Search Results

31 - 40 of 66 items :

  • evolutionary algorithms x
  • Electrical Engineering x
Clear All
A Parametric Testing Of The Firefly Algorithm In The Determination Of The Optimal Osmotic Drying Parameters Of Mushrooms

. Torreggiani and G. Bertolo, Osmotic pretreatments in fruit processing: chemical, physical and structural effects, Journal of Food Engineering, 49(30), 2001, 247–253. [15] R. Imanirad and J.S. Yeomans, Fireflies in the Fruits and Vegetables: Combining the Firefly Algorithm with Goal Programming for Setting Optimal Osmotic Dehydration Parameters of Produce, in Recent Advances in Swarm Intelligence and Evolutionary Computation. X-S. Yang (ed.), Springer, Heidelberg, Germany, 2015, 49-69. [16] J.S. Yeomans, Computing Optimal Food Drying Parameters Using the Firefly

Open access
Segmentation and Edge Detection Based on Modified ant Colony Optimization for Iris Image Processing

. [15] V. Ramos and F. Almeida, ”Artificial ant colonies in digital image habitats-a mass behaviour effect study on pattern recognition,” Arxiv preprint cs/0412086, 2004. [16] D. R. Chialvo and M. M. Millonas, ”How swarms build cognitive maps,” NATO ASI SERIES F COMPUTER AND SYSTEMS SCIENCES, vol. 144, pp. 439-439, 1995. [17] T. Niknam, R. Khorshidi, and B. B. Firouzi, ”A hybrid evolutionary algorithm for distribution feeder reconfiguration,” Sadhana, vol. 35, pp. 139-162, 2010. [18] H. Cao, P. Huang, and S. Luo, ”A

Open access
A Cross Unequal Clustering Routing Algorithm for Sensor Network

Wireless Communications , 1 (4), 660-670. [4] Marcelloni, F., Vecchio, M. (2010). Enabling energyefficient and lossy-aware data compression in wireless sensor networks by multi-objective evolutionary optimization. Information Sciences , 180 (10), 1924-1941. [5] Chamam, A., Pierre, S. (2009). On the planning of wireless sensor networks: Energy-efficient clustering under the joint routing and coverage constraint. IEEE Transactions on Mobile Computing , 8 (8), 1077-1086. [6] Li, C., Ye, M., Chen, G., Wu, J. (2005). An

Open access
A New Mechanism for Data Visualization with Tsk-Type Preprocessed Collaborative Fuzzy Rule Based System

. B. Campello, A. A. Freitas, and A. C. P. L. F. de Carvalho, A survey of evolutionary algorithms for clustering, IEEE Transaction on System Man Cybernetics- part-c, vol. 39, no. 2, pp. 133-155, 2009. [16] R. Xu and D. Wunsch, Survey of clustering algorithms, IEEE Transaction on Neural Networks, vol. 16, no. 3, pp. 645-678, 2005. [17] R. R. Yager and D. P. Filev, Approximate clustering via the mountain method, IEEE Transaction on System, Man, Cybernetics, vol. 24, no. 8, pp. 1279-1284,1994. [18] P. R. Kersten

Open access
Repulsive Self-Adaptive Acceleration Particle Swarm Optimization Approach

Electronic Engineering , 5(2):256–257, 2010. [17] S. A. Ludwig, Towards A Repulsive and Adaptive Particle Swarm Optimization Algorithm, Proceedings of Genetic and Evolutionary Computation Conference (ACM GECCO), Amsterdam, Netherlands, July 2013 (short paper). [18] J. Riget and J. S. Vesterstrø, A diversity-guided particle swarm optimizer—The ARPSO, EVALife Technical Report no. 2002-2 , 2002. [19] A. Ide and K. Yasuda, A basic study of adaptive particle swarm optimization, Denki Gakkai Ronbunshi, Electrical Engineering in Japan , 151(3):41–49, 2005

Open access
Simulation and Experimental Evaluation of the EKF Simultaneous Localization and Mapping Algorithm on the Wifibot Mobile Robot

, Vol. 50, No. 4, 2007, pp. 375–397. [10] L. D’Alfonso, W. Lucia, P. Muraca and P. Pugliese, Mobile robot localization via EKF and UKF: A comparison based on real data, Robotics and Autonomous Systems, Vol. 74, Part A, 2015, pp. 122-127. [11] M. Begum, G.K.I Mann and R.G. Gosine, An Evolutionary SLAM Algorithm for Mobile Robots, International Conference on Intelligent Robots and Systems, 2006, pp. 4066–4071. [12] M. Dissanayake, P. Newman, S. Clark, H.F. Durrant-Whyte and M. Csorba, A Solution to the Simultaneous Localization and Map Building (SLAM

Open access
Using Particle Swarm Optimization to Accurately Identify Syntactic Phrases in Free Text

. Journal of Theoretical Biology, Vol. 255, pp. 250-258, 2008. [13] Changsheng Zhang, Jigui Sun, An Alternate two phases particle swarm optimization algorithm for flow shop scheduling problem. Expert Systems with Applications, Vol. 36, pp. 5162-5167, 2009. [14] S.X. Yu, J. Shi, Segmentation with Pairwise Attraction and Repulsion. Proceedings of ICCV-2001 Conference, Vancouver, Canada, pp. 52–58, 2001. [15] Karlheinz Stber, Petra Wagner, David Stall, Jens Helbig, Matthias Thomae, Jens Blauert, Wolfgang Hess, H. Mangold, Speech Synthesis by Multilevel

Open access
Swarm Algorithms for NLP - The Case of Limited Training Data

Classification (2 nd edition), Wiley Interscience, New York, U.S.A., 2001. [16] G. Tambouratzis, Conditional Random Fields versus template-matching in MT phrasing tasks involving sparse training data, Pattern Recognition Letters, 53:44-52, 2015. [17] A.J. Booker, J.E. Dennis Jr., P.D. Frank, D.B. Serafini, V. Torczon, and M.W. Trosset, A Rigorous Framework for Optimization of Expensive Functions by Surrogates, Structural Optimisation, 17:1-13, 1999. [18] Y. Jin, Surrogate-assisted evolutionary computation: Recent advances and future challenges, Swarm and

Open access
An Artificial Potential Field Based Mobile Robot Navigation Method To Prevent From Deadlock

References [1] Hwang, Y. K.; Ahuja, N., “Gross motion planning: a survey,” ACM Computing Surveys (CSUR) vol. 24, no.3, 1992, pp.219-291. [2] Sridharan, K.; Priya T. K., “A parallel algorithm for constructing reduced visibility graph and its FPGA implementation.” Journal of Systems Architecture, vol. 50, no.10, 2004, pp.635-644. [3] Bhattacharya, P.; Gavrilova, M. L., “Roadmap-based path planning-Using the Voronoi diagram for a clearance-based shortest path,” Robotics & Automation Magazine, IEEE, vol.15, no.2, 2008, pp.58-66. [4] Garrido, S

Open access
Agent-Based Dispatching Enables Autonomous Groupage Traffic

References [1] M. Gendreau, “Vehicle Routing Problem with TimeWindows, Part I: Route Construction and Local Search Algorithms,” Transportation Science, vol. 39, no. 1, pp. 104-118, 2005. [2] M. Gendreau and O. Brysy, “Vehicle Routing Problem with Time Windows, Part II: Metaheuristics,” Transportation Science, vol. 39, pp. 119-139, 2005. [3] O. Brysy, W. Dullaert, and M. Gendreau, “Evolutionary Algorithms for the Vehicle Routing Problem with Time Windows,” J. Heuristics, vol. 10, pp. 587-611, 2004

Open access