BibTex RIS Cite

Web Based Educational Tool for Metaheuristic Algorithms

Year 2014, Volume: 20 Issue: 2, 46 - 53, 01.02.2014

Abstract

Metaheuristic optimization algorithms are nowadays being employed to solve a wide variety of optimization problems. These algorithms are not based on mathematical evidence and have mostly been developed by imitation of natural phenomenon. In this study, a web-based educational metaheuristics testing tool was developed. With this tool, the users are able to test Artificial Immune System and Artificial Bee Colony algorithms on Benchmark functions, observe the results of optimization by modifying the parameters for each algorithm, and at the same time perform optimization procedures by typing their own functions with their own constraints. In addition, information on the working steps of both algorithms are provided in the application.

References

  • Paparrizos, V. K., Samaras, N. and Sifaleras, A., “Visual LinProg: A Web-based Educational Software for Linear Programming”, Computer Application in Engineering Education, 15 (1), pp. 1-14, 2007.
  • Valdez, F., Melin, P. and Castillo, O., “Toolbox for Bio- Inspired Optimization of Mathematical Functions”, Computer Application In Engineering Education, 2011.
  • Beres, K., “Distance learning, heuristic model of education and alternative energy sources with liquid battery”, Technics Technologies Education Management, 7 (3), pp. 1418-1426, 2012.
  • De Castro, L. N. and Zuben, F. J. V., “Artificial Immune Systems: Part-II A Survey of Applications”, Technical Report, 2000.
  • Karaboğa, D., “An idea based on honey bee swarm for numerical optimization”, Technical Report TR06, Erciyes University, Turkey, pp. 1-6, 2005.
  • Deng-xu, H., Rui-min, J., “Cloud model-based Artificial Bee
  • Linh, N. T. and Anh, N. Q., “Application artificial bee colony algorithm (ABC) for reconfiguring distribution network”, Second International Conference on Computer Modeling and Simulation, 1, pp. 102-106, 2010.
  • Kang, F., Li, J. and Ma, Z., “Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions”, pp. 3508-3531, 2011. Sciences, 181 (16),
  • Molga, M. and Smutnicki, C., “Test functions for optimization needs”, http://www.zsd.ict.pwr.wroc.pl/ files/docs/functions.pdf, 2005.
  • Ökdem, S., Karaboğa, D., “Gerçek Zamanlı Optimizasyon İçin Gelişime Dayalı Hızlı Bir Algoritma”, 2005.
  • Broeck, G. V. D. and Driessens, K., “Automatic Discretization of Actions and States in Monte-Carlo Tree Search”, International Workshop on Machine Learning and Data Mining in and around Games (DMLG). 2nd Ed., Athens, pp. 1-12, 2011.
  • Karaboğa, D. and Akay, B., “A Survey: Algorithms Simulating Bee Swarm Intelligence”, Springer, 31 (1-4), pp. 61-85, 2010.
  • Yang, L., Boxue, T. and Xue, Z., “Position Accuracy Improvement of PMLSM System Based on Artificial Immune Algorithm”, Proceedings of the 26th Chinese Control pp. 3679-3683, 2007. Zhangjiajie, Hunan, China,
  • Gao, W., Liu, S. and Huang, L., “Global best artificial bee colony algorithm for global optimization”, Journal of Computational and Applied Mathematics, 236 (11), pp. 2741-2753, 2012.
  • Engin, O. and Döyen, A., “Artifical Immune Systems And Applıcatıons In Industrial Problems”, G.U. Journal of Science, 17 (1), pp. 71-84, 2004.
  • De Castro, L. N. and Timmis, J., “Artificial Immune Systems: A novel paradigm to pattern recognition”, Artificial Neural Networks in Patttern Recognition, 2, pp. 67-84, 2002.
  • De Castro, L. N., and Von Zuben, F. J., “The Clonal Selection Algorithm with Engineering Applications”, Workshop on Artificial Immune Systems and Their Applications. Las Vegas, USA, pp. 36-37, 2000.
  • Karaboğa, D., “Yapay Zeka Optimizasyon Algoritmaları”, Istanbul, Atlas Press, 2004.
  • Castro, L. N. and Zuben, J. V., “Learning and Optimization Using the Clonal Selection Principle”, IEEE Transactions on Evolutionary Computation, 6 (3), pp. 239-251, 2002.
  • Mendez, J. A., Lorenzo, C., Acosta, L., Torres, S. and Gonzales, E., “A Web-Based Tool for Control Engineering Teaching”, Computer Application In Engineering Education, 14 (3), pp. 178-187, 2006. Colony Algorithm’s Application in The Logistics Location Problem”, Management and Industrial Engineering (ICIII), 2012 International Conference on, pp. 256-259, 2012.
  • Bi, X., Wang, Y., “An Improved Artificial Bee Colony Algorithm”, Computer Research and Development (ICCRD), 2011 3rd International Conference on, pp. 174-177, 2011.

