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unconstrained optimization</title> <meta name="description" content="Search results for: unconstrained optimization"> <meta name="keywords" content="unconstrained 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> 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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="unconstrained 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> 3265</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: unconstrained optimization</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3265</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">3264</span> A New Family of Globally Convergent Conjugate Gradient Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Sellami">B. Sellami</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20Laskri"> Y. Laskri</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Belloufi"> M. Belloufi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Conjugate gradient methods are an important class of methods for unconstrained optimization, especially for large-scale problems. Recently, they have been much studied. In this paper, a new family of conjugate gradient method is proposed for unconstrained optimization. This method includes the already existing two practical nonlinear conjugate gradient methods, which produces a descent search direction at every iteration and converges globally provided that the line search satisfies the Wolfe conditions. The numerical experiments are done to test the efficiency of the new method, which implies the new method is promising. In addition the methods related to this family are uniformly discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=conjugate%20gradient%20method" title="conjugate gradient method">conjugate gradient method</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20convergence" title=" global convergence"> global convergence</a>, <a href="https://publications.waset.org/abstracts/search?q=line%20search" title=" line search"> line search</a>, <a href="https://publications.waset.org/abstracts/search?q=unconstrained%20optimization" title=" unconstrained optimization"> unconstrained optimization</a> </p> <a href="https://publications.waset.org/abstracts/40381/a-new-family-of-globally-convergent-conjugate-gradient-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40381.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">410</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">3263</span> A Conjugate Gradient Method for Large Scale Unconstrained Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Belloufi">Mohammed Belloufi</a>, <a href="https://publications.waset.org/abstracts/search?q=Rachid%20Benzine"> Rachid Benzine</a>, <a href="https://publications.waset.org/abstracts/search?q=Badreddine%20Sellami"> Badreddine Sellami</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Conjugate gradient methods is useful for solving large scale optimization problems in scientific and engineering computation, characterized by the simplicity of their iteration and their low memory requirements. It is well known that the search direction plays a main role in the line search method. In this paper, we propose a search direction with the Wolfe line search technique for solving unconstrained optimization problems. Under the above line searches and some assumptions, the global convergence properties of the given methods are discussed. Numerical results and comparisons with other CG methods are given. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=unconstrained%20optimization" title="unconstrained optimization">unconstrained optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=conjugate%20gradient%20method" title=" conjugate gradient method"> conjugate gradient method</a>, <a href="https://publications.waset.org/abstracts/search?q=strong%20Wolfe%20line%20search" title=" strong Wolfe line search"> strong Wolfe line search</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20convergence" title=" global convergence"> global convergence</a> </p> <a href="https://publications.waset.org/abstracts/40028/a-conjugate-gradient-method-for-large-scale-unconstrained-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40028.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">421</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">3262</span> A New Class of Conjugate Gradient Methods Based on a Modified Search Direction for Unconstrained Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Belloufi%20Mohammed">Belloufi Mohammed</a>, <a href="https://publications.waset.org/abstracts/search?q=Sellami%20Badreddine"> Sellami Badreddine</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Conjugate gradient methods have played a special role for solving large scale optimization problems due to the simplicity of their iteration, convergence properties and their low memory requirements. In this work, we propose a new class of conjugate gradient methods which ensures sufficient descent. Moreover, we propose a new search direction with the Wolfe line search technique for solving unconstrained optimization problems, a global convergence result for general functions is established provided that the line search satisfies the Wolfe conditions. Our numerical experiments indicate that our proposed methods are preferable and in general superior to the classical conjugate gradient methods in terms of efficiency and robustness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=unconstrained%20optimization" title="unconstrained optimization">unconstrained optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=conjugate%20gradient%20method" title=" conjugate gradient method"> conjugate gradient method</a>, <a href="https://publications.waset.org/abstracts/search?q=sufficient%20descent%20property" title=" sufficient descent property"> sufficient descent property</a>, <a href="https://publications.waset.org/abstracts/search?q=numerical%20comparisons" title=" numerical comparisons"> numerical comparisons</a> </p> <a href="https://publications.waset.org/abstracts/41725/a-new-class-of-conjugate-gradient-methods-based-on-a-modified-search-direction-for-unconstrained-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41725.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">403</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">3261</span> A New Conjugate Gradient Method with Guaranteed Descent</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Sellami">B. Sellami</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Belloufi"> M. Belloufi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Conjugate gradient methods are an important class of methods for unconstrained optimization, especially for large-scale problems. Recently, they have been much studied. In this paper, we propose a new two-parameter family of conjugate gradient methods for unconstrained optimization. The two-parameter family of methods not only includes the already existing three practical nonlinear conjugate gradient methods, but also has other family of conjugate gradient methods as subfamily. The two-parameter family of methods with the Wolfe line search is shown to ensure the descent property of each search direction. Some general convergence results are also established for the two-parameter family of methods. The numerical results show that this method is efficient for the given test problems. In addition, the methods related to this family are uniformly discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=unconstrained%20optimization" title="unconstrained optimization">unconstrained optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=conjugate%20gradient%20method" title=" conjugate gradient method"> conjugate gradient method</a>, <a href="https://publications.waset.org/abstracts/search?q=line%20search" title=" line search"> line search</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20convergence" title=" global convergence"> global convergence</a> </p> <a href="https://publications.waset.org/abstracts/41734/a-new-conjugate-gradient-method-with-guaranteed-descent" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41734.