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36 articles for “Swarm-Based Algorithms”
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Review ArticleOpen accessIn the abstractVisualization of Data Mining Techniques for the Prediction of Breast Cancer with High Accuracy RatesVasudev Sharma, Raj Kumar Rajasekaran, Shreya BadhrinarayananBreast cancer is one of the leading causes of death in women worldwide. Around one in 30 women are affected by breast cancer. Mammography has helped in detecting breast cancer in the early stages which have reduced mortality. The diagnosis of breast cancer is dependent on a variety of parameters. In this paper, we aim to create the best model … …Journal of Computer Science, Vol. 15, Issue 1 · 21 Jan 2019 · 8,948 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Elite Pool-Based Big Bang-Big Crunch Metaheuristic for Data ClusteringIbrahim Al-Marashdeh, Ghaith M. Jaradat, Masri Ayob et al.This paper delves into the capacity of enhanced Big Bang-Big Crunch (EBB-BC) metaheuristic to handle data clustering problems. BB-BC is a product of an evolution theory of the universe in physics and astronomy. Two main phases of BB-BC are big bang and big crunch. The big bang phase involves a creation of a population of random initial solutions, while in … …Journal of Computer Science, Vol. 14, Issue 12 · 19 Jun 2018 · 7,879 accessesView PDFCite
Research ArticleOpen accessIn the abstractLPSO: Another Algorithm for Workflow Scheduling in the CloudToan Phan Thanh, Loc Nguyen The, Said Elnaffar, Cuong Nguyen DoanAlthough workflow scheduling problem has been discussed by many researchers, a few efficient solutions have been introduced for Cloud computing. In this article, we present LPSO, a novel algorithm for workflow scheduling. Based on the Particle Swarm Optimization method, our proposed algorithm not only ensures the fast convergence but also avoid being trapped on local extrema. Our simulation experiments using … …Journal of Computer Science, Vol. 12, Issue 12 · 12 Feb 2017 · 6,800 accessesView PDFCite
EditorialOpen accessIn the abstractNew Kind of Statistical MethodsCagdas Hakan AladagAladag, C.H. and E. Egrioglu, 2012. Advanced Time Series Forecasting Methods. In: Advances in Time Series Forecasting, Aladag, C.H. and E. Egrioglu (Eds.), Bentham Science Publishers, Oak Park, ISBN-10: 1608053733. Aladag, C.H., 2011. A Hybrid Intelligent Technique Combines Neural Networks and Tabu Search Methods for Forecasting, Computer Search Algorithms. 1st Edn., Nova Publisher, ISBN: 978-1-61209-043-6. Aladag, C.H., E Egrioglu, U. … …Journal of Mathematics and Statistics, Vol. 10, Issue 4 · 28 Nov 2014 · 7,588 accessesView PDFCite
Research ArticleOpen accessIn the abstractGENETIC-BASED NUTRITION RECOMMENDATION MODELS. A.A. Fayoumi, A. A. Hegazy, M. A. BelalEvolutionary computing is the collective name for a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance. These techniques are being widely applied to a variety of problems in many vital fields. Also, Evolutionary Algorithms (EA) which applied the principles of Evolutionary computations, such as genetic algorithm, particle swarm, ant colony and … …Journal of Computer Science, Vol. 10, Issue 9 · 24 Apr 2014 · 6,854 accessesView PDFCite
Research ArticleOpen accessIn the abstractLOW COMPLEXITY CONSTRAINTS FOR ENERGY AND PERFORMANCE MANAGEMENT OF HETEROGENEOUS MULTICORE PROCESSORS USING DYNAMIC OPTIMIZATIONA. S. Radhamani, E. BaburajOptimization in multicore processor environment is significant in real world dynamic applications, as it is crucial to find and track the change effectively over time, which requires an optimization algorithm. In massively parallel processing multicore processor architectures, like other population based metaheuristics Constraint based Bacterial Foraging Particle Swarm Optimization (CBFPSO) scheduling can be effectively implemented. In this study we discuss … …Journal of Computer Science, Vol. 10, Issue 8 · 3 Apr 2014 · 6,645 accessesView PDFCite
Research ArticleOpen accessIn the abstractOPTIMIZING LOCALIZATION ROUTE USING PARTICLE SWARM-A GENETIC APPROACHL. Lakshmanan, D. C. TomarOne of the most key problems in wireless sensor networks is finding optimal algorithms for sending packets from source node to destination node. Several algorithms exist in literature, since some are in vital role other may not. Since WSN focus on low power consumption during packet transmission and receiving, finally we adopt by merging swarm particle based algorithm with genetic … …American Journal of Applied Sciences, Vol. 11, Issue 3 · 20 Jan 2014 · 7,381 accessesView PDFCite
Research ArticleOpen accessIn the abstractCOMPARISON OF OPTIMIZATION TECHNIQUE TO FIND THE OPTIMAL LOCATION OF FACTS CONTROLLERS FOR TRANSMISSION LINEJagadeesh Kumar Muthukrishnan, Subhransu Sekhar Dash, Harish Kiran Selvakumar et al.The aspect of this study is to compare the three different algorithms such as Genetic Algorithm (GA), Enhanced Genetic Algorithm (EGA) and Particle Swarm Optimization (PSO) to fine the optimal location of the FACTS device in the transmission line. The FACTS devices Thyristor Controlled Series Compensator (TCSC) and Unified Power Flow Controller (UPFC) are selected based on the steady stability … …American Journal of Applied Sciences, Vol. 11, Issue 2 · 26 Dec 2013 · 6,677 accessesView PDFCite
Research ArticleOpen accessIn the abstractAN ALGORITHM FOR MINING USABLE RULES USING A HOLISTIC SWARM BASED APPROACHVeenu Mangat, Renu VigEvolutionary algorithms are capable of finding near optimal solutions to problems which are intractable to solve using conventional methods. One such problem is to accurately classify patients using rule mining methodology while controlling the size of output rules. A massive amount of data pertaining to medicine is generated and recorded daily. Uncovering useful knowledge and assisting decision makers in the … …Journal of Computer Science, Vol. 10, Issue 4 · 9 Dec 2013 · 6,667 accessesView PDFCite
Research ArticleOpen accessIn the abstractA COMPARATIVE ANALYSIS OF OPTIMIZATION TECHNIQUES FOR ARTIFICIAL NEURAL NETWORK IN BIO MEDICAL APPLICATIONSV. Saishanmuga Raja, S. P. RajagopalanIn this study we compare the performance of three evolutionary algorithms such as Genetic Algorithm (GA) Particle Swarm Optimization (PSO) and Ant-Colony Optimization (ACO) which are used to optimize the Artificial Neural Network (ANN). Optimization of Neural Networks improves speed of recall and may also improve the efficiency of training. Here we have used the Ant colony optimization, Particle Swarm … …Journal of Computer Science, Vol. 10, Issue 1 · 12 Nov 2013 · 7,287 accessesView PDFCite