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and metaheuristics algorithms</title> <meta name="description" content="Search results for: heuristics and metaheuristics algorithms"> <meta name="keywords" content="heuristics and metaheuristics algorithms"> <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 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<form method="get" 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="heuristics and metaheuristics algorithms"> <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> 2113</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: heuristics and metaheuristics algorithms</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2113</span> Automated Test Data Generation For some types of Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hitesh%20Tahbildar">Hitesh Tahbildar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The cost of test data generation for a program is computationally very high. In general case, no algorithm to generate test data for all types of algorithms has been found. The cost of generating test data for different types of algorithm is different. Till date, people are emphasizing the need to generate test data for different types of programming constructs rather than different types of algorithms. The test data generation methods have been implemented to find heuristics for different types of algorithms. Some algorithms that includes divide and conquer, backtracking, greedy approach, dynamic programming to find the minimum cost of test data generation have been tested. Our experimental results say that some of these types of algorithm can be used as a necessary condition for selecting heuristics and programming constructs are sufficient condition for selecting our heuristics. Finally we recommend the different heuristics for test data generation to be selected for different types of algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ongest%20path" title="ongest path">ongest path</a>, <a href="https://publications.waset.org/abstracts/search?q=saturation%20point" title=" saturation point"> saturation point</a>, <a href="https://publications.waset.org/abstracts/search?q=lmax" title=" lmax"> lmax</a>, <a href="https://publications.waset.org/abstracts/search?q=kL" title=" kL"> kL</a>, <a href="https://publications.waset.org/abstracts/search?q=kS" title=" kS"> kS</a> </p> <a href="https://publications.waset.org/abstracts/2406/automated-test-data-generation-for-some-types-of-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2406.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">405</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">2112</span> Association Rules Mining Task Using Metaheuristics: Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abir%20Derouiche">Abir Derouiche</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdesslem%20Layeb"> Abdesslem Layeb </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Association Rule Mining (ARM) is one of the most popular data mining tasks and it is widely used in various areas. The search for association rules is an NP-complete problem that is why metaheuristics have been widely used to solve it. The present paper presents the ARM as an optimization problem and surveys the proposed approaches in the literature based on metaheuristics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Optimization" title="Optimization">Optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=Metaheuristics" title=" Metaheuristics"> Metaheuristics</a>, <a href="https://publications.waset.org/abstracts/search?q=Data%20Mining" title=" Data Mining"> Data Mining</a>, <a href="https://publications.waset.org/abstracts/search?q=Association%20rules%20Mining" title=" Association rules Mining"> Association rules Mining</a> </p> <a href="https://publications.waset.org/abstracts/120254/association-rules-mining-task-using-metaheuristics-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/120254.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">159</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">2111</span> SMART: Solution Methods with Ants Running by Types</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nicolas%20Zufferey">Nicolas Zufferey</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ant algorithms are well-known metaheuristics which have been widely used since two decades. In most of the literature, an ant is a constructive heuristic able to build a solution from scratch. However, other types of ant algorithms have recently emerged: the discussion is thus not limited by the common framework of the constructive ant algorithms. Generally, at each generation of an ant algorithm, each ant builds a solution step by step by adding an element to it. Each choice is based on the greedy force (also called the visibility, the short term profit or the heuristic information) and the trail system (central memory which collects historical information of the search process). Usually, all the ants of the population have the same characteristics and behaviors. In contrast in this paper, a new type of ant metaheuristic is proposed, namely SMART (for Solution Methods with Ants Running by Types). It relies on the use of different population of ants, where each population has its own personality. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ant%20algorithms" title="ant algorithms">ant algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20procedures" title=" evolutionary procedures"> evolutionary procedures</a>, <a href="https://publications.waset.org/abstracts/search?q=metaheuristics" title=" metaheuristics"> metaheuristics</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=population-based%20methods" title=" population-based methods"> population-based methods</a> </p> <a href="https://publications.waset.org/abstracts/36375/smart-solution-methods-with-ants-running-by-types" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36375.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">365</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">2110</span> Reducing the Computational Overhead of Metaheuristics Parameterization with Exploratory Landscape Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Iannick%20Gagnon">Iannick Gagnon</a>, <a href="https://publications.waset.org/abstracts/search?q=Alain%20April"> Alain April</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The performance of a metaheuristic on a given problem class depends on the class itself and the choice of parameters. Parameter tuning is the most time-consuming phase of the optimization process after the main calculations and it often nullifies the speed advantage of metaheuristics over traditional optimization algorithms. Several off-the-shelf parameter tuning algorithms are available, but when the objective function is expensive to evaluate, these can be prohibitively expensive to use. This paper presents a surrogate-like method for finding adequate parameters using fitness landscape analysis on simple benchmark functions and real-world objective functions. The result is a simple compound similarity metric based on the empirical correlation coefficient and a measure of convexity. It is then used to find the best benchmark functions to serve as surrogates. The near-optimal parameter set is then found using fractional factorial design. The real-world problem of NACA airfoil lift coefficient maximization is used as a preliminary proof of concept. The overall aim of this research is to reduce the computational overhead of metaheuristics parameterization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=metaheuristics" title="metaheuristics">metaheuristics</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20optimization" title=" stochastic optimization"> stochastic optimization</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=exploratory%20landscape%20analysis" title=" exploratory landscape analysis"> exploratory landscape analysis</a> </p> <a href="https://publications.waset.org/abstracts/120306/reducing-the-computational-overhead-of-metaheuristics-parameterization-with-exploratory-landscape-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/120306.