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optimization (ACO)</title> <meta name="description" content="Search results for: ant colony optimization (ACO)"> <meta name="keywords" content="ant colony optimization (ACO)"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button 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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="ant colony optimization (ACO)"> <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> 3439</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: ant colony optimization (ACO)</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3439</span> Comparative Analysis of Two Different Ant Colony Optimization Algorithm for Solving Travelling Salesman Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sourabh%20Joshi">Sourabh Joshi</a>, <a href="https://publications.waset.org/abstracts/search?q=Tarun%20Sharma"> Tarun Sharma</a>, <a href="https://publications.waset.org/abstracts/search?q=Anurag%20Sharma"> Anurag Sharma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ant Colony Optimization is heuristic Algorithm which has been proven a successful technique applied on number of combinatorial optimization problems. Two variants of Ant Colony Optimization algorithm named Ant System and Max-Min Ant System are implemented in MATLAB to solve travelling Salesman Problem and the results are compared. In, this paper both systems are analyzed by solving the some Travelling Salesman Problem and depict which system solve the problem better in term of cost and time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ant%20Colony%20Optimization" title="Ant Colony Optimization">Ant Colony Optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=Travelling%20Salesman%20Problem" title=" Travelling Salesman Problem"> Travelling Salesman Problem</a>, <a href="https://publications.waset.org/abstracts/search?q=Ant%20System" title=" Ant System"> Ant System</a>, <a href="https://publications.waset.org/abstracts/search?q=Max-Min%20Ant%20System" title=" Max-Min Ant System"> Max-Min Ant System</a> </p> <a href="https://publications.waset.org/abstracts/18457/comparative-analysis-of-two-different-ant-colony-optimization-algorithm-for-solving-travelling-salesman-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18457.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">483</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3438</span> Dynamic Construction Site Layout Using Ant Colony Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yassir%20AbdelRazig">Yassir AbdelRazig</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Evolutionary optimization methods such as genetic algorithms have been used extensively for the construction site layout problem. More recently, ant colony optimization algorithms, which are evolutionary methods based on the foraging behavior of ants, have been successfully applied to benchmark combinatorial optimization problems. This paper proposes a formulation of the site layout problem in terms of a sequencing problem that is suitable for solution using an ant colony optimization algorithm. In the construction industry, site layout is a very important planning problem. The objective of site layout is to position temporary facilities both geographically and at the correct time such that the construction work can be performed satisfactorily with minimal costs and improved safety and working environment. During the last decade, evolutionary methods such as genetic algorithms have been used extensively for the construction site layout problem. This paper proposes an ant colony optimization model for construction site layout. A simple case study for a highway project is utilized to illustrate the application of the model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ant%20colony" title="ant colony">ant colony</a>, <a href="https://publications.waset.org/abstracts/search?q=construction%20site%20layout" title=" construction site layout"> construction site layout</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithms" title=" genetic algorithms"> genetic algorithms</a> </p> <a href="https://publications.waset.org/abstracts/28641/dynamic-construction-site-layout-using-ant-colony-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28641.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">383</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3437</span> Performance Comparison of Prim’s and Ant Colony Optimization Algorithm to Select Shortest Path in Case of Link Failure</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rimmy%20Yadav">Rimmy Yadav</a>, <a href="https://publications.waset.org/abstracts/search?q=Avtar%20Singh"> Avtar Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> —Ant Colony Optimization (ACO) is a promising modern approach to the unused combinatorial optimization. Here ACO is applied to finding the shortest during communication link failure. In this paper, the performances of the prim’s and ACO algorithm are made. By comparing the time complexity and program execution time as set of parameters, we demonstrate the pleasant performance of ACO in finding excellent solution to finding shortest path during communication link failure. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20optimization" title="ant colony optimization">ant colony optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=link%20failure" title=" link failure"> link failure</a>, <a href="https://publications.waset.org/abstracts/search?q=prim%E2%80%99s%20algorithm" title=" prim’s algorithm"> prim’s algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=shortest%20path" title=" shortest path"> shortest path</a> </p> <a href="https://publications.waset.org/abstracts/31818/performance-comparison-of-prims-and-ant-colony-optimization-algorithm-to-select-shortest-path-in-case-of-link-failure" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31818.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">3436</span> Bee Colony Optimization Applied to the Bin Packing Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kenza%20Aida%20Amara">Kenza Aida Amara</a>, <a href="https://publications.waset.org/abstracts/search?q=Bachir%20Djebbar"> Bachir Djebbar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We treat the two-dimensional bin packing problem which involves packing a given set of rectangles into a minimum number of larger identical rectangles called bins. This combinatorial problem is NP-hard. We propose a pretreatment for the oriented version of the problem that allows the valorization of the lost areas in the bins and the reduction of the size problem. A heuristic method based on the strategy first-fit adapted to this problem is presented. We present an approach of resolution by bee colony optimization. Computational results express a comparison of the number of bins used with and without pretreatment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bee%20colony%20optimization" title="bee colony optimization">bee colony optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=bin%20packing" title=" bin packing"> bin packing</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=pretreatment" title=" pretreatment"> pretreatment</a> </p> <a href="https://publications.waset.org/abstracts/65005/bee-colony-optimization-applied-to-the-bin-packing-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65005.