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">452</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3260</span> A Modified Nonlinear Conjugate Gradient Algorithm for Large Scale Unconstrained Optimization Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tsegay%20Giday%20Woldu">Tsegay Giday Woldu</a>, <a href="https://publications.waset.org/abstracts/search?q=Haibin%20Zhang"> Haibin Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Xin%20Zhang"> Xin Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yemane%20Hailu%20Fissuh"> Yemane Hailu Fissuh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is well known that nonlinear conjugate gradient method is one of the widely used first order methods to solve large scale unconstrained smooth optimization problems. Because of the low memory requirement, attractive theoretical features, practical computational efficiency and nice convergence properties, nonlinear conjugate gradient methods have a special role for solving large scale unconstrained optimization problems. Large scale optimization problems are with important applications in practical and scientific world. However, nonlinear conjugate gradient methods have restricted information about the curvature of the objective function and they are likely less efficient and robust compared to some second order algorithms. To overcome these drawbacks, the new modified nonlinear conjugate gradient method is presented. The noticeable features of our work are that the new search direction possesses the sufficient descent property independent of any line search and it belongs to a trust region. Under mild assumptions and standard Wolfe line search technique, the global convergence property of the proposed algorithm is established. Furthermore, to test the practical computational performance of our new algorithm, numerical experiments are provided and implemented on the set of some large dimensional unconstrained problems. The numerical results show that the proposed algorithm is an efficient and robust compared with other similar algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=conjugate%20gradient%20method" title="conjugate gradient method">conjugate gradient method</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20convergence" title=" global convergence"> global convergence</a>, <a href="https://publications.waset.org/abstracts/search?q=large%20scale%20optimization" title=" large scale optimization"> large scale optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=sufficient%20descent%20property" title=" sufficient descent property"> sufficient descent property</a> </p> <a href="https://publications.waset.org/abstracts/102625/a-modified-nonlinear-conjugate-gradient-algorithm-for-large-scale-unconstrained-optimization-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/102625.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">205</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">3259</span> Development of Scratching Monitoring System Based on Mathematical Model of Unconstrained Bed Sensing Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Takuya%20Sumi">Takuya Sumi</a>, <a href="https://publications.waset.org/abstracts/search?q=Syoko%20Nukaya"> Syoko Nukaya</a>, <a href="https://publications.waset.org/abstracts/search?q=Takashi%20Kaburagi"> Takashi Kaburagi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hiroshi%20Tanaka"> Hiroshi Tanaka</a>, <a href="https://publications.waset.org/abstracts/search?q=Kajiro%20Watanabe"> Kajiro Watanabe</a>, <a href="https://publications.waset.org/abstracts/search?q=Yosuke%20Kurihara"> Yosuke Kurihara</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose an unconstrained measurement system for scratching motion based on mathematical model of unconstrained bed sensing method which could measure the bed vibrations due to the motion of the person on the bed. In this paper, we construct mathematical model of the unconstrained bed monitoring system, and we apply the unconstrained bed sensing method to the system for detecting scratching motion. The proposed sensors are placed under the three bed feet. When the person is lying on the bed, the output signals from the sensors are proportional to the magnitude of the vibration due to the scratching motion. Hence, we could detect the subject鈥檚 scratching motion from the output signals from ceramic sensors. We evaluated two scratching motions using the proposed system in the validity experiment as follows: First experiment is the subject鈥檚 scratching the right side cheek with his right hand, and; second experiment is the subject鈥檚 scratching the shin with another foot. As the results of the experiment, we recognized the scratching signals that enable the determination when the scratching occurred. Furthermore, the difference among the amplitudes of the output signals enabled us to estimate where the subject scratched. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=unconstrained%20bed%20sensing%20method" title="unconstrained bed sensing method">unconstrained bed sensing method</a>, <a href="https://publications.waset.org/abstracts/search?q=scratching" title=" scratching"> scratching</a>, <a href="https://publications.waset.org/abstracts/search?q=body%20movement" title=" body movement"> body movement</a>, <a href="https://publications.waset.org/abstracts/search?q=itchy" title=" itchy"> itchy</a>, <a href="https://publications.waset.org/abstracts/search?q=piezoceramics" title=" piezoceramics"> piezoceramics</a> </p> <a href="https://publications.waset.org/abstracts/1382/development-of-scratching-monitoring-system-based-on-mathematical-model-of-unconstrained-bed-sensing-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1382.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">410</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">3258</span> Sequential Covering Algorithm for Nondifferentiable Global Optimization Problem and Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Rahal">Mohamed Rahal</a>, <a href="https://publications.waset.org/abstracts/search?q=Djaouida%20Guetta"> Djaouida Guetta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the one-dimensional unconstrained global optimization problem of continuous functions satifying a H枚lder condition is considered. We extend the algorithm of sequential covering SCA for Lipschitz functions to a large class of H枚lder functions. The convergence of the method is studied and the algorithm can be applied to systems of nonlinear equations. Finally, some numerical examples are presented and illustrate the efficiency of the present approach. <p class="card-text"><strong>Keywords:</strong> <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=H%C3%B6lder%20functions" title=" H枚lder functions"> H枚lder functions</a>, <a href="https://publications.waset.org/abstracts/search?q=sequential%20covering%20method" title=" sequential covering method"> sequential covering method</a>, <a href="https://publications.waset.org/abstracts/search?q=systems%20of%20nonlinear%20equations" title=" systems of nonlinear equations"> systems of nonlinear equations</a> </p> <a href="https://publications.waset.org/abstracts/6507/sequential-covering-algorithm-for-nondifferentiable-global-optimization-problem-and-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6507.