimal solution. <p class="card-text"><strong>Keywords:</strong> <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=fruit%20fly%20optimization%20algorithm" title=" fruit fly optimization algorithm"> fruit fly optimization algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=unconstrained%20optimization%20problems" title=" unconstrained optimization problems"> unconstrained optimization problems</a> </p> <a href="https://publications.waset.org/abstracts/15923/co-evolutionary-fruit-fly-optimization-algorithm-and-firefly-algorithm-for-solving-unconstrained-optimization-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15923.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">536</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">5355</span> Solving the Economic Load Dispatch Problem Using Differential Evolution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alaa%20Sheta">Alaa Sheta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Economic Load Dispatch (ELD) is one of the vital optimization problems in power system planning. Solving the ELD problems mean finding the best mixture of power unit outputs of all members of the power system network such that the total fuel cost is minimized while sustaining operation requirements limits satisfied across the entire dispatch phases. Many optimization techniques were proposed to solve this problem. A famous one is the Quadratic Programming (QP). QP is a very simple and fast method but it still suffer many problem as gradient methods that might trapped at local minimum solutions and cannot handle complex nonlinear functions. Numbers of metaheuristic algorithms were used to solve this problem such as Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO). In this paper, another meta-heuristic search algorithm named Differential Evolution (DE) is used to solve the ELD problem in power systems planning. The practicality of the proposed DE based algorithm is verified for three and six power generator system test cases. The gained results are compared to existing results based on QP, GAs and PSO. The developed results show that differential evolution is superior in obtaining a combination of power loads that fulfill the problem constraints and minimize the total fuel cost. DE found to be fast in converging to the optimal power generation loads and capable of handling the non-linearity of ELD problem. The proposed DE solution is able to minimize the cost of generated power, minimize the total power loss in the transmission and maximize the reliability of the power provided to the customers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=economic%20load%20dispatch" title="economic load dispatch">economic load dispatch</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20systems" title=" power systems"> power systems</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=differential%20evolution" title=" differential evolution"> differential evolution</a> </p> <a href="https://publications.waset.org/abstracts/41828/solving-the-economic-load-dispatch-problem-using-differential-evolution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41828.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">282</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">5354</span> A Hybrid Multi-Objective Firefly-Sine Cosine Algorithm for Multi-Objective Optimization Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gaohuizi%20Guo">Gaohuizi Guo</a>, <a href="https://publications.waset.org/abstracts/search?q=Ning%20Zhang"> Ning Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Firefly algorithm (FA) and Sine Cosine algorithm (SCA) are two very popular and advanced metaheuristic algorithms. However, these algorithms applied to multi-objective optimization problems have some shortcomings, respectively, such as premature convergence and limited exploration capability. Combining the privileges of FA and SCA while avoiding their deficiencies may improve the accuracy and efficiency of the algorithm. This paper proposes a hybridization of FA and SCA algorithms, named multi-objective firefly-sine cosine algorithm (MFA-SCA), to develop a more efficient meta-heuristic algorithm than FA and SCA. <p class="card-text"><strong>Keywords:</strong> <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=hybrid%20algorithm" title=" hybrid algorithm"> hybrid algorithm</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=sine%20cosine%20algorithm" title=" sine cosine algorithm"> sine cosine algorithm</a> </p> <a href="https://publications.waset.org/abstracts/129731/a-hybrid-multi-objective-firefly-sine-cosine-algorithm-for-multi-objective-optimization-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129731.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">168</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">5353</span> Approximating Fixed Points by a Two-Step Iterative Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Safeer%20Hussain%20Khan">Safeer Hussain Khan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we introduce a two-step iterative algorithm to prove a strong convergence result for approximating common fixed points of three contractive-like operators. Our algorithm basically generalizes an existing algorithm..Our iterative algorithm also contains two famous iterative algorithms: Mann iterative algorithm and Ishikawa iterative algorithm. Thus our result generalizes the corresponding results proved for the above three iterative algorithms to a class of more general operators. At the end, we remark that nothing prevents us to extend our result to the case of the iterative algorithm with error terms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=contractive-like%20operator" title="contractive-like operator">contractive-like operator</a>, <a href="https://publications.waset.org/abstracts/search?q=iterative%20algorithm" title=" iterative algorithm"> iterative algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=fixed%20point" title=" fixed point"> fixed point</a>, <a href="https://publications.waset.org/abstracts/search?q=strong%20convergence" title=" strong convergence"> strong convergence</a> </p> <a href="https://publications.waset.org/abstracts/10341/approximating-fixed-points-by-a-two-step-iterative-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10341.