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">633</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">3435</span> Hybrid Bee Ant Colony Algorithm for Effective Load Balancing and Job Scheduling in Cloud Computing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thomas%20Yeboah">Thomas Yeboah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cloud Computing is newly paradigm in computing that promises a delivery of computing as a service rather than a product, whereby shared resources, software, and information are provided to computers and other devices as a utility (like the electricity grid) over a network (typically the Internet). As Cloud Computing is a newly style of computing on the internet. It has many merits along with some crucial issues that need to be resolved in order to improve reliability of cloud environment. These issues are related with the load balancing, fault tolerance and different security issues in cloud environment.In this paper the main concern is to develop an effective load balancing algorithm that gives satisfactory performance to both, cloud users and providers. This proposed algorithm (hybrid Bee Ant Colony algorithm) is a combination of two dynamic algorithms: Ant Colony Optimization and Bees Life algorithm. Ant Colony algorithm is used in this hybrid Bee Ant Colony algorithm to solve load balancing issues whiles the Bees Life algorithm is used for optimization of job scheduling in cloud environment. The results of the proposed algorithm shows that the hybrid Bee Ant Colony algorithm outperforms the performances of both Ant Colony algorithm and Bees Life algorithm when evaluated the proposed algorithm performances in terms of Waiting time and Response time on a simulator called CloudSim. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20optimization%20algorithm" title="ant colony optimization algorithm">ant colony optimization algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=bees%20life%20algorithm" title=" bees life algorithm"> bees life algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=scheduling%20algorithm" title=" scheduling algorithm"> scheduling algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=performance" title=" performance"> performance</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title=" cloud computing"> cloud computing</a>, <a href="https://publications.waset.org/abstracts/search?q=load%20balancing" title=" load balancing"> load balancing</a> </p> <a href="https://publications.waset.org/abstracts/27139/hybrid-bee-ant-colony-algorithm-for-effective-load-balancing-and-job-scheduling-in-cloud-computing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27139.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">628</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">3434</span> Roullete Wheel Selection Mechanism for Solving Travelling Salesman Problem in Ant Colony Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sourabh%20Joshi">Sourabh Joshi</a>, <a href="https://publications.waset.org/abstracts/search?q=Geetinder%20Kaur"> Geetinder Kaur</a>, <a href="https://publications.waset.org/abstracts/search?q=Sarabjit%20Kaur"> Sarabjit Kaur</a>, <a href="https://publications.waset.org/abstracts/search?q=Gulwatanpreet%20Singh"> Gulwatanpreet Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Geetika%20Mannan"> Geetika Mannan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we have use an algorithm that able to obtain an optimal solution to travelling salesman problem from a huge search space, quickly. This algorithm is based upon the ant colony optimization technique and employees roulette wheel selection mechanism. To illustrate it more clearly, a program has been implemented which is based upon this algorithm, that presents the changing process of route iteration in a more intuitive way. In the event, we had find the optimal path between hundred cities and also calculate the distance between two cities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ant%20colony" title="ant colony">ant colony</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=travelling%20salesman%20problem" title=" travelling salesman problem"> travelling salesman problem</a>, <a href="https://publications.waset.org/abstracts/search?q=roulette%20wheel%20selection" title=" roulette wheel selection"> roulette wheel selection</a> </p> <a href="https://publications.waset.org/abstracts/15736/roullete-wheel-selection-mechanism-for-solving-travelling-salesman-problem-in-ant-colony-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15736.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">441</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">3433</span> Application of Heuristic Integration Ant Colony Optimization in Path Planning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zeyu%20Zhang">Zeyu Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Guisheng%20Yin"> Guisheng Yin</a>, <a href="https://publications.waset.org/abstracts/search?q=Ziying%20Zhang"> Ziying Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Liguo%20Zhang"> Liguo Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper mainly studies the path planning method based on ant colony optimization (ACO), and proposes heuristic integration ant colony optimization (HIACO). This paper not only analyzes and optimizes the principle, but also simulates and analyzes the parameters related to the application of HIACO in path planning. Compared with the original algorithm, the improved algorithm optimizes probability formula, tabu table mechanism and updating mechanism, and introduces more reasonable heuristic factors. The optimized HIACO not only draws on the excellent ideas of the original algorithm, but also solves the problems of premature convergence, convergence to the sub optimal solution and improper exploration to some extent. HIACO can be used to achieve better simulation results and achieve the desired optimization. Combined with the probability formula and update formula, several parameters of HIACO are tested. This paper proves the principle of the HIACO and gives the best parameter range in the research of path planning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20optimization" title="ant colony optimization">ant colony optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic%20integration" title=" heuristic integration"> heuristic integration</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=probability%20formula" title=" probability formula"> probability formula</a> </p> <a href="https://publications.waset.org/abstracts/115269/application-of-heuristic-integration-ant-colony-optimization-in-path-planning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/115269.