Web Based Educational Tool for Metaheuristic Algorithms

Year 2014, Volume: 20 Issue: 2, 46 - 53, 01.02.2014

Abstract

Metaheuristic optimization algorithms are nowadays being employed to solve a wide variety of optimization problems. These algorithms are not based on mathematical evidence and have mostly been developed by imitation of natural phenomenon. In this study, a web-based educational metaheuristics testing tool was developed. With this tool, the users are able to test Artificial Immune System and Artificial Bee Colony algorithms on Benchmark functions, observe the results of optimization by modifying the parameters for each algorithm, and at the same time perform optimization procedures by typing their own functions with their own constraints. In addition, information on the working steps of both algorithms are provided in the application.

References

  • Paparrizos, V. K., Samaras, N. and Sifaleras, A., “Visual LinProg: A Web-based Educational Software for Linear Programming”, Computer Application in Engineering Education, 15 (1), pp. 1-14, 2007.
  • Valdez, F., Melin, P. and Castillo, O., “Toolbox for Bio- Inspired Optimization of Mathematical Functions”, Computer Application In Engineering Education, 2011.
  • Beres, K., “Distance learning, heuristic model of education and alternative energy sources with liquid battery”, Technics Technologies Education Management, 7 (3), pp. 1418-1426, 2012.
  • De Castro, L. N. and Zuben, F. J. V., “Artificial Immune Systems: Part-II A Survey of Applications”, Technical Report, 2000.
  • Karaboğa, D., “An idea based on honey bee swarm for numerical optimization”, Technical Report TR06, Erciyes University, Turkey, pp. 1-6, 2005.
  • Deng-xu, H., Rui-min, J., “Cloud model-based Artificial Bee
  • Linh, N. T. and Anh, N. Q., “Application artificial bee colony algorithm (ABC) for reconfiguring distribution network”, Second International Conference on Computer Modeling and Simulation, 1, pp. 102-106, 2010.
  • Kang, F., Li, J. and Ma, Z., “Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions”, pp. 3508-3531, 2011. Sciences, 181 (16),
  • Molga, M. and Smutnicki, C., “Test functions for optimization needs”, http://www.zsd.ict.pwr.wroc.pl/ files/docs/functions.pdf, 2005.
  • Ökdem, S., Karaboğa, D., “Gerçek Zamanlı Optimizasyon İçin Gelişime Dayalı Hızlı Bir Algoritma”, 2005.
  • Broeck, G. V. D. and Driessens, K., “Automatic Discretization of Actions and States in Monte-Carlo Tree Search”, International Workshop on Machine Learning and Data Mining in and around Games (DMLG). 2nd Ed., Athens, pp. 1-12, 2011.
  • Karaboğa, D. and Akay, B., “A Survey: Algorithms Simulating Bee Swarm Intelligence”, Springer, 31 (1-4), pp. 61-85, 2010.
  • Yang, L., Boxue, T. and Xue, Z., “Position Accuracy Improvement of PMLSM System Based on Artificial Immune Algorithm”, Proceedings of the 26th Chinese Control pp. 3679-3683, 2007. Zhangjiajie, Hunan, China,
  • Gao, W., Liu, S. and Huang, L., “Global best artificial bee colony algorithm for global optimization”, Journal of Computational and Applied Mathematics, 236 (11), pp. 2741-2753, 2012.
  • Engin, O. and Döyen, A., “Artifical Immune Systems And Applıcatıons In Industrial Problems”, G.U. Journal of Science, 17 (1), pp. 71-84, 2004.
  • De Castro, L. N. and Timmis, J., “Artificial Immune Systems: A novel paradigm to pattern recognition”, Artificial Neural Networks in Patttern Recognition, 2, pp. 67-84, 2002.
  • De Castro, L. N., and Von Zuben, F. J., “The Clonal Selection Algorithm with Engineering Applications”, Workshop on Artificial Immune Systems and Their Applications. Las Vegas, USA, pp. 36-37, 2000.
  • Karaboğa, D., “Yapay Zeka Optimizasyon Algoritmaları”, Istanbul, Atlas Press, 2004.
  • Castro, L. N. and Zuben, J. V., “Learning and Optimization Using the Clonal Selection Principle”, IEEE Transactions on Evolutionary Computation, 6 (3), pp. 239-251, 2002.
  • Mendez, J. A., Lorenzo, C., Acosta, L., Torres, S. and Gonzales, E., “A Web-Based Tool for Control Engineering Teaching”, Computer Application In Engineering Education, 14 (3), pp. 178-187, 2006. Colony Algorithm’s Application in The Logistics Location Problem”, Management and Industrial Engineering (ICIII), 2012 International Conference on, pp. 256-259, 2012.
  • Bi, X., Wang, Y., “An Improved Artificial Bee Colony Algorithm”, Computer Research and Development (ICCRD), 2011 3rd International Conference on, pp. 174-177, 2011.
There are 21 citations in total.