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">369</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">3257</span> Descent Algorithms for Optimization Algorithms Using q-Derivative</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Geetanjali%20Panda">Geetanjali Panda</a>, <a href="https://publications.waset.org/abstracts/search?q=Suvrakanti%20Chakraborty"> Suvrakanti Chakraborty</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, Newton-like descent methods are proposed for unconstrained optimization problems, which use q-derivatives of the gradient of an objective function. First, a local scheme is developed with alternative sufficient optimality condition, and then the method is extended to a global scheme. Moreover, a variant of practical Newton scheme is also developed introducing a real sequence. Global convergence of these schemes is proved under some mild conditions. Numerical experiments and graphical illustrations are provided. Finally, the performance profiles on a test set show that the proposed schemes are competitive to the existing first-order schemes for optimization problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Descent%20algorithm" title="Descent algorithm">Descent algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=line%20search%20method" title=" line search method"> line search method</a>, <a href="https://publications.waset.org/abstracts/search?q=q%20calculus" title=" q calculus"> q calculus</a>, <a href="https://publications.waset.org/abstracts/search?q=Quasi%20Newton%20method" title=" Quasi Newton method"> Quasi Newton method</a> </p> <a href="https://publications.waset.org/abstracts/62700/descent-algorithms-for-optimization-algorithms-using-q-derivative" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62700.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">3256</span> On the convergence of the Mixed Integer Randomized Pattern Search Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ebert%20Brea">Ebert Brea</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose a novel direct search algorithm for identifying at least a local minimum of mixed integer nonlinear unconstrained optimization problems. The Mixed Integer Randomized Pattern Search Algorithm (MIRPSA), so-called by the author, is based on a randomized pattern search, which is modified by the MIRPSA for finding at least a local minimum of our problem. The MIRPSA has two main operations over the randomized pattern search: moving operation and shrinking operation. Each operation is carried out by the algorithm when a set of conditions is held. The convergence properties of the MIRPSA is analyzed using a Markov chain approach, which is represented by an infinite countable set of state space 位, where each state d(q) is defined by a measure of the qth randomized pattern search Hq, for all q in N. According to the algorithm, when a moving operation is carried out on the qth randomized pattern search Hq, the MIRPSA holds its state. Meanwhile, if the MIRPSA carries out a shrinking operation over the qth randomized pattern search Hq, the algorithm will visit the next state, this is, a shrinking operation at the qth state causes a changing of the qth state into (q+1)th state. It is worthwhile pointing out that the MIRPSA never goes back to any visited states because the MIRPSA only visits any qth by shrinking operations. In this article, we describe the MIRPSA for mixed integer nonlinear unconstrained optimization problems for doing a deep study of its convergence properties using Markov chain viewpoint. We herein include a low dimension case for showing more details of the MIRPSA, when the algorithm is used for identifying the minimum of a mixed integer quadratic function. Besides, numerical examples are also shown in order to measure the performance of the MIRPSA. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=direct%20search" title="direct search">direct search</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed%20integer%20optimization" title=" mixed integer optimization"> mixed integer optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20search" title=" random search"> random search</a>, <a href="https://publications.waset.org/abstracts/search?q=convergence" title=" convergence"> convergence</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20chain" title=" Markov chain"> Markov chain</a> </p> <a href="https://publications.waset.org/abstracts/33175/on-the-convergence-of-the-mixed-integer-randomized-pattern-search-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33175.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">470</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">3255</span> Second Order Optimality Conditions in Nonsmooth Analysis on Riemannian Manifolds</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seyedehsomayeh%20Hosseini">Seyedehsomayeh Hosseini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Much attention has been paid over centuries to understanding and solving the problem of minimization of functions. Compared to linear programming and nonlinear unconstrained optimization problems, nonlinear constrained optimization problems are much more difficult. Since the procedure of finding an optimizer is a search based on the local information of the constraints and the objective function, it is very important to develop techniques using geometric properties of the constraints and the objective function. In fact, differential geometry provides a powerful tool to characterize and analyze these geometric properties. Thus, there is clearly a link between the techniques of optimization on manifolds and standard constrained optimization approaches. Furthermore, there are manifolds that are not defined as constrained sets in R^n an important example is the Grassmann manifolds. Hence, to solve optimization problems on these spaces, intrinsic methods are used. In a nondifferentiable problem, the gradient information of the objective function generally cannot be used to determine the direction in which the function is decreasing. Therefore, techniques of nonsmooth analysis are needed to deal with such a problem. As a manifold, in general, does not have a linear structure, the usual techniques, which are often used in nonsmooth analysis on linear spaces, cannot be applied and new techniques need to be developed. This paper presents necessary and sufficient conditions for a strict local minimum of extended real-valued, nonsmooth functions defined on Riemannian manifolds. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Riemannian%20manifolds" title="Riemannian manifolds">Riemannian manifolds</a>, <a href="https://publications.waset.org/abstracts/search?q=nonsmooth%20optimization" title=" nonsmooth optimization"> nonsmooth optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=lower%20semicontinuous%20functions" title=" lower semicontinuous functions"> lower semicontinuous functions</a>, <a href="https://publications.waset.org/abstracts/search?q=subdifferential" title=" subdifferential"> subdifferential</a> </p> <a href="https://publications.waset.org/abstracts/35809/second-order-optimality-conditions-in-nonsmooth-analysis-on-riemannian-manifolds" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35809.