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">250</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">3432</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">3431</span> IACOP - Route Optimization in Wireless Networks Using Improved Ant Colony Optimization Protocol</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Vasundra">S. Vasundra</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Venkatesh"> D. Venkatesh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wireless networks have gone through an extraordinary growth in the past few years, and will keep on playing a crucial role in future data communication. The present wireless networks aim to make communication possible anywhere and anytime. With the converging of mobile and wireless communications with Internet services, the boundary between mobile personal telecommunications and wireless computer networks is disappearing. Wireless networks of the next generation need the support of all the advances on new architectures, standards, and protocols. Since an ad hoc network may consist of a large number of mobile hosts, this imposes a significant challenge on the design of an effective and efficient routing protocol that can work well in an environment with frequent topological changes. This paper proposes improved ant colony optimization (IACO) technique. It also maintains load balancing in wireless networks. The simulation results show that the proposed IACO performs better than existing routing techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wireless%20networks" title="wireless networks">wireless networks</a>, <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20optimization" title=" ant colony optimization"> ant colony optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=load%20balancing" title=" load balancing"> load balancing</a>, <a href="https://publications.waset.org/abstracts/search?q=architecture" title=" architecture"> architecture</a> </p> <a href="https://publications.waset.org/abstracts/43366/iacop-route-optimization-in-wireless-networks-using-improved-ant-colony-optimization-protocol" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43366.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">3430</span> Software Architecture Optimization Using Swarm Intelligence Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arslan%20Ellahi">Arslan Ellahi</a>, <a href="https://publications.waset.org/abstracts/search?q=Syed%20Amjad%20Hussain"> Syed Amjad Hussain</a>, <a href="https://publications.waset.org/abstracts/search?q=Fawaz%20Saleem%20Bokhari"> Fawaz Saleem Bokhari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Optimization of software architecture can be done with respect to a quality attributes (QA). In this paper, there is an analysis of multiple research papers from different dimensions that have been used to classify those attributes. We have proposed a technique of swarm intelligence Meta heuristic ant colony optimization algorithm as a contribution to solve this critical optimization problem of software architecture. We have ranked quality attributes and run our algorithm on every QA, and then we will rank those on the basis of accuracy. At the end, we have selected the most accurate quality attributes. Ant colony algorithm is an effective algorithm and will perform best in optimizing the QA’s and ranking them. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=complexity" title="complexity">complexity</a>, <a href="https://publications.waset.org/abstracts/search?q=rapid%20evolution" title=" rapid evolution"> rapid evolution</a>, <a href="https://publications.waset.org/abstracts/search?q=swarm%20intelligence" title=" swarm intelligence"> swarm intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=dimensions" title=" dimensions"> dimensions</a> </p> <a href="https://publications.waset.org/abstracts/94992/software-architecture-optimization-using-swarm-intelligence-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94992.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">261</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3429</span> Economic Load Dispatch with Valve-Point Loading Effect by Using Differential Evolution Immunized Ant Colony Optimization Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nur%20Azzammudin%20Rahmat">Nur Azzammudin Rahmat</a>, <a href="https://publications.waset.org/abstracts/search?q=Ismail%20Musirin"> Ismail Musirin</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Farid%20Abidin"> Ahmad Farid Abidin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Economic load dispatch is performed by the utilities in order to determine the best generation level at the most feasible operating cost. In order to guarantee satisfying energy delivery to the consumer, a precise calculation of generation level is required. In order to achieve accurate and practical solution, several considerations such as prohibited operating zones, valve-point effect and ramp-rate limit need to be taken into account. However, these considerations cause the optimization to become complex and difficult to solve. This research focuses on the valve-point effect that causes ripple in the fuel-cost curve. This paper also proposes Differential Evolution Immunized Ant Colony Optimization (DEIANT) in solving economic load dispatch problem with valve-point effect. Comparative studies involving DEIANT, EP and ACO are conducted on IEEE 30-Bus RTS for performance assessments. Results indicate that DEIANT is superior to the other compared methods in terms of calculating lower operating cost and power loss. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20optimization%20%28ACO%29" title="ant colony optimization (ACO)">ant colony optimization (ACO)</a>, <a href="https://publications.waset.org/abstracts/search?q=differential%20evolution%20%28DE%29" title=" differential evolution (DE)"> differential evolution (DE)</a>, <a href="https://publications.waset.org/abstracts/search?q=differential%20evolution%20immunized%20ant%20colony%20optimization%20%28DEIANT%29" title=" differential evolution immunized ant colony optimization (DEIANT)"> differential evolution immunized ant colony optimization (DEIANT)</a>, <a href="https://publications.waset.org/abstracts/search?q=economic%20load%20dispatch%20%28ELD%29" title=" economic load dispatch (ELD)"> economic load dispatch (ELD)</a> </p> <a href="https://publications.waset.org/abstracts/6637/economic-load-dispatch-with-valve-point-loading-effect-by-using-differential-evolution-immunized-ant-colony-optimization-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6637.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">446</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">3428</span> Ant Colony Optimization Control for Multilevel STATCOM</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20T%C3%A9djini">H. Tédjini</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20Meslem"> Y. Meslem</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Guesbaoui"> B. Guesbaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Safa"> A. Safa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Flexible AC Transmission Systems (FACTS) are potentially becoming more flexible and more economical local controllers in the power system; and because of the high MVA ratings, it would be expensive to provide independent, equal, regulated DC voltage sources to power the multilevel converters which are presently proposed for STATCOMs. DC voltage sources can be derived from the DC link capacitances which are charged by the rectified ac power. In this paper a new stronger control combined of nonlinear control based Lyapunov’s theorem and Ant Colony Algorithm (ACA) to maintain stability of multilevel STATCOM and the utility. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Static%20Compensator%20%28STATCOM%29" title="Static Compensator (STATCOM)">Static Compensator (STATCOM)</a>, <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20optimization%20%28ACO%29" title=" ant colony optimization (ACO)"> ant colony optimization (ACO)</a>, <a href="https://publications.waset.org/abstracts/search?q=lyapunov%20control%20theory" title=" lyapunov control theory"> lyapunov control theory</a>, <a href="https://publications.waset.org/abstracts/search?q=Decoupled%20power%20control" title=" Decoupled power control"> Decoupled power control</a>, <a href="https://publications.waset.org/abstracts/search?q=neutral%20point%20clamped%20%28NPC%29" title=" neutral point clamped (NPC)"> neutral point clamped (NPC)</a> </p> <a href="https://publications.waset.org/abstracts/19254/ant-colony-optimization-control-for-multilevel-statcom" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19254.