Details

Primary Language Turkish
Journal Section Research Article
Authors

Güncel Sarıman This is me

Ecir Uğur Küçüksille This is me

Publication Date February 1, 2014
Published in Issue Year 2014 Volume: 20 Issue: 2

Cite

APA Sarıman, G. ., & Küçüksille, E. U. . (2014). Web Based Educational Tool for Metaheuristic Algorithms. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 20(2), 46-53. https://doi.org/10.5505/pajes.2014.15870
AMA Sarıman G, Küçüksille EU. Web Based Educational Tool for Metaheuristic Algorithms. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. February 2014;20(2):46-53. doi:10.5505/pajes.2014.15870
Chicago Sarıman, Güncel, and Ecir Uğur Küçüksille. “Web Based Educational Tool for Metaheuristic Algorithms”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 20, no. 2 (February 2014): 46-53. https://doi.org/10.5505/pajes.2014.15870.
EndNote Sarıman G, Küçüksille EU (February 1, 2014) Web Based Educational Tool for Metaheuristic Algorithms. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 20 2 46–53.
IEEE G. . Sarıman and E. U. . Küçüksille, “Web Based Educational Tool for Metaheuristic Algorithms”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol. 20, no. 2, pp. 46–53, 2014, doi: 10.5505/pajes.2014.15870.
ISNAD Sarıman, Güncel - Küçüksille, Ecir Uğur. “Web Based Educational Tool for Metaheuristic Algorithms”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 20/2 (February 2014), 46-53. https://doi.org/10.5505/pajes.2014.15870.
JAMA Sarıman G, Küçüksille EU. Web Based Educational Tool for Metaheuristic Algorithms. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2014;20:46–53.
MLA Sarıman, Güncel and Ecir Uğur Küçüksille. “Web Based Educational Tool for Metaheuristic Algorithms”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol. 20, no. 2, 2014, pp. 46-53, doi:10.5505/pajes.2014.15870.
Vancouver Sarıman G, Küçüksille EU. Web Based Educational Tool for Metaheuristic Algorithms. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2014;20(2):46-53.





Creative Commons Lisansı
Bu dergi Creative Commons Al 4.0 Uluslararası Lisansı ile lisanslanmıştır.