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">361</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">3254</span> Classification Method for Turnover While Sleeping Using Multi-Point Unconstrained Sensing Devices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Shiba">K. Shiba</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Kobayashi"> T. Kobayashi</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Kaburagi"> T. Kaburagi</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20Kurihara"> Y. Kurihara</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Elderly population in the world is increasing, and consequently, their nursing burden is also increasing. In such situations, monitoring and evaluating their daily action facilitates efficient nursing care. Especially, we focus on an unconscious activity during sleep, i.e. turnover. Monitoring turnover during sleep is essential to evaluate various conditions related to sleep. Bedsores are considered as one of the monitoring conditions. Changing patient’s posture every two hours is required for caregivers to prevent bedsore. Herein, we attempt to develop an unconstrained nocturnal monitoring system using a sensing device based on piezoelectric ceramics that can detect the vibrations owing to human body movement on the bed. In the proposed method, in order to construct a multi-points sensing, we placed two sensing devices under the right and left legs at the head-side of an ordinary bed. Using this equipment, when a subject lies on the bed, feature is calculated from the output voltages of the sensing devices. In order to evaluate our proposed method, we conducted an experiment with six healthy male subjects. Consequently, the period during which turnover occurs can be correctly classified as the turnover period with 100% accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=turnover" title="turnover">turnover</a>, <a href="https://publications.waset.org/abstracts/search?q=piezoelectric%20ceramics" title=" piezoelectric ceramics"> piezoelectric ceramics</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-points%20sensing" title=" multi-points sensing"> multi-points sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=unconstrained%20monitoring%20system" title=" unconstrained monitoring system"> unconstrained monitoring system</a> </p> <a href="https://publications.waset.org/abstracts/75765/classification-method-for-turnover-while-sleeping-using-multi-point-unconstrained-sensing-devices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75765.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">194</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">3253</span> Global Convergence of a Modified Three-Term Conjugate Gradient Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Belloufi%20Mohammed">Belloufi Mohammed</a>, <a href="https://publications.waset.org/abstracts/search?q=Sellami%20Badreddine"> Sellami Badreddine</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with a new nonlinear modified three-term conjugate gradient algorithm for solving large-scale unstrained optimization problems. The search direction of the algorithms from this class has three terms and is computed as modifications of the classical conjugate gradient algorithms to satisfy both the descent and the conjugacy conditions. An example of three-term conjugate gradient algorithm from this class, as modifications of the classical and well known Hestenes and Stiefel or of the CG_DESCENT by Hager and Zhang conjugate gradient algorithms, satisfying both the descent and the conjugacy conditions is presented. Under mild conditions, we prove that the modified three-term conjugate gradient algorithm with Wolfe type line search is globally convergent. Preliminary numerical results show the proposed method is very promising. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=unconstrained%20optimization" title="unconstrained optimization">unconstrained optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=three-term%20conjugate%20gradient" title=" three-term conjugate gradient"> three-term conjugate gradient</a>, <a href="https://publications.waset.org/abstracts/search?q=sufficient%20descent%20property" title=" sufficient descent property"> sufficient descent property</a>, <a href="https://publications.waset.org/abstracts/search?q=line%20search" title=" line search"> line search</a> </p> <a href="https://publications.waset.org/abstracts/41727/global-convergence-of-a-modified-three-term-conjugate-gradient-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41727.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">375</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3252</span> A New Modification of Nonlinear Conjugate Gradient Coefficients with Global Convergence Properties</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Alhawarat">Ahmad Alhawarat</a>, <a href="https://publications.waset.org/abstracts/search?q=Mustafa%20Mamat"> Mustafa Mamat</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Rivaie"> Mohd Rivaie</a>, <a href="https://publications.waset.org/abstracts/search?q=Ismail%20Mohd"> Ismail Mohd</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Conjugate gradient method has been enormously used to solve large scale unconstrained optimization problems due to the number of iteration, memory, CPU time, and convergence property, in this paper we find a new class of nonlinear conjugate gradient coefficient with global convergence properties proved by exact line search. The numerical results for our new 尾K give a good result when it compared with well-known formulas. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=conjugate%20gradient%20method" title="conjugate gradient method">conjugate gradient method</a>, <a href="https://publications.waset.org/abstracts/search?q=conjugate%20gradient%20coefficient" title=" conjugate gradient coefficient"> conjugate gradient coefficient</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20convergence" title=" global convergence"> global convergence</a> </p> <a href="https://publications.waset.org/abstracts/1392/a-new-modification-of-nonlinear-conjugate-gradient-coefficients-with-global-convergence-properties" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1392.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">463</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">3251</span> Optimization of Personnel Selection Problems via Unconstrained Geometric Programming</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vildan%20Kistik">Vildan Kistik</a>, <a href="https://publications.waset.org/abstracts/search?q=Tuncay%20Can"> Tuncay Can</a> </p> <p class="card-text"><strong>Abstract:</strong></p> From a business perspective, cost and profit are two key factors for businesses. The intent of most businesses is to minimize the cost to maximize or equalize the profit, so as to provide the greatest benefit to itself. However, the physical system is very complicated because of technological constructions, rapid increase of competitive environments and similar factors. In such a system it is not easy to maximize profits or to minimize costs. Businesses must decide on the competence and competence of the personnel to be recruited, taking into consideration many criteria in selecting personnel. There are many criteria to determine the competence and competence of a staff member. Factors such as the level of education, experience, psychological and sociological position, and human relationships that exist in the field are just some of the important factors in selecting a staff for a firm. Personnel selection is a very important and costly process in terms of businesses in today's competitive market. Although there are many mathematical methods developed for the selection of personnel, unfortunately the use of these mathematical methods is rarely encountered in real life. In this study, unlike other methods, an exponential programming model was established based on the possibilities of failing in case the selected personnel was started to work. With the necessary transformations, the problem has been transformed into unconstrained Geometrical Programming problem and personnel selection problem is approached with geometric programming technique. Personnel selection scenarios for a classroom were established with the help of normal distribution and optimum solutions were obtained. In the most appropriate solutions, the personnel selection process for the classroom has been achieved with minimum cost. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=geometric%20programming" title="geometric programming">geometric programming</a>, <a href="https://publications.waset.org/abstracts/search?q=personnel%20selection" title=" personnel selection"> personnel selection</a>, <a href="https://publications.waset.org/abstracts/search?q=non-linear%20programming" title=" non-linear programming"> non-linear programming</a>, <a href="https://publications.waset.org/abstracts/search?q=operations%20research" title=" operations research"> operations research</a> </p> <a href="https://publications.waset.org/abstracts/70257/optimization-of-personnel-selection-problems-via-unconstrained-geometric-programming" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70257.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">269</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">3250</span> Fusion of Shape and Texture for Unconstrained Periocular Authentication</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=D.%20R.%20Ambika">D. R. Ambika</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20R.%20Radhika"> K. R. Radhika</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Seshachalam"> D. Seshachalam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Unconstrained authentication is an important component for personal automated systems and human-computer interfaces. Existing solutions mostly use face as the primary object of analysis. The performance of face-based systems is largely determined by the extent of deformation caused in the facial region and amount of useful information available in occluded face images. Periocular region is a useful portion of face with discriminative ability coupled with resistance to deformation. A reliable portion of periocular area is available for occluded images. The present work demonstrates that joint representation of periocular texture and periocular structure provides an effective expression and poses invariant representation. The proposed methodology provides an effective and compact description of periocular texture and shape. The method is tested over four benchmark datasets exhibiting varied acquisition conditions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=periocular%20authentication" title="periocular authentication">periocular authentication</a>, <a href="https://publications.waset.org/abstracts/search?q=Zernike%20moments" title=" Zernike moments"> Zernike moments</a>, <a href="https://publications.waset.org/abstracts/search?q=LBP%20variance" title=" LBP variance"> LBP variance</a>, <a href="https://publications.waset.org/abstracts/search?q=shape%20and%20texture%20fusion" title=" shape and texture fusion"> shape and texture fusion</a> </p> <a href="https://publications.waset.org/abstracts/68833/fusion-of-shape-and-texture-for-unconstrained-periocular-authentication" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68833.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">278</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">3249</span> Finite Element and Split Bregman Methods for Solving a Family of Optimal Control Problem with Partial Differential Equation Constraint</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahmoud%20Lot">Mahmoud Lot</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this article, we will discuss the solution of elliptic optimal control problem. First, by using the nite element method, we obtain the discrete form of the problem. The obtained discrete problem is actually a large scale constrained optimization problem. Solving this optimization problem with traditional methods is di铿僣ult and requires a lot of CPU time and memory. But split Bergman method converts the constrained problem to an unconstrained, and hence it saves time and memory requirement. Then we use the split Bregman method for solving this problem, and examples show the speed and accuracy of split Bregman methods for solving these types of problems. We also use the SQP method for solving the examples and compare with the split Bregman method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Split%20Bregman%20Method" title="Split Bregman Method">Split Bregman Method</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20control%20with%20elliptic%20partial%20differential%20equation%20constraint" title=" optimal control with elliptic partial differential equation constraint"> optimal control with elliptic partial differential equation constraint</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20element%20method" title=" finite element method"> finite element method</a> </p> <a href="https://publications.waset.org/abstracts/123437/finite-element-and-split-bregman-methods-for-solving-a-family-of-optimal-control-problem-with-partial-differential-equation-constraint" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/123437.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">152</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">3248</span> A Parallel Implementation of Artificial Bee Colony Algorithm within CUDA Architecture</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Selcuk%20Aslan">Selcuk Aslan</a>, <a href="https://publications.waset.org/abstracts/search?q=Dervis%20Karaboga"> Dervis Karaboga</a>, <a href="https://publications.waset.org/abstracts/search?q=Celal%20Ozturk"> Celal Ozturk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Artificial Bee Colony (ABC) algorithm is one of the most successful swarm intelligence based metaheuristics. It has been applied to a number of constrained or unconstrained numerical and combinatorial optimization problems. In this paper, we presented a parallelized version of ABC algorithm by adapting employed and onlooker bee phases to the Compute Unified Device Architecture (CUDA) platform which is a graphical processing unit (GPU) programming environment by NVIDIA. The execution speed and obtained results of the proposed approach and sequential version of ABC algorithm are compared on functions that are typically used as benchmarks for optimization algorithms. Tests on standard benchmark functions with different colony size and number of parameters showed that proposed parallelization approach for ABC algorithm decreases the execution time consumed by the employed and onlooker bee phases in total and achieved similar or better quality of the results compared to the standard sequential implementation of the ABC algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Artificial%20Bee%20Colony%20algorithm" title="Artificial Bee Colony algorithm">Artificial Bee Colony algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=GPU%20computing" title=" GPU computing"> GPU computing</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=parallelization" title=" parallelization"> parallelization</a> </p> <a href="https://publications.waset.org/abstracts/44876/a-parallel-implementation-of-artificial-bee-colony-algorithm-within-cuda-architecture" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44876.