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">556</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">3427</span> Solving Flowshop Scheduling Problems with Ant Colony Optimization Heuristic</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arshad%20Mehmood%20Ch">Arshad Mehmood Ch</a>, <a href="https://publications.waset.org/abstracts/search?q=Riaz%20Ahmad"> Riaz Ahmad</a>, <a href="https://publications.waset.org/abstracts/search?q=Imran%20Ali%20Ch"> Imran Ali Ch</a>, <a href="https://publications.waset.org/abstracts/search?q=Waqas%20Durrani"> Waqas Durrani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study deals with the application of Ant Colony Optimization (ACO) approach to solve no-wait ﬂowshop scheduling problem (NW-FSSP). ACO algorithm so developed has been coded on Matlab computer application. The paper covers detailed steps to apply ACO and focuses on judging the strength of ACO in relation to other solution techniques previously applied to solve no-wait flowshop problem. The general purpose approach was able to find reasonably accurate solutions for almost all the problems under consideration and was able to handle a fairly large spectrum of problems with far reduced CPU effort. Careful scrutiny of the results reveals that the algorithm presented results better than other approaches like Genetic algorithm and Tabu Search heuristics etc; earlier applied to solve NW-FSSP data sets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=no-wait" title="no-wait">no-wait</a>, <a href="https://publications.waset.org/abstracts/search?q=%EF%AC%82owshop" title=" ﬂowshop"> ﬂowshop</a>, <a href="https://publications.waset.org/abstracts/search?q=scheduling" title=" scheduling"> scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20optimization%20%28ACO%29" title=" ant colony optimization (ACO)"> ant colony optimization (ACO)</a>, <a href="https://publications.waset.org/abstracts/search?q=makespan" title=" makespan"> makespan</a> </p> <a href="https://publications.waset.org/abstracts/9974/solving-flowshop-scheduling-problems-with-ant-colony-optimization-heuristic" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9974.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">434</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">3426</span> An Ant Colony Optimization Approach for the Pollution Routing Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20Parthiban">P. Parthiban</a>, <a href="https://publications.waset.org/abstracts/search?q=Sonu%20Rajak"> Sonu Rajak</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Kannan"> N. Kannan</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Dhanalakshmi"> R. Dhanalakshmi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with the Vehicle Routing Problem (VRP) with environmental considerations which is called Pollution Routing Problem (PRP). The objective is to minimize the operational and environmental costs. It consists of routing a number of vehicles to serve a set of customers, and determining fuel consumption, driver wages and their speed on each route segment, while respecting the capacity constraints and time windows. In this context, we presented an Ant Colony Optimization (ACO) approach, combined with a Speed Optimization Algorithm (SOA) to solve the PRP. The proposed solution method consists of two stages. Stage one is to solve a Vehicle Routing Problem with Time Window (VRPTW) using ACO and in the second stage a SOA is run on the resulting VRPTW solutions. Given a vehicle route, the SOA consists of finding the optimal speed on each arc of the route in order to minimize an objective function comprising fuel consumption costs and driver wages. The proposed algorithm tested on benchmark problem, the preliminary results show that the proposed algorithm is able to provide good solutions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20optimization" title="ant colony optimization">ant colony optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=CO2%20emissions" title=" CO2 emissions"> CO2 emissions</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=speed%20optimization" title=" speed optimization"> speed optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=vehicle%20routing" title=" vehicle routing"> vehicle routing</a> </p> <a href="https://publications.waset.org/abstracts/52973/an-ant-colony-optimization-approach-for-the-pollution-routing-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52973.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">322</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">3425</span> Classification Rule Discovery by Using Parallel Ant Colony Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Waseem%20Shahzad">Waseem Shahzad</a>, <a href="https://publications.waset.org/abstracts/search?q=Ayesha%20Tahir%20Khan"> Ayesha Tahir Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamid%20Hussain%20Awan"> Hamid Hussain Awan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ant-Miner algorithm that lies under ACO algorithms is used to extract knowledge from data in the form of rules. A variant of Ant-Miner algorithm named as cAnt-MinerPB is used to generate list of rules using pittsburgh approach in order to maintain the rule interaction among the rules that are generated. In this paper, we propose a parallel Ant MinerPB in which Ant colony optimization algorithm runs parallel. In this technique, a data set is divided vertically (i-e attributes) into different subsets. These subsets are created based on the correlation among attributes using Mutual Information (MI). It generates rules in a parallel manner and then merged to form a final list of rules. The results have shown that the proposed technique achieved higher accuracy when compared with original cAnt-MinerPB and also the execution time has also reduced. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20optimization" title="ant colony optimization">ant colony optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=parallel%20Ant-MinerPB" title=" parallel Ant-MinerPB"> parallel Ant-MinerPB</a>, <a href="https://publications.waset.org/abstracts/search?q=vertical%20partitioning" title=" vertical partitioning"> vertical partitioning</a>, <a href="https://publications.waset.org/abstracts/search?q=classification%20rule%20discovery" title=" classification rule discovery"> classification rule discovery</a> </p> <a href="https://publications.waset.org/abstracts/43773/classification-rule-discovery-by-using-parallel-ant-colony-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43773.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">295</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">3424</span> Comparison of ANFIS Update Methods Using Genetic Algorithm, Particle Swarm Optimization, and Artificial Bee Colony</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Michael%20R.%20Phangtriastu">Michael R. Phangtriastu</a>, <a href="https://publications.waset.org/abstracts/search?q=Herriyandi%20Herriyandi"> Herriyandi Herriyandi</a>, <a href="https://publications.waset.org/abstracts/search?q=Diaz%20D.