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Kutulama Problemi için Geliştirilmiş Karınca Aslanı Optimizasyonu Algoritması

Year 2018, Volume: 11 Issue: 2, 13 - 19, 15.11.2018

Uğur Yüzgeç Haydar Kılıç

Abstract

Bu çalışmada kutulama problemi için bir geliştirilmiş
karınca aslanı optimizasyon algoritması (GKAO) önerilmiştir. Karınca aslanı
optimizasyon algoritması (KAO) temel olarak karınca aslanlarının avlanma
stratejilerini taklit eden bir meta-sezgisel optimizasyon algoritmasıdır. KAO
algoritmasının en büyük handikaplarından birisi uzun çalışma süresidir. KAO
yapısında yer alan rastgele karınca yürüyüşü modeli ve seçim yönteminde yapılan
iyileştirmelerle ortaya çıkarılan GKAO bu handikabı ortadan kaldırmıştır.
Önerilen GKAO algoritması kutulama problemi olarak adlandırılan optimizasyon
problemine uyarlanarak test edilmiştir. Önerilen algoritma parçacık sürüsü
optimizasyon algoritması (PSO), ateş böceği algoritması (FA), istilacı yabani
ot optimizasyon algoritması (IWO) ve karınca aslanı optimizasyon algoritması (KAO)
ile karşılaştırılmıştır. Sonuçlar önerilen GKAO algoritma performansının kullanılan
meta-sezgisel algoritma performanslarından daha başarılı olduğunu göstermiştir. 