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">153</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">2109</span> An Optimal Steganalysis Based Approach for Embedding Information in Image Cover Media with Security</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahlem%20Fatnassi">Ahlem Fatnassi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamza%20Gharsellaoui"> Hamza Gharsellaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=Sadok%20Bouamama"> Sadok Bouamama</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with the study of interest in the fields of Steganography and Steganalysis. Steganography involves hiding information in a cover media to obtain the stego media in such a way that the cover media is perceived not to have any embedded message for its unintended recipients. Steganalysis is the mechanism of detecting the presence of hidden information in the stego media and it can lead to the prevention of disastrous security incidents. In this paper, we provide a critical review of the steganalysis algorithms available to analyze the characteristics of an image stego media against the corresponding cover media and understand the process of embedding the information and its detection. We anticipate that this paper can also give a clear picture of the current trends in steganography so that we can develop and improvise appropriate steganalysis algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimization" title="optimization">optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristics%20and%20metaheuristics%20algorithms" title=" heuristics and metaheuristics algorithms"> heuristics and metaheuristics algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=embedded%20systems" title=" embedded systems"> embedded systems</a>, <a href="https://publications.waset.org/abstracts/search?q=low-power%20consumption" title=" low-power consumption"> low-power consumption</a>, <a href="https://publications.waset.org/abstracts/search?q=steganalysis%20heuristic%20approach" title=" steganalysis heuristic approach"> steganalysis heuristic approach</a> </p> <a href="https://publications.waset.org/abstracts/44034/an-optimal-steganalysis-based-approach-for-embedding-information-in-image-cover-media-with-security" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44034.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">292</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">2108</span> A Hybrid Distributed Algorithm for Solving Job Shop Scheduling Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aydin%20Teymourifar">Aydin Teymourifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Gurkan%20Ozturk"> Gurkan Ozturk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a distributed hybrid algorithm is proposed for solving the job shop scheduling problem. The suggested method executes different artificial neural networks, heuristics and meta-heuristics simultaneously on more than one machine. The neural networks are used to control the constraints of the problem while the meta-heuristics search the global space and the heuristics are used to prevent the premature convergence. To attain an efficient distributed intelligent method for solving big and distributed job shop scheduling problems, Apache Spark and Hadoop frameworks are used. In the algorithm implementation and design steps, new approaches are applied. Comparison between the proposed algorithm and other efficient algorithms from the literature shows its efficiency, which is able to solve large size problems in short time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20algorithms" title="distributed algorithms">distributed algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=Apache%20Spark" title=" Apache Spark"> Apache Spark</a>, <a href="https://publications.waset.org/abstracts/search?q=Hadoop" title=" Hadoop"> Hadoop</a>, <a href="https://publications.waset.org/abstracts/search?q=job%20shop%20scheduling" title=" job shop scheduling"> job shop scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a> </p> <a href="https://publications.waset.org/abstracts/72320/a-hybrid-distributed-algorithm-for-solving-job-shop-scheduling-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72320.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">387</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2107</span> On the Application of Heuristics of the Traveling Salesman Problem for the Task of Restoring the DNA Matrix</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Boris%20Melnikov">Boris Melnikov</a>, <a href="https://publications.waset.org/abstracts/search?q=Dmitrii%20Chaikovskii"> Dmitrii Chaikovskii</a>, <a href="https://publications.waset.org/abstracts/search?q=Elena%20Melnikova"> Elena Melnikova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The traveling salesman problem (TSP) is a well-known optimization problem that seeks to find the shortest possible route that visits a set of points and returns to the starting point. In this paper, we apply some heuristics of the TSP for the task of restoring the DNA matrix. This restoration problem is often considered in biocybernetics. For it, we must recover the matrix of distances between DNA sequences if not all the elements of the matrix under consideration are known at the input. We consider the possibility of using this method in the testing of distance calculation algorithms between a pair of DNAs to restore the partially filled matrix. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimization%20problems" title="optimization problems">optimization problems</a>, <a href="https://publications.waset.org/abstracts/search?q=DNA%20matrix" title=" DNA matrix"> DNA matrix</a>, <a href="https://publications.waset.org/abstracts/search?q=partially%20filled%20matrix" title=" partially filled matrix"> partially filled matrix</a>, <a href="https://publications.waset.org/abstracts/search?q=traveling%20salesman%20problem" title=" traveling salesman problem"> traveling salesman problem</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic%20algorithms" title=" heuristic algorithms"> heuristic algorithms</a> </p> <a href="https://publications.waset.org/abstracts/172868/on-the-application-of-heuristics-of-the-traveling-salesman-problem-for-the-task-of-restoring-the-dna-matrix" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/172868.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">150</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">2106</span> An Ensemble Learning Method for Applying Particle Swarm Optimization Algorithms to Systems Engineering Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ken%20Hampshire">Ken Hampshire</a>, <a href="https://publications.waset.org/abstracts/search?q=Thomas%20Mazzuchi"> Thomas Mazzuchi</a>, <a href="https://publications.waset.org/abstracts/search?q=Shahram%20Sarkani"> Shahram Sarkani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As a subset of metaheuristics, nature-inspired optimization algorithms such as particle swarm optimization (PSO) have shown promise both in solving intractable problems and in their extensibility to novel problem formulations due to their general approach requiring few assumptions. Unfortunately, single instantiations of algorithms require detailed tuning of parameters and cannot be proven to be best suited to a particular illustrative problem on account of the “no free lunch” (NFL) theorem. Using these algorithms in real-world problems requires exquisite knowledge of the many techniques and is not conducive to reconciling the various approaches to given classes of problems. This research aims to present a unified view of PSO-based approaches from the perspective of relevant systems engineering problems, with the express purpose of then eliciting the best solution for any problem formulation in an ensemble learning bucket of models approach. The central hypothesis of the research is that extending the PSO algorithms found in the literature to real-world optimization problems requires a general ensemble-based method for all problem formulations but a specific implementation and solution for any instance. The main results are a problem-based literature survey and a general method to find more globally optimal solutions for any systems engineering optimization problem. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title="particle swarm optimization">particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=nature-inspired%20optimization" title=" nature-inspired optimization"> nature-inspired optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=metaheuristics" title=" metaheuristics"> metaheuristics</a>, <a href="https://publications.waset.org/abstracts/search?q=systems%20engineering" title=" systems engineering"> systems engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=ensemble%20learning" title=" ensemble learning"> ensemble learning</a> </p> <a href="https://publications.waset.org/abstracts/167097/an-ensemble-learning-method-for-applying-particle-swarm-optimization-algorithms-to-systems-engineering-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167097.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">99</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">2105</span> Heuristics for Optimizing Power Consumption in the Smart Grid</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zaid%20Jamal%20Saeed%20Almahmoud">Zaid Jamal Saeed Almahmoud</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Our increasing reliance on electricity, with inefficient consumption trends, has resulted in several economical and environmental threats. These threats include wasting billions of dollars, draining limited resources, and elevating the impact of climate change. As a solution, the smart grid is emerging as the future power grid, with smart techniques to optimize power consumption and electricity generation. Minimizing the peak power consumption under a fixed delay requirement is a significant problem in the smart grid. In addition, matching demand to supply is a key requirement for the success of the future electricity. In this work, we consider the problem of minimizing the peak demand under appliances constraints by scheduling power jobs with uniform release dates and deadlines. As the problem is known to be NP-Hard, we propose two versions of a heuristic algorithm for solving this problem. Our theoretical analysis and experimental results show that our proposed heuristics outperform existing methods by providing a better approximation to the optimal solution. In addition, we consider dynamic pricing methods to minimize the peak load and match demand to supply in the smart grid. Our contribution is the proposal of generic, as well as customized pricing heuristics to minimize the peak demand and match demand with supply. In addition, we propose optimal pricing algorithms that can be used when the maximum deadline period of the power jobs is relatively small. Finally, we provide theoretical analysis and conduct several experiments to evaluate the performance of the proposed algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=heuristics" title="heuristics">heuristics</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=smart%20grid" title=" smart grid"> smart grid</a>, <a href="https://publications.waset.org/abstracts/search?q=peak%20demand" title=" peak demand"> peak demand</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20supply" title=" power supply"> power supply</a> </p> <a href="https://publications.waset.org/abstracts/158813/heuristics-for-optimizing-power-consumption-in-the-smart-grid" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158813.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">88</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">2104</span> Enunciation on Complexities of Selected Tree Searching Algorithms </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Parag%20Bhalchandra">Parag Bhalchandra</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20D.%20Khamitkar"> S. D. Khamitkar </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Searching trees is a most interesting application of Artificial Intelligence. Over the period of time, many innovative methods have been evolved to better search trees with respect to computational complexities. Tree searches are difficult to understand due to the exponential growth of possibilities when increasing the number of nodes or levels in the tree. Usually it is understood when we traverse down in the tree, traverse down to greater depth, in the search of a solution or a goal. However, this does not happen in reality as explicit enumeration is not a very efficient method and there are many algorithmic speedups that will find the optimal solution without the burden of evaluating all possible trees. It was a common question before all researchers where they often wonder what algorithms will yield the best and fastest result The intention of this paper is two folds, one to review selected tree search algorithms and search strategies that can be applied to a problem space and the second objective is to stimulate to implement recent developments in the complexity behavior of search strategies. The algorithms discussed here apply in general to both brute force and heuristic searches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=trees%20search" title="trees search">trees search</a>, <a href="https://publications.waset.org/abstracts/search?q=asymptotic%20complexity" title=" asymptotic complexity"> asymptotic complexity</a>, <a href="https://publications.waset.org/abstracts/search?q=brute%20force" title=" brute force"> brute force</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristics%20algorithms" title=" heuristics algorithms"> heuristics algorithms</a> </p> <a href="https://publications.waset.org/abstracts/13407/enunciation-on-complexities-of-selected-tree-searching-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13407.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">304</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">2103</span> A Heuristic for the Integrated Production and Distribution Scheduling Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Christian%20Meinecke">Christian Meinecke</a>, <a href="https://publications.waset.org/abstracts/search?q=Bernd%20Scholz-Reiter"> Bernd Scholz-Reiter</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The integrated problem of production and distribution scheduling is relevant in many industrial applications. Thus, many heuristics to solve this integrated problem have been developed in the last decade. Most of these heuristics use a sequential working principal or a single decomposition and integration approach to separate and solve sub-problems. A heuristic using a multi-step decomposition and integration approach is presented in this paper and evaluated in a case study. The result show significant improved results compared with sequential scheduling heuristics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=production%20and%20outbound%20distribution" title="production and outbound distribution">production and outbound distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=integrated%20planning" title=" integrated planning"> integrated planning</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic" title=" heuristic"> heuristic</a>, <a href="https://publications.waset.org/abstracts/search?q=decomposition" title=" decomposition"> decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=integration" title=" integration"> integration</a> </p> <a href="https://publications.waset.org/abstracts/4364/a-heuristic-for-the-integrated-production-and-distribution-scheduling-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4364.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">429</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">2102</span> Two Efficient Heuristic Algorithms for the Integrated Production Planning and Warehouse Layout Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Pourmohammadi%20Fallah">Mohammad Pourmohammadi Fallah</a>, <a href="https://publications.waset.org/abstracts/search?q=Maziar%20Salahi"> Maziar Salahi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the literature, a mixed-integer linear programming model for the integrated production planning and warehouse layout problem is proposed. To solve the model, the authors proposed a Lagrangian relax-and-fix heuristic that takes a significant amount of time to stop with gaps above 5$\%$ for large-scale instances. Here, we present two heuristic algorithms to solve the problem. In the first one, we use a greedy approach by allocating warehouse locations with less reservation costs and also less transportation costs from the production area to locations and from locations to the output point to items with higher demands. Then a smaller model is solved. In the second heuristic, first, we sort items in descending order according to the fraction of the sum of the demands for that item in the time horizon plus the maximum demand for that item in the time horizon and the sum of all its demands in the time horizon. Then we categorize the sorted items into groups of 3, 4, or 5 and solve a small-scale optimization problem for each group, hoping to improve the solution of the first heuristic. Our preliminary numerical results show the effectiveness of the proposed heuristics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=capacitated%20lot-sizing" title="capacitated lot-sizing">capacitated lot-sizing</a>, <a href="https://publications.waset.org/abstracts/search?q=warehouse%20layout" title=" warehouse layout"> warehouse layout</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed-integer%20linear%20programming" title=" mixed-integer linear programming"> mixed-integer linear programming</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristics%20algorithm" title=" heuristics algorithm"> heuristics algorithm</a> </p> <a href="https://publications.waset.org/abstracts/154415/two-efficient-heuristic-algorithms-for-the-integrated-production-planning-and-warehouse-layout-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/154415.