%20Santika"> Diaz D. Santika</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a comparison of the implementation of metaheuristic algorithms to train the antecedent parameters and consequence parameters in the adaptive network-based fuzzy inference system (ANFIS). The algorithms compared are genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC). The objective of this paper is to benchmark well-known metaheuristic algorithms. The algorithms are applied to several data set with different nature. The combinations of the algorithms' parameters are tested. In all algorithms, a different number of populations are tested. In PSO, combinations of velocity are tested. In ABC, a different number of limit abandonment are tested. Experiments find out that ABC is more reliable than other algorithms, ABC manages to get better mean square error (MSE) than other algorithms in all data set. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ANFIS" title="ANFIS">ANFIS</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20bee%20colony" title=" artificial bee colony"> artificial bee colony</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=metaheuristic%20algorithm" title=" metaheuristic algorithm"> metaheuristic algorithm</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/68821/comparison-of-anfis-update-methods-using-genetic-algorithm-particle-swarm-optimization-and-artificial-bee-colony" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68821.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">352</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">3423</span> Ant System with Acoustic Communication</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saad%20Bougrine">Saad Bougrine</a>, <a href="https://publications.waset.org/abstracts/search?q=Salma%20Ouchraa"> Salma Ouchraa</a>, <a href="https://publications.waset.org/abstracts/search?q=Belaid%20Ahiod"> Belaid Ahiod</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelhakim%20Ameur%20El%20Imrani"> Abdelhakim Ameur El Imrani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ant colony optimization is an ant algorithm framework that took inspiration from foraging behaviour of ant colonies. Indeed, ACO algorithms use a chemical communication, represented by pheromone trails, to build good solutions. However, ants involve different communication channels to interact. Thus, this paper introduces the acoustic communication between ants while they are foraging. This process allows fine and local exploration of search space and permits optimal solution to be improved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=acoustic%20communication" title="acoustic communication">acoustic communication</a>, <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20optimization" title=" ant colony optimization"> ant colony 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=traveling%20salesman%20problem" title=" traveling salesman problem"> traveling salesman problem</a> </p> <a href="https://publications.waset.org/abstracts/7857/ant-system-with-acoustic-communication" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7857.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">586</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">3422</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">3421</span> Solving Directional Overcurrent Relay Coordination Problem Using Artificial Bees Colony</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20H.%20Hussain">M. H. Hussain</a>, <a href="https://publications.waset.org/abstracts/search?q=I.%20Musirin"> I. Musirin</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20F.%20Abidin"> A. F. Abidin</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20R.%20A.%20Rahim"> S. R. A. Rahim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the implementation of Artificial Bees Colony (ABC) algorithm in solving Directional OverCurrent Relays (DOCRs) coordination problem for near-end faults occurring in fixed network topology. The coordination optimization of DOCRs is formulated as linear programming (LP) problem. The objective function is introduced to minimize the operating time of the associated relay which depends on the time multiplier setting. The proposed technique is to taken as a technique for comparison purpose in order to highlight its superiority. The proposed algorithms have been tested successfully on 8 bus test system. The simulation results demonstrated that the ABC algorithm which has been proved to have good search ability is capable in dealing with constraint optimization problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20bees%20colony" title="artificial bees colony">artificial bees colony</a>, <a href="https://publications.waset.org/abstracts/search?q=directional%20overcurrent%20relay%20coordination%20problem" title=" directional overcurrent relay coordination problem"> directional overcurrent relay coordination problem</a>, <a href="https://publications.waset.org/abstracts/search?q=relay%20settings" title=" relay settings"> relay settings</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20multiplier%20setting" title=" time multiplier setting"> time multiplier setting</a> </p> <a href="https://publications.waset.org/abstracts/9892/solving-directional-overcurrent-relay-coordination-problem-using-artificial-bees-colony" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9892.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">330</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">3420</span> ACO-TS: an ACO-based Algorithm for Optimizing Cloud Task Scheduling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fahad%20Y.%20Al-dawish">Fahad Y. Al-dawish</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The current trend by a large number of organizations and individuals to use cloud computing. Many consider it a significant shift in the field of computing. Cloud computing are distributed and parallel systems consisting of a collection of interconnected physical and virtual machines. With increasing request and profit of cloud computing infrastructure, diverse computing processes can be executed on cloud environment. Many organizations and individuals around the world depend on the cloud computing environments infrastructure to carry their applications, platform, and infrastructure. One of the major and essential issues in this environment related to allocating incoming tasks to suitable virtual machine (cloud task scheduling). Cloud task scheduling is classified as optimization problem, and there are several meta-heuristic algorithms have been anticipated to solve and optimize this problem. Good task scheduler should execute its scheduling technique on altering environment and the types of incoming task set. In this research project a cloud task scheduling methodology based on ant colony optimization ACO algorithm, we call it ACO-TS Ant Colony Optimization for Task Scheduling has been proposed and compared with different scheduling algorithms (Random, First Come First Serve FCFS, and Fastest Processor to the Largest Task First FPLTF). Ant Colony Optimization (ACO) is random optimization search method that will be used for assigning incoming tasks to available virtual machines VMs. The main role of proposed algorithm is to minimizing the makespan of certain tasks set and maximizing resource utilization by balance the load among virtual machines. The proposed scheduling algorithm was evaluated by using Cloudsim toolkit framework. Finally after analyzing and evaluating the performance of experimental results we find that the proposed algorithm ACO-TS perform better than Random, FCFS, and FPLTF algorithms in each of the makespaan and resource utilization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cloud%20Task%20scheduling" title="cloud Task scheduling">cloud Task scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20optimization%20%28ACO%29" title=" ant colony optimization (ACO)"> ant colony optimization (ACO)</a>, <a href="https://publications.waset.org/abstracts/search?q=cloudsim" title=" cloudsim"> cloudsim</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title=" cloud computing"> cloud computing</a> </p> <a href="https://publications.waset.org/abstracts/32867/aco-ts-an-aco-based-algorithm-for-optimizing-cloud-task-scheduling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32867.