Keywords

Karınca Aslanı Algoritması Optimizasyon Kutulama Problemi

References

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  • Z. Beheshti and S. M. H. Shamsuddin, “A review of population-based meta-heuristic algorithm,” International Journal of Advances in Soft Computing and its Applications, vol. 5, no. 1, pp. 1-35, 2013.
  • M. H. N. Tayarani, X. Yao, and H. Xu, “Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey,” IEEE Trans. Evol. Comput., vol. 19, no. 5, pp. 609–629, 2015.
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  • D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, vol. Addison-Wesley, 1989.
  • R. Storn and K. Price, “Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces,” J. Glob. Optimization, vol. 11, no. 4, pp. 341–359, 1997.
  • K. V Price, R. M. Storn, and J. A. Lampinen, Differential Evolution: A Practical Approach to Global Optimization, vol. 28. 2005.
  • S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by Simulated Annealing,” Science (80-.)., vol. 220, no. 4598, pp. 671–680, 1983.
  • F. Glover, “Future paths for integer programming and links to artificial intelligence,” Comput. Oper. Res., vol. 13, no. 5, pp. 533–549, 1986.
  • Zong Woo Geem, Joong Hoon Kim, and G. V. Loganathan, “A New Heuristic Optimization Algorithm: Harmony Search,” Simulation, vol. 76, no. 2, pp. 60–68, 2001.
  • X. S. Yang, “Harmony search as a metaheuristic algorithm,” Studies in Computational Intelligence, vol. 191. pp. 1–14, 2009.
  • M. Eusuff, K. Lansey, and F. Pasha, “Shuffled frog-leaping algorithm: A memetic meta-heuristic for discrete optimization,” Eng. Optim., vol. 38, no. 2, pp. 129–154, 2006.
  • K. K. Bhattacharjee and S. P. Sarmah, “Shuffled frog leaping algorithm and its application to 0/1 knapsack problem,” Appl. Soft Comput. J., vol. 19, pp. 252–263, 2014.
  • J. Kennedy and R. Eberhart, “Particle swarm optimization,” Neural Networks, 1995. Proceedings., IEEE Int. Conf., vol. 4, pp. 1942–1948 vol.4, 1995.
  • R.Eberhart, J.Kennedy, “A new optimizer using particle swarm theory”, in:Sixth International Symposium on Micro Machine and Human Science, MHS, 1995,pp.39–43.
  • D. Karaboga and C. Ozturk, “A novel clustering approach: Artificial Bee Colony (ABC) algorithm,” Appl. Soft Comput. J., vol. 11, no. 1, pp. 652–657, 2011.
  • D. Karaboga, “An idea based on honey bee swarm for numerical optimization,” Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, Tech. Rep., 2005.
  • M. Dorigo, V. Maniezzo, and A. Colorni, “Ant system: Optimization by a colony of cooperating agents,” IEEE Trans. Syst. Man, Cybern. Part B Cybern., vol. 26, no. 1, pp. 29–41, 1996.
  • M. Dorigo, M. Birattari, and T. Stutzle, “Ant colony optimization,” IEEE Comput. Intell. Mag., vol. 1, no. 4, pp. 28–39, 2006.
  • D. Dasgupta, “Artificial Immune Systems and their Applications”, Springer-Verlag, 1999, ISBN3540643907.
  • L.N. de Charsto, J. Timmis, “An Introduction to Artificial Immune Systems: A New Computational Intelligence Paradigm”, Springer-Verlag, 2002.
  • K. Passino, “Biomimicry of bacterial foraging for distributed optimization and control”, IEEE Control Syst. Mag., vol. 22, no.3, pp.52–67, 2002.
  • S. Mishra, “A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation”, IEEE Trans. Evol. Comput., vol. 9, no.1, pp.61–73, 2005.
  • S. Mirjalili, “The ant lion optimizer,” Adv. Eng. Softw., vol. 83, pp. 80–98, 2015.
  • More Raju, Lalit Chandra Saikia, Nidul Sinha, "Automatic generation control of a multi-area system using ant lion optimizer algorithm based PID plus second order derivative controller", International Journal of Electrical Power and Energy Systems, Volume 80, September 2016, Pages 52-63, ISSN 0142-0615
  • Satheeshkumar, R., Shivakumar, R. "Ant Lion Optimization Approach for Load Frequency Control of Multi-Area Interconnected Power Systems", Circuits and Systems, 7(09), 2357, 2016.
  • Kamboj, V. K., Bhadoria, A., Bath, S. K., "Solution of non-convex economic load dispatch problem for small-scale power systems using ant lion optimizer", Neural Computing and Applications, 1-12, 2016.
  • Nischal, M. M., Mehta, S., "Optimal load dispatch using ant lion optimization", Int J Eng Res Appl, 5(8), 10-19, 2015.
  • Yao, P., Wang, H., "Dynamic Adaptive Ant Lion Optimizer applied to route planning for unmanned aerial vehicle", Soft Computing, 1-14, 2016.
  • Petrovic, M., Petronijevic, J., Mitic, M., Vukovic, N., Plemic, A., Miljkovic, Z., Babic, B., "The ant lion optimization algorithm for flexible process planning. JPE, 18(2), 65-68, 2015.
  • N. Chopra and S. Mehta, "Multi-objective optimum generation scheduling using Ant Lion Optimization," 2015 Annual IEEE India Conference (INDICON), New Delhi, 2015, pp. 1-6.
  • Gupta, E., Saxena, A., "Performance Evaluation of Antlion Optimizer Based Regulator in Automatic Generation Control of Interconnected Power System", Journal of Engineering, 2016.
  • Babers, R., Ghali, N. I., Hassanien, A. E., Madbouly, N. M., "Optimal community detection approach based on Ant Lion Optimization", In Computer Engineering Conference (ICENCO), 2015, 11th International (pp. 284-289). IEEE.
  • Nair, S. S., Rana, K. P. S., Kumar, V., Chawla, A., "Efficient Modeling of Linear Discrete Filters Using Ant Lion Optimizer", Circuits, Systems, and Signal Processing, 1-34, 2016.
  • Rebecca, N., Shin, M., MH, S., Zuriani, M., "Ant Lion Optimizer for Optimal Reactive Power Dispatch Solution", Journal of Electrical Systems, (3), 67-74, 2015.
  • Martinez-Sykora, A., Alvarez-Valdes, R., Bennell, J. A., Ruiz, R. and Tamarit, J. M., “Metaheuristics for the irregular bin packing problem with free rotations,” Eur. J. Oper. Res., vol. 258, no. 2, pp. 440–455, 2017.
  • Christensen, H. I., Khan, A., Pokutta, S. and Tetali, P., “Approximation and online algorithms for multidimensional bin packing: A survey,” Computer Science Review, vol. 24. pp. 63–79, 2017.
  • Yarpiz, Bin Packing Problem using GA, PSO, FA, and IWO.http://yarpiz.com/363/ypap105-bin-packing-problem, Accessed 20 April 2018.