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">378</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">3247</span> A Weighted Approach to Unconstrained Iris Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yao-Hong%20Tsai">Yao-Hong Tsai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a weighted approach to unconstrained iris recognition. Nowadays, commercial systems are usually characterized by strong acquisition constraints based on the subject鈥檚 cooperation. However, it is not always achievable for real scenarios in our daily life. Researchers have been focused on reducing these constraints and maintaining the performance of the system by new techniques at the same time. With large variation in the environment, there are two main improvements to develop the proposed iris recognition system. For solving extremely uneven lighting condition, statistic based illumination normalization is first used on eye region to increase the accuracy of iris feature. The detection of the iris image is based on Adaboost algorithm. Secondly, the weighted approach is designed by Gaussian functions according to the distance to the center of the iris. Furthermore, local binary pattern (LBP) histogram is then applied to texture classification with the weight. Experiment showed that the proposed system provided users a more flexible and feasible way to interact with the verification system through iris recognition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=authentication" title="authentication">authentication</a>, <a href="https://publications.waset.org/abstracts/search?q=iris%20recognition" title=" iris recognition"> iris recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=adaboost" title=" adaboost"> adaboost</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20binary%20pattern" title=" local binary pattern"> local binary pattern</a> </p> <a href="https://publications.waset.org/abstracts/3876/a-weighted-approach-to-unconstrained-iris-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3876.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">224</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">3246</span> Efficient Feature Fusion for Noise Iris in Unconstrained Environment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yao-Hong%20Tsai">Yao-Hong Tsai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an efficient fusion algorithm for iris images to generate stable feature for recognition in unconstrained environment. Recently, iris recognition systems are focused on real scenarios in our daily life without the subject鈥檚 cooperation. Under large variation in the environment, the objective of this paper is to combine information from multiple images of the same iris. The result of image fusion is a new image which is more stable for further iris recognition than each original noise iris image. A wavelet-based approach for multi-resolution image fusion is applied in the fusion process. The detection of the iris image is based on Adaboost algorithm and then local binary pattern (LBP) histogram is then applied to texture classification with the weighting scheme. Experiment showed that the generated features from the proposed fusion algorithm can improve the performance for verification system through iris recognition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20fusion" title="image fusion">image fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=iris%20recognition" title=" iris recognition"> iris recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20binary%20pattern" title=" local binary pattern"> local binary pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet" title=" wavelet"> wavelet</a> </p> <a href="https://publications.waset.org/abstracts/17027/efficient-feature-fusion-for-noise-iris-in-unconstrained-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17027.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">367</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">3245</span> User Guidance for Effective Query Interpretation in Natural Language Interfaces to Ontologies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aliyu%20Isah%20Agaie">Aliyu Isah Agaie</a>, <a href="https://publications.waset.org/abstracts/search?q=Masrah%20Azrifah%20Azmi%20Murad"> Masrah Azrifah Azmi Murad</a>, <a href="https://publications.waset.org/abstracts/search?q=Nurfadhlina%20Mohd%20Sharef"> Nurfadhlina Mohd Sharef</a>, <a href="https://publications.waset.org/abstracts/search?q=Aida%20Mustapha"> Aida Mustapha </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Natural Language Interfaces typically support a restricted language and also have scopes and limitations that naïve users are unaware of, resulting in errors when the users attempt to retrieve information from ontologies. To overcome this challenge, an auto-suggest feature is introduced into the querying process where users are guided through the querying process using interactive query construction system. Guiding users to formulate their queries, while providing them with an unconstrained (or almost unconstrained) way to query the ontology results in better interpretation of the query and ultimately lead to an effective search. The approach described in this paper is unobtrusive and subtly guides the users, so that they have a choice of either selecting from the suggestion list or typing in full. The user is not coerced into accepting system suggestions and can express himself using fragments or full sentences. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=auto-suggest" title="auto-suggest">auto-suggest</a>, <a href="https://publications.waset.org/abstracts/search?q=expressiveness" title=" expressiveness"> expressiveness</a>, <a href="https://publications.waset.org/abstracts/search?q=habitability" title=" habitability"> habitability</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20interface" title=" natural language interface"> natural language interface</a>, <a href="https://publications.waset.org/abstracts/search?q=query%20interpretation" title=" query interpretation"> query interpretation</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20guidance" title=" user guidance"> user guidance</a> </p> <a href="https://publications.waset.org/abstracts/42815/user-guidance-for-effective-query-interpretation-in-natural-language-interfaces-to-ontologies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42815.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">474</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">3244</span> Curve Fitting by Cubic Bezier Curves Using Migrating Birds Optimization Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mitat%20Uysal">Mitat Uysal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A new met heuristic optimization algorithm called as Migrating Birds Optimization is used for curve fitting by rational cubic Bezier Curves. This requires solving a complicated multivariate optimization problem. In this study, the solution of this optimization problem is achieved by Migrating Birds Optimization algorithm that is a powerful met heuristic nature-inspired algorithm well appropriate for optimization. The results of this study show that the proposed method performs very well and being able to fit the data points to cubic Bezier Curves with a high degree of accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=algorithms" title="algorithms">algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=Bezier%20curves" title=" Bezier curves"> Bezier curves</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic%20optimization" title=" heuristic optimization"> heuristic optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=migrating%20birds%20optimization" title=" migrating birds optimization"> migrating birds optimization</a> </p> <a href="https://publications.waset.org/abstracts/78026/curve-fitting-by-cubic-bezier-curves-using-migrating-birds-optimization-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78026.