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">550</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">5352</span> Tuning Fractional Order Proportional-Integral-Derivative Controller Using Hybrid Genetic Algorithm Particle Swarm and Differential Evolution Optimization Methods for Automatic Voltage Regulator System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fouzi%20Aboura">Fouzi Aboura</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The fractional order proportional-integral-derivative (FOPID) controller or fractional order (PIλDµ) is a proportional-integral-derivative (PID) controller where integral order (λ) and derivative order (µ) are fractional, one of the important application of classical PID is the Automatic Voltage Regulator (AVR).The FOPID controller needs five parameters optimization while the design of conventional PID controller needs only three parameters to be optimized. In our paper we have proposed a comparison between algorithms Differential Evolution (DE) and Hybrid Genetic Algorithm Particle Swarm Optimization (HGAPSO) ,we have studied theirs characteristics and performance analysis to find an optimum parameters of the FOPID controller, a new objective function is also proposed to take into account the relation between the performance criteria’s. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=FOPID%20controller" title="FOPID controller">FOPID controller</a>, <a href="https://publications.waset.org/abstracts/search?q=fractional%20order" title=" fractional order"> fractional order</a>, <a href="https://publications.waset.org/abstracts/search?q=AVR%20system" title=" AVR system"> AVR system</a>, <a href="https://publications.waset.org/abstracts/search?q=objective%20function" title=" objective function"> objective function</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=GA" title=" GA"> GA</a>, <a href="https://publications.waset.org/abstracts/search?q=PSO" title=" PSO"> PSO</a>, <a href="https://publications.waset.org/abstracts/search?q=HGAPSO" title=" HGAPSO"> HGAPSO</a> </p> <a href="https://publications.waset.org/abstracts/164900/tuning-fractional-order-proportional-integral-derivative-controller-using-hybrid-genetic-algorithm-particle-swarm-and-differential-evolution-optimization-methods-for-automatic-voltage-regulator-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164900.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">90</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">5351</span> Genetic Algorithm Based Deep Learning Parameters Tuning for Robot Object Recognition and Grasping</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Delowar%20Hossain">Delowar Hossain</a>, <a href="https://publications.waset.org/abstracts/search?q=Genci%20Capi"> Genci Capi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper concerns with the problem of deep learning parameters tuning using a genetic algorithm (GA) in order to improve the performance of deep learning (DL) method. We present a GA based DL method for robot object recognition and grasping. GA is used to optimize the DL parameters in learning procedure in term of the fitness function that is good enough. After finishing the evolution process, we receive the optimal number of DL parameters. To evaluate the performance of our method, we consider the object recognition and robot grasping tasks. Experimental results show that our method is efficient for robot object recognition and grasping. <p class="card-text"><strong>Keywords:</strong> <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=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20recognition" title=" object recognition"> object recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=robot%20grasping" title=" robot grasping"> robot grasping</a> </p> <a href="https://publications.waset.org/abstracts/67943/genetic-algorithm-based-deep-learning-parameters-tuning-for-robot-object-recognition-and-grasping" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67943.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">353</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">5350</span> A Multi-Population DE with Adaptive Mutation and Local Search for Global Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhoucheng%20Bao">Zhoucheng Bao</a>, <a href="https://publications.waset.org/abstracts/search?q=Haiyan%20Zhu"> Haiyan Zhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Tingting%20Pang"> Tingting Pang</a>, <a href="https://publications.waset.org/abstracts/search?q=Zuling%20Wang"> Zuling Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a multi-population DE with adaptive mutation and local search for global optimization, named AMMADE. In order to better coordinate the cooperation between the populations and the rational use of resources. In AMMADE, the population is divided based on the Euclidean distance sorting method at each generation to appropriately coordinate the cooperation between subpopulations and the usage of resources, such that the best-performed subpopulation will get more computing resources in the next generation. Further, an adaptive local search strategy is employed on the best-performed subpopulation to achieve a balanced search. The proposed algorithm has been tested by solving optimization problems taken from CEC2014 benchmark problems. Experimental results show that our algorithm can achieve a competitive or better than related methods. The results also confirm the significance of devised strategies in the proposed algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=differential%20evolution" title="differential evolution">differential evolution</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-mutation%20strategies" title=" multi-mutation strategies"> multi-mutation strategies</a>, <a href="https://publications.waset.org/abstracts/search?q=memetic%20algorithm" title=" memetic algorithm"> memetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20local%20search" title=" adaptive local search"> adaptive local search</a> </p> <a href="https://publications.waset.org/abstracts/145112/a-multi-population-de-with-adaptive-mutation-and-local-search-for-global-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145112.