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">196</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">2101</span> Efficient Reconstruction of DNA Distance Matrices Using an Inverse Problem Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Boris%20Melnikov">Boris Melnikov</a>, <a href="https://publications.waset.org/abstracts/search?q=Ye%20Zhang"> Ye Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Dmitrii%20Chaikovskii"> Dmitrii Chaikovskii</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We continue to consider one of the cybernetic methods in computational biology related to the study of DNA chains. Namely, we are considering the problem of reconstructing the not fully filled distance matrix of DNA chains. When applied in a programming context, it is revealed that with a modern computer of average capabilities, creating even a small-sized distance matrix for mitochondrial DNA sequences is quite time-consuming with standard algorithms. As the size of the matrix grows larger, the computational effort required increases significantly, potentially spanning several weeks to months of non-stop computer processing. Hence, calculating the distance matrix on conventional computers is hardly feasible, and supercomputers are usually not available. Therefore, we started publishing our variants of the algorithms for calculating the distance between two DNA chains; then, we published algorithms for restoring partially filled matrices, i.e., the inverse problem of matrix processing. In this paper, we propose an algorithm for restoring the distance matrix for DNA chains, and the primary focus is on enhancing the algorithms that shape the greedy function within the branches and boundaries method framework. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DNA%20chains" title="DNA chains">DNA chains</a>, <a href="https://publications.waset.org/abstracts/search?q=distance%20matrix" title=" distance matrix"> distance matrix</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20problem" title=" optimization problem"> optimization problem</a>, <a href="https://publications.waset.org/abstracts/search?q=restoring%20algorithm" title=" restoring algorithm"> restoring algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=greedy%20algorithm" title=" greedy algorithm"> greedy algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristics" title=" heuristics"> heuristics</a> </p> <a href="https://publications.waset.org/abstracts/167026/efficient-reconstruction-of-dna-distance-matrices-using-an-inverse-problem-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167026.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">118</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">2100</span> Algorithms for Run-Time Task Mapping in NoC-Based Heterogeneous MPSoCs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20K.%20Benhaoua">M. K. Benhaoua</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20K.%20Singh"> A. K. Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20E.%20Benyamina"> A. E. Benyamina</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Boulet"> P. Boulet</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mapping parallelized tasks of applications onto these MPSoCs can be done either at design time (static) or at run-time (dynamic). Static mapping strategies find the best placement of tasks at design-time, and hence, these are not suitable for dynamic workload and seem incapable of runtime resource management. The number of tasks or applications executing in MPSoC platform can exceed the available resources, requiring efficient run-time mapping strategies to meet these constraints. This paper describes a new Spiral Dynamic Task Mapping heuristic for mapping applications onto NoC-based Heterogeneous MPSoC. This heuristic is based on packing strategy and routing Algorithm proposed also in this paper. Heuristic try to map the tasks of an application in a clustering region to reduce the communication overhead between the communicating tasks. The heuristic proposed in this paper attempts to map the tasks of an application that are most related to each other in a spiral manner and to find the best possible path load that minimizes the communication overhead. In this context, we have realized a simulation environment for experimental evaluations to map applications with varying number of tasks onto an 8x8 NoC-based Heterogeneous MPSoCs platform, we demonstrate that the new mapping heuristics with the new modified dijkstra routing algorithm proposed are capable of reducing the total execution time and energy consumption of applications when compared to state-of-the-art run-time mapping heuristics reported in the literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multiprocessor%20system%20on%20chip" title="multiprocessor system on chip">multiprocessor system on chip</a>, <a href="https://publications.waset.org/abstracts/search?q=MPSoC" title=" MPSoC"> MPSoC</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20on%20chip" title=" network on chip"> network on chip</a>, <a href="https://publications.waset.org/abstracts/search?q=NoC" title=" NoC"> NoC</a>, <a href="https://publications.waset.org/abstracts/search?q=heterogeneous%20architectures" title=" heterogeneous architectures"> heterogeneous architectures</a>, <a href="https://publications.waset.org/abstracts/search?q=run-time%20mapping%20heuristics" title=" run-time mapping heuristics"> run-time mapping heuristics</a>, <a href="https://publications.waset.org/abstracts/search?q=routing%20algorithm" title=" routing algorithm "> routing algorithm </a> </p> <a href="https://publications.waset.org/abstracts/24295/algorithms-for-run-time-task-mapping-in-noc-based-heterogeneous-mpsocs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24295.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">489</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">2099</span> A Coordinate-Based Heuristic Route Search Algorithm for Delivery Truck Routing Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Tarek">Ahmed Tarek</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Alveed"> Ahmed Alveed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Vehicle routing problem is a well-known re-search avenue in computing. Modern vehicle routing is more focused with the GPS-based coordinate system, as the state-of-the-art vehicle, and trucking systems are equipped with digital navigation. In this paper, a new two dimensional coordinate-based algorithm for addressing the vehicle routing problem for a supply chain network is proposed and explored, and the algorithm is compared with other available, and recently devised heuristics. For the algorithms discussed, which includes the pro-posed coordinate-based search heuristic as well, the advantages and the disadvantages associated with the heuristics are explored. The proposed algorithm is studied from the stand point of a small supermarket chain delivery network that supplies to its stores in four different states around the East Coast area, and is trying to optimize its trucking delivery cost. Minimizing the delivery cost for the supply network of a supermarket chain is important to ensure its business success. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=coordinate-based%20optimal%20routing" title="coordinate-based optimal routing">coordinate-based optimal routing</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamiltonian%20Circuit" title=" Hamiltonian Circuit"> Hamiltonian Circuit</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic%20algorithm" title=" heuristic algorithm"> heuristic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=traveling%20salesman%20problem" title=" traveling salesman problem"> traveling salesman problem</a>, <a href="https://publications.waset.org/abstracts/search?q=vehicle%20routing%20problem" title=" vehicle routing problem"> vehicle routing problem</a> </p> <a href="https://publications.waset.org/abstracts/136218/a-coordinate-based-heuristic-route-search-algorithm-for-delivery-truck-routing-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/136218.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">147</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2098</span> A Hybrid Pareto-Based Swarm Optimization Algorithm for the Multi-Objective Flexible Job Shop Scheduling Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aydin%20Teymourifar">Aydin Teymourifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Gurkan%20Ozturk"> Gurkan Ozturk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a new hybrid particle swarm optimization algorithm is proposed for the multi-objective flexible job shop scheduling problem that is very important and hard combinatorial problem. The Pareto approach is used for solving the multi-objective problem. Several new local search heuristics are integrated into an algorithm based on the critical block concept to enhance the performance of the algorithm. The algorithm is compared with the recently published multi-objective algorithms based on benchmarks selected from the literature. Several metrics are used for quantifying performance and comparison of the achieved solutions. The algorithms are also compared based on the Weighting summation of objectives approach. The proposed algorithm can find the Pareto solutions more efficiently than the compared algorithms in less computational time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=swarm-based%20optimization" title="swarm-based optimization">swarm-based optimization</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=Pareto%20optimality" title=" Pareto optimality"> Pareto optimality</a>, <a href="https://publications.waset.org/abstracts/search?q=flexible%20job%20shop%20scheduling" title=" flexible job shop scheduling"> flexible job shop scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a> </p> <a href="https://publications.waset.org/abstracts/72144/a-hybrid-pareto-based-swarm-optimization-algorithm-for-the-multi-objective-flexible-job-shop-scheduling-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72144.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">2097</span> Approximation Algorithms for Peak-Demand Reduction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zaid%20Jamal%20Saeed%20Almahmoud">Zaid Jamal Saeed Almahmoud</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Smart grid is emerging as the future power grid, with smart techniques to optimize power consumption and electricity generation. Minimizing peak power consumption under a fixed delay requirement is a significant problem in the smart grid.For this problem, all appliances must be scheduled within a given finite time duration. We consider the problem of minimizing the peak demand under appliances constraints by scheduling power jobs with uniform release dates and deadlines. As the problem is known to be NP-hard, we analyze the performance of a version of the natural greedy heuristic for solving this problem. Our theoretical analysis and experimental results show that the proposed heuristic outperforms existing methods by providing a better approximation to the optimal solution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=peak%20demand%20scheduling" title="peak demand scheduling">peak demand scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=approximation%20algorithms" title=" approximation algorithms"> approximation algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=smart%20grid" title=" smart grid"> smart grid</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristics" title=" heuristics"> heuristics</a> </p> <a href="https://publications.waset.org/abstracts/157964/approximation-algorithms-for-peak-demand-reduction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157964.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">94</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">2096</span> An Approximation Technique to Automate Tron</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20Jayashree">P. Jayashree</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Rajkumar"> S. Rajkumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the trend of virtual and augmented reality environments booming to provide a life like experience, gaming is a major tool in supporting such learning environments. In this work, a variant of Voronoi heuristics, employing supervised learning for the TRON game is proposed. The paper discusses the features that would be really useful when a machine learning bot is to be used as an opponent against a human player. Various game scenarios, nature of the bot and the experimental results are provided for the proposed variant to prove that the approach is better than those that are currently followed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20Intelligence" title="artificial Intelligence">artificial Intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=automation" title=" automation"> automation</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=TRON%20game" title=" TRON game"> TRON game</a>, <a href="https://publications.waset.org/abstracts/search?q=Voronoi%20heuristics" title=" Voronoi heuristics"> Voronoi heuristics</a> </p> <a href="https://publications.waset.org/abstracts/60483/an-approximation-technique-to-automate-tron" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60483.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">467</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">2095</span> Optimization of Robot Motion Planning Using Biogeography Based Optimization (Bbo)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jaber%20Nikpouri">Jaber Nikpouri</a>, <a href="https://publications.waset.org/abstracts/search?q=Arsalan%20Amralizadeh"> Arsalan Amralizadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In robotics manipulators, the trajectory should be optimum, thus the torque of the robot can be minimized in order to save power. This paper includes an optimal path planning scheme for a robotic manipulator. Recently, techniques based on metaheuristics of natural computing, mainly evolutionary algorithms (EA), have been successfully applied to a large number of robotic applications. In this paper, the improved BBO algorithm is used to minimize the objective function in the presence of different obstacles. The simulation represents that the proposed optimal path planning method has satisfactory performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biogeography-based%20optimization" title="biogeography-based optimization">biogeography-based optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=path%20planning" title=" path planning"> path planning</a>, <a href="https://publications.waset.org/abstracts/search?q=obstacle%20detection" title=" obstacle detection"> obstacle detection</a>, <a href="https://publications.waset.org/abstracts/search?q=robotic%20manipulator" title=" robotic manipulator"> robotic manipulator</a> </p> <a href="https://publications.waset.org/abstracts/55588/optimization-of-robot-motion-planning-using-biogeography-based-optimization-bbo" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55588.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">2094</span> Heuristic Search Algorithm (HSA) for Enhancing the Lifetime of Wireless Sensor Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tripatjot%20S.%20Panag">Tripatjot S. Panag</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20S.