Year 2018, Volume: 11 Issue: 2, 13 - 19, 15.11.2018

Uğur Yüzgeç Haydar Kılıç

Abstract

References

  • S. J. Nanda and G. Panda, “A survey on nature inspired metaheuristic algorithms for partitional clustering,” Swarm and Evolutionary Computation, vol. 16. pp. 1–18, 2014.
  • Z. Beheshti and S. M. H. Shamsuddin, “A review of population-based meta-heuristic algorithm,” International Journal of Advances in Soft Computing and its Applications, vol. 5, no. 1, pp. 1-35, 2013.
  • M. H. N. Tayarani, X. Yao, and H. Xu, “Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey,” IEEE Trans. Evol. Comput., vol. 19, no. 5, pp. 609–629, 2015.
  • J. H. Holland, “Adaptation in Natural and Artificial Systems,” Ann Arbor MI Univ. Michigan Press, vol. Ann Arbor, p. 183, 1975.
  • D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, vol. Addison-Wesley, 1989.
  • R. Storn and K. Price, “Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces,” J. Glob. Optimization, vol. 11, no. 4, pp. 341–359, 1997.
  • K. V Price, R. M. Storn, and J. A. Lampinen, Differential Evolution: A Practical Approach to Global Optimization, vol. 28. 2005.
  • S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by Simulated Annealing,” Science (80-.)., vol. 220, no. 4598, pp. 671–680, 1983.
  • F. Glover, “Future paths for integer programming and links to artificial intelligence,” Comput. Oper. Res., vol. 13, no. 5, pp. 533–549, 1986.
  • Zong Woo Geem, Joong Hoon Kim, and G. V. Loganathan, “A New Heuristic Optimization Algorithm: Harmony Search,” Simulation, vol. 76, no. 2, pp. 60–68, 2001.
  • X. S. Yang, “Harmony search as a metaheuristic algorithm,” Studies in Computational Intelligence, vol. 191. pp. 1–14, 2009.
  • M. Eusuff, K. Lansey, and F. Pasha, “Shuffled frog-leaping algorithm: A memetic meta-heuristic for discrete optimization,” Eng. Optim., vol. 38, no. 2, pp. 129–154, 2006.
  • K. K. Bhattacharjee and S. P. Sarmah, “Shuffled frog leaping algorithm and its application to 0/1 knapsack problem,” Appl. Soft Comput. J., vol. 19, pp. 252–263, 2014.
  • J. Kennedy and R. Eberhart, “Particle swarm optimization,” Neural Networks, 1995. Proceedings., IEEE Int. Conf., vol. 4, pp. 1942–1948 vol.4, 1995.
  • R.Eberhart, J.Kennedy, “A new optimizer using particle swarm theory”, in:Sixth International Symposium on Micro Machine and Human Science, MHS, 1995,pp.39–43.
  • D. Karaboga and C. Ozturk, “A novel clustering approach: Artificial Bee Colony (ABC) algorithm,” Appl. Soft Comput. J., vol. 11, no. 1, pp. 652–657, 2011.
  • D. Karaboga, “An idea based on honey bee swarm for numerical optimization,” Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, Tech. Rep., 2005.
  • M. Dorigo, V. Maniezzo, and A. Colorni, “Ant system: Optimization by a colony of cooperating agents,” IEEE Trans. Syst. Man, Cybern. Part B Cybern., vol. 26, no. 1, pp. 29–41, 1996.
  • M. Dorigo, M. Birattari, and T. Stutzle, “Ant colony optimization,” IEEE Comput. Intell. Mag., vol. 1, no. 4, pp. 28–39, 2006.
  • D. Dasgupta, “Artificial Immune Systems and their Applications”, Springer-Verlag, 1999, ISBN3540643907.
  • L.N. de Charsto, J. Timmis, “An Introduction to Artificial Immune Systems: A New Computational Intelligence Paradigm”, Springer-Verlag, 2002.
  • K. Passino, “Biomimicry of bacterial foraging for distributed optimization and control”, IEEE Control Syst. Mag., vol. 22, no.3, pp.52–67, 2002.
  • S. Mishra, “A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation”, IEEE Trans. Evol. Comput., vol. 9, no.1, pp.61–73, 2005.
  • S. Mirjalili, “The ant lion optimizer,” Adv. Eng. Softw., vol. 83, pp. 80–98, 2015.
  • More Raju, Lalit Chandra Saikia, Nidul Sinha, "Automatic generation control of a multi-area system using ant lion optimizer algorithm based PID plus second order derivative controller", International Journal of Electrical Power and Energy Systems, Volume 80, September 2016, Pages 52-63, ISSN 0142-0615
  • Satheeshkumar, R., Shivakumar, R. "Ant Lion Optimization Approach for Load Frequency Control of Multi-Area Interconnected Power Systems", Circuits and Systems, 7(09), 2357, 2016.
  • Kamboj, V. K., Bhadoria, A., Bath, S. K., "Solution of non-convex economic load dispatch problem for small-scale power systems using ant lion optimizer", Neural Computing and Applications, 1-12, 2016.
  • Nischal, M. M., Mehta, S., "Optimal load dispatch using ant lion optimization", Int J Eng Res Appl, 5(8), 10-19, 2015.
  • Yao, P., Wang, H., "Dynamic Adaptive Ant Lion Optimizer applied to route planning for unmanned aerial vehicle", Soft Computing, 1-14, 2016.
  • Petrovic, M., Petronijevic, J., Mitic, M., Vukovic, N., Plemic, A., Miljkovic, Z., Babic, B., "The ant lion optimization algorithm for flexible process planning. JPE, 18(2), 65-68, 2015.
  • N. Chopra and S. Mehta, "Multi-objective optimum generation scheduling using Ant Lion Optimization," 2015 Annual IEEE India Conference (INDICON), New Delhi, 2015, pp. 1-6.
  • Gupta, E., Saxena, A., "Performance Evaluation of Antlion Optimizer Based Regulator in Automatic Generation Control of Interconnected Power System", Journal of Engineering, 2016.
  • Babers, R., Ghali, N. I., Hassanien, A. E., Madbouly, N. M., "Optimal community detection approach based on Ant Lion Optimization", In Computer Engineering Conference (ICENCO), 2015, 11th International (pp. 284-289). IEEE.
  • Nair, S. S., Rana, K. P. S., Kumar, V., Chawla, A., "Efficient Modeling of Linear Discrete Filters Using Ant Lion Optimizer", Circuits, Systems, and Signal Processing, 1-34, 2016.
  • Rebecca, N., Shin, M., MH, S., Zuriani, M., "Ant Lion Optimizer for Optimal Reactive Power Dispatch Solution", Journal of Electrical Systems, (3), 67-74, 2015.
  • Martinez-Sykora, A., Alvarez-Valdes, R., Bennell, J. A., Ruiz, R. and Tamarit, J. M., “Metaheuristics for the irregular bin packing problem with free rotations,” Eur. J. Oper. Res., vol. 258, no. 2, pp. 440–455, 2017.
  • Christensen, H. I., Khan, A., Pokutta, S. and Tetali, P., “Approximation and online algorithms for multidimensional bin packing: A survey,” Computer Science Review, vol. 24. pp. 63–79, 2017.
  • Yarpiz, Bin Packing Problem using GA, PSO, FA, and IWO.http://yarpiz.com/363/ypap105-bin-packing-problem, Accessed 20 April 2018.
There are 38 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Makaleler(Araştırma)
Authors