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">3419</span> Hybrid Artificial Bee Colony and Least Squares Method for Rule-Based Systems Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahcene%20Habbi">Ahcene Habbi</a>, <a href="https://publications.waset.org/abstracts/search?q=Yassine%20Boudouaoui"> Yassine Boudouaoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with the problem of automatic rule generation for fuzzy systems design. The proposed approach is based on hybrid artificial bee colony (ABC) optimization and weighted least squares (LS) method and aims to find the structure and parameters of fuzzy systems simultaneously. More precisely, two ABC based fuzzy modeling strategies are presented and compared. The first strategy uses global optimization to learn fuzzy models, the second one hybridizes ABC and weighted least squares estimate method. The performances of the proposed ABC and ABC-LS fuzzy modeling strategies are evaluated on complex modeling problems and compared to other advanced modeling methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automatic%20design" title="automatic design">automatic design</a>, <a href="https://publications.waset.org/abstracts/search?q=learning" title=" learning"> learning</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20rules" title=" fuzzy rules"> fuzzy rules</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid" title=" hybrid"> hybrid</a>, <a href="https://publications.waset.org/abstracts/search?q=swarm%20optimization" title=" swarm optimization"> swarm optimization</a> </p> <a href="https://publications.waset.org/abstracts/15603/hybrid-artificial-bee-colony-and-least-squares-method-for-rule-based-systems-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15603.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">437</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">3418</span> Hybrid Gravity Gradient Inversion-Ant Colony Optimization Algorithm for Motion Planning of Mobile Robots</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Meng%20Wu">Meng Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Motion planning is a common task required to be fulfilled by robots. A strategy combining Ant Colony Optimization (ACO) and gravity gradient inversion algorithm is proposed for motion planning of mobile robots. In this paper, in order to realize optimal motion planning strategy, the cost function in ACO is designed based on gravity gradient inversion algorithm. The obstacles around mobile robot can cause gravity gradient anomalies; the gradiometer is installed on the mobile robot to detect the gravity gradient anomalies. After obtaining the anomalies, gravity gradient inversion algorithm is employed to calculate relative distance and orientation between mobile robot and obstacles. The relative distance and orientation deduced from gravity gradient inversion algorithm is employed as cost function in ACO algorithm to realize motion planning. The proposed strategy is validated by the simulation and experiment results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=motion%20planning" title="motion planning">motion planning</a>, <a href="https://publications.waset.org/abstracts/search?q=gravity%20gradient%20inversion%20algorithm" title=" gravity gradient inversion algorithm"> gravity gradient inversion algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20optimization" title=" ant colony optimization"> ant colony optimization</a> </p> <a href="https://publications.waset.org/abstracts/110462/hybrid-gravity-gradient-inversion-ant-colony-optimization-algorithm-for-motion-planning-of-mobile-robots" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/110462.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">137</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">3417</span> Multi-Criteria Test Case Selection Using Ant Colony Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Niranjana%20Devi%20N.">Niranjana Devi N.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Test case selection is to select the subset of only the fit test cases and remove the unfit, ambiguous, redundant, unnecessary test cases which in turn improve the quality and reduce the cost of software testing. Test cases optimization is the problem of finding the best subset of test cases from a pool of the test cases to be audited. It will meet all the objectives of testing concurrently. But most of the research have evaluated the fitness of test cases only on single parameter fault detecting capability and optimize the test cases using a single objective. In the proposed approach, nine parameters are considered for test case selection and the best subset of parameters for test case selection is obtained using Interval Type-2 Fuzzy Rough Set. Test case selection is done in two stages. The first stage is the fuzzy entropy-based filtration technique, used for estimating and reducing the ambiguity in test case fitness evaluation and selection. The second stage is the ant colony optimization-based wrapper technique with a forward search strategy, employed to select test cases from the reduced test suite of the first stage. The results are evaluated using the Coverage parameters, Precision, Recall, F-Measure, APSC, APDC, and SSR. The experimental evaluation demonstrates that by this approach considerable computational effort can be avoided. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20optimization" title="ant colony optimization">ant colony optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20entropy" title=" fuzzy entropy"> fuzzy entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=interval%20type-2%20fuzzy%20rough%20set" title=" interval type-2 fuzzy rough set"> interval type-2 fuzzy rough set</a>, <a href="https://publications.waset.org/abstracts/search?q=test%20case%20selection" title=" test case selection"> test case selection</a> </p> <a href="https://publications.waset.org/abstracts/30870/multi-criteria-test-case-selection-using-ant-colony-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30870.