%20Dhillon"> J. S. Dhillon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The lifetime of a wireless sensor network can be effectively increased by using scheduling operations. Once the sensors are randomly deployed, the task at hand is to find the largest number of disjoint sets of sensors such that every sensor set provides complete coverage of the target area. At any instant, only one of these disjoint sets is switched on, while all other are switched off. This paper proposes a heuristic search method to find the maximum number of disjoint sets that completely cover the region. A population of randomly initialized members is made to explore the solution space. A set of heuristics has been applied to guide the members to a possible solution in their neighborhood. The heuristics escalate the convergence of the algorithm. The best solution explored by the population is recorded and is continuously updated. The proposed algorithm has been tested for applications which require sensing of multiple target points, referred to as point coverage applications. Results show that the proposed algorithm outclasses the existing algorithms. It always finds the optimum solution, and that too by making fewer number of fitness function evaluations than the existing approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=coverage" title="coverage">coverage</a>, <a href="https://publications.waset.org/abstracts/search?q=disjoint%20sets" title=" disjoint sets"> disjoint sets</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic" title=" heuristic"> heuristic</a>, <a href="https://publications.waset.org/abstracts/search?q=lifetime" title=" lifetime"> lifetime</a>, <a href="https://publications.waset.org/abstracts/search?q=scheduling" title=" scheduling"> scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=Wireless%20sensor%20networks" title=" Wireless sensor networks"> Wireless sensor networks</a>, <a href="https://publications.waset.org/abstracts/search?q=WSN" title=" WSN"> WSN</a> </p> <a href="https://publications.waset.org/abstracts/34165/heuristic-search-algorithm-hsa-for-enhancing-the-lifetime-of-wireless-sensor-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34165.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">2093</span> A Genetic Algorithm Based Permutation and Non-Permutation Scheduling Heuristics for Finite Capacity Material Requirement Planning Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Watchara%20Songserm">Watchara Songserm</a>, <a href="https://publications.waset.org/abstracts/search?q=Teeradej%20Wuttipornpun"> Teeradej Wuttipornpun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a genetic algorithm based permutation and non-permutation scheduling heuristics (GAPNP) to solve a multi-stage finite capacity material requirement planning (FCMRP) problem in automotive assembly flow shop with unrelated parallel machines. In the algorithm, the sequences of orders are iteratively improved by the GA characteristics, whereas the required operations are scheduled based on the presented permutation and non-permutation heuristics. Finally, a linear programming is applied to minimize the total cost. The presented GAPNP algorithm is evaluated by using real datasets from automotive companies. The required parameters for GAPNP are intently tuned to obtain a common parameter setting for all case studies. The results show that GAPNP significantly outperforms the benchmark algorithm about 30% on average. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=capacitated%20MRP" title="capacitated MRP">capacitated MRP</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=linear%20programming" title=" linear programming"> linear programming</a>, <a href="https://publications.waset.org/abstracts/search?q=automotive%20industries" title=" automotive industries"> automotive industries</a>, <a href="https://publications.waset.org/abstracts/search?q=flow%20shop" title=" flow shop"> flow shop</a>, <a href="https://publications.waset.org/abstracts/search?q=application%20in%20industry" title=" application in industry"> application in industry</a> </p> <a href="https://publications.waset.org/abstracts/67589/a-genetic-algorithm-based-permutation-and-non-permutation-scheduling-heuristics-for-finite-capacity-material-requirement-planning-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67589.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">490</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">2092</span> A Bi-Objective Model to Optimize the Total Time and Idle Probability for Facility Location Problem Behaving as M/M/1/K Queues</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amirhossein%20Chambari">Amirhossein Chambari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This article proposes a bi-objective model for the facility location problem subject to congestion (overcrowding). Motivated by implementations to locate servers in internet mirror sites, communication networks, one-server-systems, so on. This model consider for situations in which immobile (or fixed) service facilities are congested (or queued) by stochastic demand to behave as M/M/1/K queues. We consider for this problem two simultaneous perspectives; (1) Customers (desire to limit times of accessing and waiting for service) and (2) Service provider (desire to limit average facility idle-time). A bi-objective model is setup for facility location problem with two objective functions; (1) Minimizing sum of expected total traveling and waiting time (customers) and (2) Minimizing the average facility idle-time percentage (service provider). The proposed model belongs to the class of mixed-integer nonlinear programming models and the class of NP-hard problems. In addition, to solve the model, controlled elitist non-dominated sorting genetic algorithms (Controlled NSGA-II) and controlled elitist non-dominated ranking genetic algorithms (NRGA-I) are proposed. Furthermore, the two proposed metaheuristics algorithms are evaluated by establishing standard multiobjective metrics. Finally, the results are analyzed and some conclusions are given. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bi-objective" title="bi-objective">bi-objective</a>, <a href="https://publications.waset.org/abstracts/search?q=facility%20location" title=" facility location"> facility location</a>, <a href="https://publications.waset.org/abstracts/search?q=queueing" title=" queueing"> queueing</a>, <a href="https://publications.waset.org/abstracts/search?q=controlled%20NSGA-II" title=" controlled NSGA-II"> controlled NSGA-II</a>, <a href="https://publications.waset.org/abstracts/search?q=NRGA-I" title=" NRGA-I"> NRGA-I</a> </p> <a href="https://publications.waset.org/abstracts/28474/a-bi-objective-model-to-optimize-the-total-time-and-idle-probability-for-facility-location-problem-behaving-as-mm1k-queues" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28474.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">583</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">2091</span> The Influence of Group Heuristics on Corporate Social Responsibility Messages Designed to Reduce Illegal Consumption</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kate%20Whitman">Kate Whitman</a>, <a href="https://publications.waset.org/abstracts/search?q=Zahra%20Murad"> Zahra Murad</a>, <a href="https://publications.waset.org/abstracts/search?q=Joe%20Cox"> Joe Cox</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Corporate social responsibility projects are suggested to motivate consumers to reciprocate good corporate deeds with their custom. When the projects benefit the ingroup vs the outgroup, such as locals rather than foreigners, the effect on reciprocity is suggested to be more powerful. This may be explained by group heuristics, a theory which indicates that favours to the ingroup (but not outgroup) are expected to be reciprocated, resulting in ingroup favouritism. The heuristic is theorised to explain prosocial behaviours towards the ingroup. The aim of this study is to test whether group heuristics similarly explain a reduction in antisocial behaviours towards the ingroup, measured by illegal consumption which harms a group that consumers identify with. In order to test corporate social responsibility messages, a population of interested consumers is required, so sport fans are recruited. A pre-registered experiment (N = 600) tests the influence of a focused “team” benefiting message vs a broader “sport” benefiting message on change in illegal intentions. The influence of group (team) identity and trait reciprocity on message efficacy are tested as measures of group heuristics. Results suggest that the “team” treatment significantly reduces illegal consumption intentions. The “sport” treatment interacted with the team identification measure, increasing illegal consumption intentions for low team identification individuals. The results suggest that corporate social responsibility may be effective in reducing illegal consumption, if the messages are delivered directly from brands to consumers with brand identification. Messages delivered on the behalf of an industry may have an undesirable effect. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=live%20sports" title="live sports">live sports</a>, <a href="https://publications.waset.org/abstracts/search?q=piracy" title=" piracy"> piracy</a>, <a href="https://publications.waset.org/abstracts/search?q=counterfeiting" title=" counterfeiting"> counterfeiting</a>, <a href="https://publications.waset.org/abstracts/search?q=corporate%20social%20responsibility" title=" corporate social responsibility"> corporate social responsibility</a>, <a href="https://publications.waset.org/abstracts/search?q=group%20heuristics" title=" group heuristics"> group heuristics</a>, <a href="https://publications.waset.org/abstracts/search?q=ingroup%20bias" title=" ingroup bias"> ingroup bias</a>, <a href="https://publications.waset.org/abstracts/search?q=team%20identification" title=" team identification"> team identification</a> </p> <a href="https://publications.waset.org/abstracts/176230/the-influence-of-group-heuristics-on-corporate-social-responsibility-messages-designed-to-reduce-illegal-consumption" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176230.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">84</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2090</span> Hierarchical Clustering Algorithms in Data Mining</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Z.%20Abdullah">Z. Abdullah</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20R.%20Hamdan"> A. R. Hamdan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Clustering is a process of grouping objects and data into groups of clusters to ensure that data objects from the same cluster are identical to each other. Clustering algorithms in one of the areas in data mining and it can be classified into partition, hierarchical, density based, and grid-based. Therefore, in this paper, we do a survey and review for four major hierarchical clustering algorithms called CURE, ROCK, CHAMELEON, and BIRCH. The obtained state of the art of these algorithms will help in eliminating the current problems, as well as deriving more robust and scalable algorithms for clustering. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering" title="clustering">clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20learning" title=" unsupervised learning"> unsupervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithms" title=" algorithms"> algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical" title=" hierarchical"> hierarchical</a> </p> <a href="https://publications.waset.org/abstracts/31217/hierarchical-clustering-algorithms-in-data-mining" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31217.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">885</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">2089</span> Solving Weighted Number of Operation Plus Processing Time Due-Date Assignment, Weighted Scheduling and Process Planning Integration Problem Using Genetic and Simulated Annealing Search Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Halil%20Ibrahim%20Demir">Halil Ibrahim Demir</a>, <a href="https://publications.waset.org/abstracts/search?q=Caner%20Erden"> Caner Erden</a>, <a href="https://publications.waset.org/abstracts/search?q=Mumtaz%20Ipek"> Mumtaz Ipek</a>, <a href="https://publications.waset.org/abstracts/search?q=Ozer%20Uygun"> Ozer Uygun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traditionally, the three important manufacturing functions, which are process planning, scheduling and due-date assignment, are performed separately and sequentially. For couple of decades, hundreds of studies are done on integrated process planning and scheduling problems and numerous researches are performed on scheduling with due date assignment problem, but unfortunately the integration of these three important functions are not adequately addressed. Here, the integration of these three important functions is studied by using genetic, random-genetic hybrid, simulated annealing, random-simulated annealing hybrid and random search techniques. As well, the importance of the integration of these three functions and the power of meta-heuristics and of hybrid heuristics are studied. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=process%20planning" title="process planning">process planning</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20scheduling" title=" weighted scheduling"> weighted scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20due-date%20assignment" title=" weighted due-date assignment"> weighted due-date assignment</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20search" title=" genetic search"> genetic search</a>, <a href="https://publications.waset.org/abstracts/search?q=simulated%20annealing" title=" simulated annealing"> simulated annealing</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20meta-heuristics" title=" hybrid meta-heuristics"> hybrid meta-heuristics</a> </p> <a href="https://publications.waset.org/abstracts/57629/solving-weighted-number-of-operation-plus-processing-time-due-date-assignment-weighted-scheduling-and-process-planning-integration-problem-using-genetic-and-simulated-annealing-search-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57629.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">469</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">2088</span> Advanced Technologies and Algorithms for Efficient Portfolio Selection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Konstantinos%20Liagkouras">Konstantinos Liagkouras</a>, <a href="https://publications.waset.org/abstracts/search?q=Konstantinos%20Metaxiotis"> Konstantinos Metaxiotis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we present a classification of the various technologies applied for the solution of the portfolio selection problem according to the discipline and the methodological framework followed. We provide a concise presentation of the emerged categories and we are trying to identify which methods considered obsolete and which lie at the heart of the debate. On top of that, we provide a comparative study of the different technologies applied for efficient portfolio construction and we suggest potential paths for future work that lie at the intersection of the presented techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=portfolio%20selection" title="portfolio selection">portfolio selection</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20techniques" title=" optimization techniques"> optimization techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20models" title=" financial models"> financial models</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic" title=" stochastic"> stochastic</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristics" title=" heuristics"> heuristics</a> </p> <a href="https://publications.waset.org/abstracts/31917/advanced-technologies-and-algorithms-for-efficient-portfolio-selection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31917.