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">157</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">5349</span> Complex Fuzzy Evolution Equation with Nonlocal Conditions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdelati%20El%20Allaoui">Abdelati El Allaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=Said%20Melliani"> Said Melliani</a>, <a href="https://publications.waset.org/abstracts/search?q=Lalla%20Saadia%20Chadli"> Lalla Saadia Chadli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The objective of this paper is to study the existence and uniqueness of Mild solutions for a complex fuzzy evolution equation with nonlocal conditions that accommodates the notion of fuzzy sets defined by complex-valued membership functions. We first propose definition of complex fuzzy strongly continuous semigroups. We then give existence and uniqueness result relevant to the complex fuzzy evolution equation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Complex%20fuzzy%20evolution%20equations" title="Complex fuzzy evolution equations">Complex fuzzy evolution equations</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlocal%20conditions" title=" nonlocal conditions"> nonlocal conditions</a>, <a href="https://publications.waset.org/abstracts/search?q=mild%20solution" title=" mild solution"> mild solution</a>, <a href="https://publications.waset.org/abstracts/search?q=complex%20fuzzy%20semigroups" title=" complex fuzzy semigroups"> complex fuzzy semigroups</a> </p> <a href="https://publications.waset.org/abstracts/59900/complex-fuzzy-evolution-equation-with-nonlocal-conditions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59900.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">281</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">5348</span> Optimal Operation of Bakhtiari and Roudbar Dam Using Differential Evolution Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ramin%20Mansouri">Ramin Mansouri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the contrast of rivers discharge regime with water demands, one of the best ways to use water resources is to regulate the natural flow of the rivers and supplying water needs to construct dams. Optimal utilization of reservoirs, consideration of multiple important goals together at the same is of very high importance. To study about analyzing this method, statistical data of Bakhtiari and Roudbar dam over 46 years (1955 until 2001) is used. Initially an appropriate objective function was specified and using DE algorithm, the rule curve was developed. In continue, operation policy using rule curves was compared to standard comparative operation policy. The proposed method distributed the lack to the whole year and lowest damage was inflicted to the system. The standard deviation of monthly shortfall of each year with the proposed algorithm was less deviated than the other two methods. The Results show that median values for the coefficients of F and Cr provide the optimum situation and cause DE algorithm not to be trapped in local optimum. The most optimal answer for coefficients are 0.6 and 0.5 for F and Cr coefficients, respectively. After finding the best combination of coefficients values F and CR, algorithms for solving the independent populations were examined. For this purpose, the population of 4, 25, 50, 100, 500 and 1000 members were studied in two generations (G=50 and 100). result indicates that the generation number 200 is suitable for optimizing. The increase in time per the number of population has almost a linear trend, which indicates the effect of population in the runtime algorithm. Hence specifying suitable population to obtain an optimal results is very important. Standard operation policy had better reversibility percentage, but inflicts severe vulnerability to the system. The results obtained in years of low rainfall had very good results compared to other comparative methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=reservoirs" title="reservoirs">reservoirs</a>, <a href="https://publications.waset.org/abstracts/search?q=differential%20evolution" title=" differential evolution"> differential evolution</a>, <a href="https://publications.waset.org/abstracts/search?q=dam" title=" dam"> dam</a>, <a href="https://publications.waset.org/abstracts/search?q=Optimal%20operation" title=" Optimal operation"> Optimal operation</a> </p> <a href="https://publications.waset.org/abstracts/167675/optimal-operation-of-bakhtiari-and-roudbar-dam-using-differential-evolution-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167675.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">78</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">5347</span> Multi-Scale Urban Spatial Evolution Analysis Based on Space Syntax: A Case Study in Modern Yangzhou, China </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dai%20Zhimei">Dai Zhimei</a>, <a href="https://publications.waset.org/abstracts/search?q=Hua%20Chen"> Hua Chen </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The exploration of urban spatial evolution is an important part of urban development research. Therefore, the evolutionary modern Yangzhou urban spatial texture was taken as the research object, and Spatial Syntax was used as the main research tool, this paper explored Yangzhou spatial evolution law and its driving factors from the urban street network scale, district scale and street scale. The study has concluded that at the urban scale, Yangzhou urban spatial evolution is the result of a variety of causes, including physical and geographical condition, policy and planning factors, and traffic conditions, and the evolution of space also has an impact on social, economic, environmental and cultural factors. At the district and street scales, changes in space will have a profound influence on the history of the city and the activities of people. At the end of the article, the matters needing attention during the evolution of urban space were summarized. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=block" title="block">block</a>, <a href="https://publications.waset.org/abstracts/search?q=space%20syntax%20and%20methodology" title=" space syntax and methodology"> space syntax and methodology</a>, <a href="https://publications.waset.org/abstracts/search?q=street" title=" street"> street</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20space" title=" urban space"> urban space</a>, <a href="https://publications.waset.org/abstracts/search?q=Yangzhou" title=" Yangzhou"> Yangzhou</a> </p> <a href="https://publications.waset.org/abstracts/102099/multi-scale-urban-spatial-evolution-analysis-based-on-space-syntax-a-case-study-in-modern-yangzhou-china" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/102099.