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">668</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">3416</span> Traffic Signal Control Using Citizens’ Knowledge through the Wisdom of the Crowd</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aleksandar%20Jovanovic">Aleksandar Jovanovic</a>, <a href="https://publications.waset.org/abstracts/search?q=Katarina%20Kukic"> Katarina Kukic</a>, <a href="https://publications.waset.org/abstracts/search?q=Ana%20Uzelac"> Ana Uzelac</a>, <a href="https://publications.waset.org/abstracts/search?q=Dusan%20Teodorovic"> Dusan Teodorovic</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wisdom of the Crowd (WoC) is a decentralized method that uses the collective intelligence of humans. Individual guesses may be far from the target, but when considered as a group, they converge on optimal solutions for a given problem. We will utilize WoC to address the challenge of controlling traffic lights within intersections from the streets of Kragujevac, Serbia. The problem at hand falls within the category of NP-hard problems. We will employ an algorithm that leverages the swarm intelligence of bees: Bee Colony Optimization (BCO). Data regarding traffic signal timing at a single intersection will be gathered from citizens through a survey. Results obtained in that manner will be compared to the BCO results for different traffic scenarios. We will use Vissim traffic simulation software as a tool to compare the performance of bees’ and humans’ collective intelligence. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wisdom%20of%20the%20crowd" title="wisdom of the crowd">wisdom of the crowd</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20signal%20control" title=" traffic signal control"> traffic signal control</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=bee%20colony%20optimization" title=" bee colony optimization"> bee colony optimization</a> </p> <a href="https://publications.waset.org/abstracts/174794/traffic-signal-control-using-citizens-knowledge-through-the-wisdom-of-the-crowd" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/174794.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">108</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">3415</span> Internet of Things: Route Search Optimization Applying Ant Colony Algorithm and Theory of Computer Science</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tushar%20Bhardwaj">Tushar Bhardwaj</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Internet of Things (IoT) possesses a dynamic network where the network nodes (mobile devices) are added and removed constantly and randomly, hence the traffic distribution in the network is quite variable and irregular. The basic but very important part in any network is route searching. We have many conventional route searching algorithms like link-state, and distance vector algorithms but they are restricted to the static point to point network topology. In this paper we propose a model that uses the Ant Colony Algorithm for route searching. It is dynamic in nature and has positive feedback mechanism that conforms to the route searching. We have also embedded the concept of Non-Deterministic Finite Automata [NDFA] minimization to reduce the network to increase the performance. Results show that Ant Colony Algorithm gives the shortest path from the source to destination node and NDFA minimization reduces the broadcasting storm effectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=routing" title="routing">routing</a>, <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20algorithm" title=" ant colony algorithm"> ant colony algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=NDFA" title=" NDFA"> NDFA</a>, <a href="https://publications.waset.org/abstracts/search?q=IoT" title=" IoT"> IoT</a> </p> <a href="https://publications.waset.org/abstracts/1965/internet-of-things-route-search-optimization-applying-ant-colony-algorithm-and-theory-of-computer-science" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1965.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">444</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">3414</span> Particle Filter State Estimation Algorithm Based on Improved Artificial Bee Colony Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Guangyuan%20Zhao">Guangyuan Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Nan%20Huang"> Nan Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Xuesong%20Han"> Xuesong Han</a>, <a href="https://publications.waset.org/abstracts/search?q=Xu%20Huang"> Xu Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to solve the problem of sample dilution in the traditional particle filter algorithm and achieve accurate state estimation in a nonlinear system, a particle filter method based on an improved artificial bee colony (ABC) algorithm was proposed. The algorithm simulated the process of bee foraging and optimization and made the high likelihood region of the backward probability of particles moving to improve the rationality of particle distribution. The opposition-based learning (OBL) strategy is introduced to optimize the initial population of the artificial bee colony algorithm. The convergence factor is introduced into the neighborhood search strategy to limit the search range and improve the convergence speed. Finally, the crossover and mutation operations of the genetic algorithm are introduced into the search mechanism of the following bee, which makes the algorithm jump out of the local extreme value quickly and continue to search the global extreme value to improve its optimization ability. The simulation results show that the improved method can improve the estimation accuracy of particle filters, ensure the diversity of particles, and improve the rationality of particle distribution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=particle%20filter" title="particle filter">particle filter</a>, <a href="https://publications.waset.org/abstracts/search?q=impoverishment" title=" impoverishment"> impoverishment</a>, <a href="https://publications.waset.org/abstracts/search?q=state%20estimation" title=" state estimation"> state estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20bee%20colony%20algorithm" title=" artificial bee colony algorithm"> artificial bee colony algorithm</a> </p> <a href="https://publications.waset.org/abstracts/174985/particle-filter-state-estimation-algorithm-based-on-improved-artificial-bee-colony-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/174985.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">151</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">3413</span> ACOPIN: An ACO Algorithm with TSP Approach for Clustering Proteins in Protein Interaction Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jamaludin%20Sallim">Jamaludin Sallim</a>, <a href="https://publications.waset.org/abstracts/search?q=Rozlina%20Mohamed"> Rozlina Mohamed</a>, <a href="https://publications.waset.org/abstracts/search?q=Roslina%20Abdul%20Hamid"> Roslina Abdul Hamid</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we proposed an Ant Colony Optimization (ACO) algorithm together with Traveling Salesman Problem (TSP) approach to investigate the clustering problem in Protein Interaction Networks (PIN). We named this combination as ACOPIN. The purpose of this work is two-fold. First, to test the efficacy of ACO in clustering PIN and second, to propose the simple generalization of the ACO algorithm that might allow its application in clustering proteins in PIN. We split this paper to three main sections. First, we describe the PIN and clustering proteins in PIN. Second, we discuss the steps involved in each phase of ACO algorithm. Finally, we present some results of the investigation with the clustering patterns. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20optimization%20algorithm" title="ant colony optimization algorithm">ant colony optimization algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=searching%20algorithm" title=" searching algorithm"> searching algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20functional%20module" title=" protein functional module"> protein functional module</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20interaction%20network" title=" protein interaction network "> protein interaction network </a> </p> <a href="https://publications.waset.org/abstracts/22367/acopin-an-aco-algorithm-with-tsp-approach-for-clustering-proteins-in-protein-interaction-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22367.