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">432</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">2087</span> Analysis of Q-Learning on Artificial Neural Networks for Robot Control Using Live Video Feed</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nihal%20Murali">Nihal Murali</a>, <a href="https://publications.waset.org/abstracts/search?q=Kunal%20Gupta"> Kunal Gupta</a>, <a href="https://publications.waset.org/abstracts/search?q=Surekha%20Bhanot"> Surekha Bhanot</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Training of artificial neural networks (ANNs) using reinforcement learning (RL) techniques is being widely discussed in the robot learning literature. The high model complexity of ANNs along with the model-free nature of RL algorithms provides a desirable combination for many robotics applications. There is a huge need for algorithms that generalize using raw sensory inputs, such as vision, without any hand-engineered features or domain heuristics. In this paper, the standard control problem of line following robot was used as a test-bed, and an ANN controller for the robot was trained on images from a live video feed using Q-learning. A virtual agent was first trained in simulation environment and then deployed onto a robot’s hardware. The robot successfully learns to traverse a wide range of curves and displays excellent generalization ability. Qualitative analysis of the evolution of policies, performance and weights of the network provide insights into the nature and convergence of the learning algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title="artificial neural networks">artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=q-learning" title=" q-learning"> q-learning</a>, <a href="https://publications.waset.org/abstracts/search?q=reinforcement%20learning" title=" reinforcement learning"> reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=robot%20learning" title=" robot learning"> robot learning</a> </p> <a href="https://publications.waset.org/abstracts/70136/analysis-of-q-learning-on-artificial-neural-networks-for-robot-control-using-live-video-feed" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70136.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">372</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">2086</span> Fault Diagnosis of Manufacturing Systems Using AntTreeStoch with Parameter Optimization by ACO</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ouahab%20Kadri">Ouahab Kadri</a>, <a href="https://publications.waset.org/abstracts/search?q=Leila%20Hayet%20Mouss"> Leila Hayet Mouss</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present three diagnostic modules for complex and dynamic systems. These modules are based on three ant colony algorithms, which are AntTreeStoch, Lumer & Faieta and Binary ant colony. We chose these algorithms for their simplicity and their wide application range. However, we cannot use these algorithms in their basement forms as they have several limitations. To use these algorithms in a diagnostic system, we have proposed three variants. We have tested these algorithms on datasets issued from two industrial systems, which are clinkering system and pasteurization system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20algorithms" title="ant colony algorithms">ant colony algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=complex%20and%20dynamic%20systems" title=" complex and dynamic systems"> complex and dynamic systems</a>, <a href="https://publications.waset.org/abstracts/search?q=diagnosis" title=" diagnosis"> diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/42293/fault-diagnosis-of-manufacturing-systems-using-anttreestoch-with-parameter-optimization-by-aco" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42293.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">298</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">2085</span> Enhanced Imperialist Competitive Algorithm for the Cell Formation Problem Using Sequence Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20H.%20Borghei">S. H. Borghei</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Teymourian"> E. Teymourian</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Mobin"> M. Mobin</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20M.%20Komaki"> G. M. Komaki</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Sheikh"> S. Sheikh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Imperialist competitive algorithm (ICA) is a recent meta-heuristic method that is inspired by the social evolutions for solving NP-Hard problems. The ICA is a population based algorithm which has achieved a great performance in comparison to other meta-heuristics. This study is about developing enhanced ICA approach to solve the cell formation problem (CFP) using sequence data. In addition to the conventional ICA, an enhanced version of ICA, namely EICA, applies local search techniques to add more intensification aptitude and embed the features of exploration and intensification more successfully. Suitable performance measures are used to compare the proposed algorithms with some other powerful solution approaches in the literature. In the same way, for checking the proficiency of algorithms, forty test problems are presented. Five benchmark problems have sequence data, and other ones are based on 0-1 matrices modified to sequence based problems. Computational results elucidate the efficiency of the EICA in solving CFP problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cell%20formation%20problem" title="cell formation problem">cell formation problem</a>, <a href="https://publications.waset.org/abstracts/search?q=group%20technology" title=" group technology"> group technology</a>, <a href="https://publications.waset.org/abstracts/search?q=imperialist%20competitive%20algorithm" title=" imperialist competitive algorithm"> imperialist competitive algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=sequence%20data" title=" sequence data"> sequence data</a> </p> <a href="https://publications.waset.org/abstracts/37026/enhanced-imperialist-competitive-algorithm-for-the-cell-formation-problem-using-sequence-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37026.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">455</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">2084</span> A Hybrid Algorithm Based on Greedy Randomized Adaptive Search Procedure and Chemical Reaction Optimization for the Vehicle Routing Problem with Hard Time Windows</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Imen%20Boudali">Imen Boudali</a>, <a href="https://publications.waset.org/abstracts/search?q=Marwa%20Ragmoun"> Marwa Ragmoun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Vehicle Routing Problem with Hard Time Windows (VRPHTW) is a basic distribution management problem that models many real-world problems. The objective of the problem is to deliver a set of customers with known demands on minimum-cost vehicle routes while satisfying vehicle capacity and hard time windows for customers. In this paper, we propose to deal with our optimization problem by using a new hybrid stochastic algorithm based on two metaheuristics: Chemical Reaction Optimization (CRO) and Greedy Randomized Adaptive Search Procedure (GRASP). The first method is inspired by the natural process of chemical reactions enabling the transformation of unstable substances with excessive energy to stable ones. During this process, the molecules interact with each other through a series of elementary reactions to reach minimum energy for their existence. This property is embedded in CRO to solve the VRPHTW. In order to enhance the population diversity throughout the search process, we integrated the GRASP in our method. Simulation results on the base of Solomon’s benchmark instances show the very satisfactory performances of the proposed approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Benchmark%20Problems" title="Benchmark Problems">Benchmark Problems</a>, <a href="https://publications.waset.org/abstracts/search?q=Combinatorial%20Optimization" title=" Combinatorial Optimization"> Combinatorial Optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=Vehicle%20Routing%20Problem%20with%20Hard%20Time%20Windows" title=" Vehicle Routing Problem with Hard Time Windows"> Vehicle Routing Problem with Hard Time Windows</a>, <a href="https://publications.waset.org/abstracts/search?q=Meta-heuristics" title=" Meta-heuristics"> Meta-heuristics</a>, <a href="https://publications.waset.org/abstracts/search?q=Hybridization" title=" Hybridization"> Hybridization</a>, <a href="https://publications.waset.org/abstracts/search?q=GRASP" title=" GRASP"> GRASP</a>, <a href="https://publications.waset.org/abstracts/search?q=CRO" title=" CRO"> CRO</a> </p> <a href="https://publications.waset.org/abstracts/70528/a-hybrid-algorithm-based-on-greedy-randomized-adaptive-search-procedure-and-chemical-reaction-optimization-for-the-vehicle-routing-problem-with-hard-time-windows" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70528.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">411</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" 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