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">180</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">5346</span> Evolving Convolutional Filter Using Genetic Algorithm for Image Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rujia%20Chen">Rujia Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Ajit%20Narayanan"> Ajit Narayanan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Convolutional neural networks (CNN), as typically applied in deep learning, use layer-wise backpropagation (BP) to construct filters and kernels for feature extraction. Such filters are 2D or 3D groups of weights for constructing feature maps at subsequent layers of the CNN and are shared across the entire input. BP as a gradient descent algorithm has well-known problems of getting stuck at local optima. The use of genetic algorithms (GAs) for evolving weights between layers of standard artificial neural networks (ANNs) is a well-established area of neuroevolution. In particular, the use of crossover techniques when optimizing weights can help to overcome problems of local optima. However, the application of GAs for evolving the weights of filters and kernels in CNNs is not yet an established area of neuroevolution. In this paper, a GA-based filter development algorithm is proposed. The results of the proof-of-concept experiments described in this paper show the proposed GA algorithm can find filter weights through evolutionary techniques rather than BP learning. For some simple classification tasks like geometric shape recognition, the proposed algorithm can achieve 100% accuracy. The results for MNIST classification, while not as good as possible through standard filter learning through BP, show that filter and kernel evolution warrants further investigation as a new subarea of neuroevolution for deep architectures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neuroevolution" title="neuroevolution">neuroevolution</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title=" convolutional neural network"> convolutional 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=filters" title=" filters"> filters</a>, <a href="https://publications.waset.org/abstracts/search?q=kernels" title=" kernels"> kernels</a> </p> <a href="https://publications.waset.org/abstracts/136990/evolving-convolutional-filter-using-genetic-algorithm-for-image-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/136990.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">186</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">5345</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">5344</span> A Geometrical Perspective on the Insulin Evolution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuhei%20Kunihiro">Yuhei Kunihiro</a>, <a href="https://publications.waset.org/abstracts/search?q=Sorin%20V.%20Sabau"> Sorin V. Sabau</a>, <a href="https://publications.waset.org/abstracts/search?q=Kazuhiro%20Shibuya"> Kazuhiro Shibuya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We study the molecular evolution of insulin from the metric geometry point of view. In mathematics, and particularly in geometry, distances and metrics between objects are of fundamental importance. Using a weaker notion than the classical distance, namely the weighted quasi-metrics, one can study the geometry of biological sequences (DNA, mRNA, or proteins) space. We analyze from the geometrical point of view a family of 60 insulin homologous sequences ranging on a large variety of living organisms from human to the nematode C. elegans. We show that the distances between sequences provide important information about the evolution and function of insulin. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=metric%20geometry" title="metric geometry">metric geometry</a>, <a href="https://publications.waset.org/abstracts/search?q=evolution" title=" evolution"> evolution</a>, <a href="https://publications.waset.org/abstracts/search?q=insulin" title=" insulin"> insulin</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20elegans" title=" C. elegans "> C. elegans </a> </p> <a href="https://publications.waset.org/abstracts/1430/a-geometrical-perspective-on-the-insulin-evolution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1430.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">336</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">5343</span> Digestion Optimization Algorithm: A Novel Bio-Inspired Intelligence for Global Optimization Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Akintayo%20E.%20Akinsunmade">Akintayo E. Akinsunmade</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The digestion optimization algorithm is a novel biological-inspired metaheuristic method for solving complex optimization problems. The algorithm development was inspired by studying the human digestive system. The algorithm mimics the process of food ingestion, breakdown, absorption, and elimination to eﬀectively and eﬃciently search for optimal solutions. This algorithm was tested for optimal solutions on seven diﬀerent types of optimization benchmark functions. The algorithm produced optimal solutions with standard errors, which were compared with the exact solution of the test functions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bio-inspired%20algorithm" title="bio-inspired algorithm">bio-inspired algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=benchmark%20optimization%20functions" title=" benchmark optimization functions"> benchmark optimization functions</a>, <a href="https://publications.waset.org/abstracts/search?q=digestive%20system%20in%20human" title=" digestive system in human"> digestive system in human</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithm%20development" title=" algorithm development"> algorithm development</a> </p> <a href="https://publications.waset.org/abstracts/194133/digestion-optimization-algorithm-a-novel-bio-inspired-intelligence-for-global-optimization-problems" class="btn btn-primary 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