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">612</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">3412</span> An Improved Ant Colony Algorithm for Genome Rearrangements</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Essam%20Al%20Daoud">Essam Al Daoud</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Genome rearrangement is an important area in computational biology and bioinformatics. The basic problem in genome rearrangements is to compute the edit distance, i.e., the minimum number of operations needed to transform one genome into another. Unfortunately, unsigned genome rearrangement problem is NP-hard. In this study an improved ant colony optimization algorithm to approximate the edit distance is proposed. The main idea is to convert the unsigned permutation to signed permutation and evaluate the ants by using Kaplan algorithm. Two new operations are added to the standard ant colony algorithm: Replacing the worst ants by re-sampling the ants from a new probability distribution and applying the crossover operations on the best ants. The proposed algorithm is tested and compared with the improved breakpoint reversal sort algorithm by using three datasets. The results indicate that the proposed algorithm achieves better accuracy ratio than the previous methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20algorithm" title="ant colony algorithm">ant colony algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=edit%20distance" title=" edit distance"> edit distance</a>, <a href="https://publications.waset.org/abstracts/search?q=genome%0D%0Abreakpoint" title=" genome breakpoint"> genome breakpoint</a>, <a href="https://publications.waset.org/abstracts/search?q=genome%20rearrangement" title=" genome rearrangement"> genome rearrangement</a>, <a href="https://publications.waset.org/abstracts/search?q=reversal%20sort" title=" reversal sort"> reversal sort</a> </p> <a href="https://publications.waset.org/abstracts/5601/an-improved-ant-colony-algorithm-for-genome-rearrangements" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5601.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">344</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">3411</span> Design of Digital IIR Filter Using Opposition Learning and Artificial Bee Colony Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=J.%20S.%20Dhillon">J. S. Dhillon</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20K.%20Dhaliwal"> K. K. Dhaliwal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In almost all the digital filtering applications the digital infinite impulse response (IIR) filters are preferred over finite impulse response (FIR) filters because they provide much better performance, less computational cost and have smaller memory requirements for similar magnitude specifications. However, the digital IIR filters are generally multimodal with respect to the filter coefficients and therefore, reliable methods that can provide global optimal solutions are required. The artificial bee colony (ABC) algorithm is one such recently introduced meta-heuristic optimization algorithm. But in some cases it shows insufficiency while searching the solution space resulting in a weak exchange of information and hence is not able to return better solutions. To overcome this deficiency, the opposition based learning strategy is incorporated in ABC and hence a modified version called oppositional artificial bee colony (OABC) algorithm is proposed in this paper. Duplication of members is avoided during the run which also augments the exploration ability. The developed algorithm is then applied for the design of optimal and stable digital IIR filter structure where design of low-pass (LP) and high-pass (HP) filters is carried out. Fuzzy theory is applied to achieve maximize satisfaction of minimum magnitude error and stability constraints. To check the effectiveness of OABC, the results are compared with some well established filter design techniques and it is observed that in most cases OABC returns better or atleast comparable results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=digital%20infinite%20impulse%20response%20filter" title="digital infinite impulse response filter">digital infinite impulse response filter</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20bee%20colony%20optimization" title=" artificial bee colony optimization"> artificial bee colony optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=opposition%20based%20learning" title=" opposition based learning"> opposition based learning</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20filter%20design" title=" digital filter design"> digital filter design</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-parameter%20optimization" title=" multi-parameter optimization"> multi-parameter optimization</a> </p> <a href="https://publications.waset.org/abstracts/33966/design-of-digital-iir-filter-using-opposition-learning-and-artificial-bee-colony-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33966.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">477</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">3410</span> Metabolic Pathway Analysis of Microbes using the Artificial Bee Colony Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Serena%20Gomez">Serena Gomez</a>, <a href="https://publications.waset.org/abstracts/search?q=Raeesa%20Tanseen"> Raeesa Tanseen</a>, <a href="https://publications.waset.org/abstracts/search?q=Netra%20Shaligram"> Netra Shaligram</a>, <a href="https://publications.waset.org/abstracts/search?q=Nithin%20Francis"> Nithin Francis</a>, <a href="https://publications.waset.org/abstracts/search?q=Sandesh%20B.%20J."> Sandesh B. J.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The human gut consists of a community of microbes which has a lot of effects on human health disease. Metabolic modeling can help to predict relative populations of stable microbes and their effect on health disease. In order to study and visualize microbes in the human gut, we developed a tool that offers the following modules: Build a tool that can be used to perform Flux Balance Analysis for microbes in the human gut using the Artificial Bee Colony optimization algorithm. Run simulations for an individual microbe in different conditions, such as aerobic and anaerobic and visualize the results of these simulations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=microbes" title="microbes">microbes</a>, <a href="https://publications.waset.org/abstracts/search?q=metabolic%20modeling" title=" metabolic modeling"> metabolic modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=flux%20balance%20analysis" title=" flux balance analysis"> flux balance analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20bee%20colony" title=" artificial bee colony"> artificial bee colony</a> </p> <a href="https://publications.waset.org/abstracts/162178/metabolic-pathway-analysis-of-microbes-using-the-artificial-bee-colony-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162178.pdf" target="_blank" class="btn 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