Uğur Yüzgeç

Haydar Kılıç This is me

Publication Date November 15, 2018
Published in Issue Year 2018 Volume: 11 Issue: 2

Cite

APA Yüzgeç, U., & Kılıç, H. (2018). Kutulama Problemi için Geliştirilmiş Karınca Aslanı Optimizasyonu Algoritması. Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi, 11(2), 13-19.
AMA Yüzgeç U, Kılıç H. Kutulama Problemi için Geliştirilmiş Karınca Aslanı Optimizasyonu Algoritması. TBV-BBMD. November 2018;11(2):13-19.
Chicago Yüzgeç, Uğur, and Haydar Kılıç. “Kutulama Problemi için Geliştirilmiş Karınca Aslanı Optimizasyonu Algoritması”. Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi 11, no. 2 (November 2018): 13-19.
EndNote Yüzgeç U, Kılıç H (November 1, 2018) Kutulama Problemi için Geliştirilmiş Karınca Aslanı Optimizasyonu Algoritması. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi 11 2 13–19.
IEEE U. Yüzgeç and H. Kılıç, “Kutulama Problemi için Geliştirilmiş Karınca Aslanı Optimizasyonu Algoritması”, TBV-BBMD, vol. 11, no. 2, pp. 13–19, 2018.
ISNAD Yüzgeç, Uğur - Kılıç, Haydar. “Kutulama Problemi için Geliştirilmiş Karınca Aslanı Optimizasyonu Algoritması”. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi 11/2 (November 2018), 13-19.
JAMA Yüzgeç U, Kılıç H. Kutulama Problemi için Geliştirilmiş Karınca Aslanı Optimizasyonu Algoritması. TBV-BBMD. 2018;11:13–19.
MLA Yüzgeç, Uğur and Haydar Kılıç. “Kutulama Problemi için Geliştirilmiş Karınca Aslanı Optimizasyonu Algoritması”. Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi, vol. 11, no. 2, 2018, pp. 13-19.
Vancouver Yüzgeç U, Kılıç H. Kutulama Problemi için Geliştirilmiş Karınca Aslanı Optimizasyonu Algoritması. TBV-BBMD. 2018;11(2):13-9.
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