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">3243</span> Steepest Descent Method with New Step Sizes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bib%20Paruhum%20Silalahi">Bib Paruhum Silalahi</a>, <a href="https://publications.waset.org/abstracts/search?q=Djihad%20Wungguli"> Djihad Wungguli</a>, <a href="https://publications.waset.org/abstracts/search?q=Sugi%20Guritman"> Sugi Guritman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Steepest descent method is a simple gradient method for optimization. This method has a slow convergence in heading to the optimal solution, which occurs because of the zigzag form of the steps. Barzilai and Borwein modified this algorithm so that it performs well for problems with large dimensions. Barzilai and Borwein method results have sparked a lot of research on the method of steepest descent, including alternate minimization gradient method and Yuan method. Inspired by previous works, we modified the step size of the steepest descent method. We then compare the modification results against the Barzilai and Borwein method, alternate minimization gradient method and Yuan method for quadratic function cases in terms of the iterations number and the running time. The average results indicate that the steepest descent method with the new step sizes provide good results for small dimensions and able to compete with the results of Barzilai and Borwein method and the alternate minimization gradient method for large dimensions. The new step sizes have faster convergence compared to the other methods, especially for cases with large dimensions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=steepest%20descent" title="steepest descent">steepest descent</a>, <a href="https://publications.waset.org/abstracts/search?q=line%20search" title=" line search"> line search</a>, <a href="https://publications.waset.org/abstracts/search?q=iteration" title=" iteration"> iteration</a>, <a href="https://publications.waset.org/abstracts/search?q=running%20time" title=" running time"> running time</a>, <a href="https://publications.waset.org/abstracts/search?q=unconstrained%20optimization" title=" unconstrained optimization"> unconstrained optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=convergence" title=" convergence"> convergence</a> </p> <a href="https://publications.waset.org/abstracts/29734/steepest-descent-method-with-new-step-sizes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29734.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">540</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">3242</span> A Mean鈥揤ariance鈥揝kewness Portfolio Optimization Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kostas%20Metaxiotis">Kostas Metaxiotis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Portfolio optimization is one of the most important topics in finance. This paper proposes a mean–variance–skewness (MVS) portfolio optimization model. Traditionally, the portfolio optimization problem is solved by using the mean–variance (MV) framework. In this study, we formulate the proposed model as a three-objective optimization problem, where the portfolio's expected return and skewness are maximized whereas the portfolio risk is minimized. For solving the proposed three-objective portfolio optimization model we apply an adapted version of the non-dominated sorting genetic algorithm (NSGAII). Finally, we use a real dataset from FTSE-100 for validating the proposed model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms" title="evolutionary algorithms">evolutionary algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization" title=" portfolio optimization"> portfolio optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=skewness" title=" skewness"> skewness</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20selection" title=" stock selection"> stock selection</a> </p> <a href="https://publications.waset.org/abstracts/102472/a-mean-variance-skewness-portfolio-optimization-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/102472.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">198</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">3241</span> Improved Whale Algorithm Based on Information Entropy and Its Application in Truss Structure Optimization Design</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Serges%20Mendomo%20%20Meye">Serges Mendomo Meye</a>, <a href="https://publications.waset.org/abstracts/search?q=Li%20Guowei"> Li Guowei</a>, <a href="https://publications.waset.org/abstracts/search?q=Shen%20Zhenzhong"> Shen Zhenzhong</a>, <a href="https://publications.waset.org/abstracts/search?q=Gan%20Lei"> Gan Lei</a>, <a href="https://publications.waset.org/abstracts/search?q=Xu%20Liqun"> Xu Liqun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Given the limitations of the original whale optimization algorithm (WAO) in local optimum and low convergence accuracy in truss structure optimization problems, based on the fundamental whale algorithm, an improved whale optimization algorithm (SWAO) based on information entropy is proposed. The information entropy itself is an uncertain measure. It is used to control the range of whale searches in path selection. It can overcome the shortcomings of the basic whale optimization algorithm (WAO) and can improve the global convergence speed of the algorithm. Taking truss structure as the optimization research object, the mathematical model of truss structure optimization is established; the cross-sectional area of truss is taken as the design variable; the objective function is the weight of truss structure; and an improved whale optimization algorithm (SWAO) is used for optimization design, which provides a new idea and means for its application in large and complex engineering structure optimization design. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=information%20entropy" title="information entropy">information entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20optimization" title=" structural optimization"> structural optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=truss%20structure" title=" truss structure"> truss structure</a>, <a href="https://publications.waset.org/abstracts/search?q=whale%20algorithm" title=" whale algorithm"> whale algorithm</a> </p> <a href="https://publications.waset.org/abstracts/139986/improved-whale-algorithm-based-on-information-entropy-and-its-application-in-truss-structure-optimization-design" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139986.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">249</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">3240</span> Improved Particle Swarm Optimization with Cellular Automata and Fuzzy Cellular Automata</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ramin%20Javadzadeh">Ramin Javadzadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The particle swarm optimization are Meta heuristic optimization method, which are used for clustering and pattern recognition applications are abundantly. These algorithms in multimodal optimization problems are more efficient than genetic algorithms. A major drawback in these algorithms is their slow convergence to global optimum and their weak stability can be considered in various running of these algorithms. In this paper, improved Particle swarm optimization is introduced for the first time to overcome its problems. The fuzzy cellular automata is used for improving the algorithm efficiently. The credibility of the proposed approach is evaluated by simulations, and it is shown that the proposed approach achieves better results can be achieved compared to the Particle swarm optimization algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cellular%20automata" title="cellular automata">cellular automata</a>, <a href="https://publications.waset.org/abstracts/search?q=cellular%20learning%20automata" title=" cellular learning automata"> cellular learning automata</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20search" title=" local search"> local search</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a> </p> <a href="https://publications.waset.org/abstracts/24739/improved-particle-swarm-optimization-with-cellular-automata-and-fuzzy-cellular-automata" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24739.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">606</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">3239</span> Non-Stationary Stochastic Optimization of an Oscillating Water Column</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mar%C3%ADa%20L.%20Jal%C3%B3n">Mar铆a L. Jal贸n</a>, <a href="https://publications.waset.org/abstracts/search?q=Feargal%20Brennan"> Feargal Brennan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A non-stationary stochastic optimization methodology is applied to an OWC (oscillating water column) to find the design that maximizes the wave energy extraction. Different temporal cycles are considered to represent the long-term variability of the wave climate at the site in the optimization problem. The results of the non-stationary stochastic optimization problem are compared against those obtained by a stationary stochastic optimization problem. The comparative analysis reveals that the proposed non-stationary optimization provides designs with a better fit to reality. However, the stationarity assumption can be adequate when looking at averaged system response. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=non-stationary%20stochastic%20optimization" title="non-stationary stochastic optimization">non-stationary stochastic optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=oscillating%20water" title=" oscillating water"> oscillating water</a>, <a href="https://publications.waset.org/abstracts/search?q=temporal%20variability" title=" temporal variability"> temporal variability</a>, <a href="https://publications.waset.org/abstracts/search?q=wave%20energy" title=" wave energy"> wave energy</a> </p> <a href="https://publications.waset.org/abstracts/75300/non-stationary-stochastic-optimization-of-an-oscillating-water-column" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75300.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">373</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">3238</span> Two-Stage Approach for Solving the Multi-Objective Optimization Problem on Combinatorial Configurations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Liudmyla%20Koliechkina">Liudmyla Koliechkina</a>, <a href="https://publications.waset.org/abstracts/search?q=Olena%20Dvirna"> Olena Dvirna</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The statement of the multi-objective optimization problem on combinatorial configurations is formulated, and the approach to its solution is proposed. The problem is of interest as a combinatorial optimization one with many criteria, which is a model of many applied tasks. The approach to solving the multi-objective optimization problem on combinatorial configurations consists of two stages; the first is the reduction of the multi-objective problem to the single criterion based on existing multi-objective optimization methods, the second stage solves the directly replaced single criterion combinatorial optimization problem by the horizontal combinatorial method. This approach provides the optimal solution to the multi-objective optimization problem on combinatorial configurations, taking into account additional restrictions for a finite number of steps. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=discrete%20set" title="discrete set">discrete set</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20combinatorial%20optimization" title=" linear combinatorial optimization"> linear combinatorial optimization</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=Pareto%20solutions" title=" Pareto solutions"> Pareto solutions</a>, <a href="https://publications.waset.org/abstracts/search?q=partial%20permutation%20set" title=" partial permutation set"> partial permutation set</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20graph" title=" structural graph"> structural graph</a> </p> <a href="https://publications.waset.org/abstracts/133824/two-stage-approach-for-solving-the-multi-objective-optimization-problem-on-combinatorial-configurations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133824.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">167</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">3237</span> Model of Optimal Centroids Approach for Multivariate Data Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pham%20Van%20Nha">Pham Van Nha</a>, <a href="https://publications.waset.org/abstracts/search?q=Le%20Cam%20Binh"> Le Cam Binh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm. PSO was inspired by the natural behavior of birds and fish in migration and foraging for food. PSO is considered as a multidisciplinary optimization model that can be applied in various optimization problems. PSO’s ideas are simple and easy to understand but PSO is only applied in simple model problems. We think that in order to expand the applicability of PSO in complex problems, PSO should be described more explicitly in the form of a mathematical model. In this paper, we represent PSO in a mathematical model and apply in the multivariate data classification. First, PSOs general mathematical model (MPSO) is analyzed as a universal optimization model. Then, Model of Optimal Centroids (MOC) is proposed for the multivariate data classification. Experiments were conducted on some benchmark data sets to prove the effectiveness of MOC compared with several proposed schemes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analysis%20of%20optimization" title="analysis of optimization">analysis of optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence%20based%20optimization" title=" artificial intelligence based optimization"> artificial intelligence based optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20for%20learning%20and%20data%20analysis" title=" optimization for learning and data analysis"> optimization for learning and data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20optimization" title=" global optimization"> global optimization</a> </p> <a href="https://publications.waset.org/abstracts/126058/model-of-optimal-centroids-approach-for-multivariate-data-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/126058.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">208</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">3236</span> Cuckoo Search (CS) Optimization Algorithm for Solving Constrained Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sait%20Ali%20Uymaz">Sait Ali Uymaz</a>, <a href="https://publications.waset.org/abstracts/search?q=G%C3%BClay%20Tezel"> G眉lay Tezel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the comparison results on the performance of the Cuckoo Search (CS) algorithm for constrained optimization problems. For constraint handling, CS algorithm uses penalty method. CS algorithm is tested on thirteen well-known test problems and the results obtained are compared to Particle Swarm Optimization (PSO) algorithm. Mean, best, median and worst values were employed for the analyses of performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cuckoo%20search" title="cuckoo search">cuckoo search</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=constrained%20optimization%20problems" title=" constrained optimization problems"> constrained optimization problems</a>, <a href="https://publications.waset.org/abstracts/search?q=penalty%20method" title=" penalty method"> penalty method</a> </p> <a href="https://publications.waset.org/abstracts/13991/cuckoo-search-cs-optimization-algorithm-for-solving-constrained-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13991.pdf" target="_blank" class="btn btn-primary 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