Büyük ölçekli optimizasyon problemleri için seçime dayalı yerel arama mekanizmasına sahip ikili Jaya algoritması

Yıl 2023, , 2435 - 2450, 12.04.2023

Ahmet Özkış , Murat Karakoyun

https://doi.org/10.17341/gazimmfd.1111302

Cited By: 1

Öz

Jaya, yakın zamanda sürekli optimizasyon problemlerinin çözümü için önerilen popülasyon tabanlı metasezgisel bir algoritmadır. Literatürde ikili optimizasyon problemlerinin çözümü için çeşitli Jaya varyantları geliştirilmiştir. Bunlardan biri olan JayaX-LSM algoritması CAP problemlerinin çözümünde kullanılmış ve başarılı sonuçlar üretmiştir. Ancak CAP problemlerinden daha yüksek boyutlu ve kompleks bir yapıya sahip olan M* problemleri üzerinde test ettiğimizde algoritmanın oldukça başarısız sonuçlar ürettiği görülmüştür. Bu çalışmada, ikili optimizasyon problemlerinde çözüm uzayının etkili bir şekilde aranmasını sağlayan yeni bir yerel arama modülü (ELSM) geliştirilmiştir. Bu modül ikili JayaX algoritmasına eklenerek JayaX-ELSM algoritması önerilmiştir. Önerilen JayaX-ELSM algoritmasının performansı öncelikle JayaX-LSM algoritmasıyla CAP ve M* problem setleri üzerinde karşılaştırmalı olarak analiz edilmiştir. Daha sonra, önerilen algoritma, literatürde yakın zamanda yayınlanmış toplam 11 farklı algoritmayla performans karşılaştırmasına tabi tutulmuştur. Elde edilen sonuçlar, önerilen JayaX-ELSM'nin JayaX-LSM algoritmasının CAP problemlerinde sergilediği performansı devam ettirdiğini, M* problemlerinde de JayaX-LSM'den çok daha başarılı sonuçlar ürettiğini göstermektedir. Ayrıca önerilen algoritmanın M* problemleri üzerindeki performansının, diğer algoritmalarla karşılaştırıldığında rekabetçi ve ümit verici olduğu gözlenmiştir.

Anahtar Kelimeler

Jaya algoritması, İkili optimizasyon problemleri, kapasitesiz tesis yerleştirme problemleri, Yerel arama mekanizması, Metasezgisel algoritmalar

Kaynakça

• 1. Murty, K., Optimization models for decision making. 2003.
• 2. Gould, N., An introduction to algorithms for continuous optimization. 2006, Oxford University Computing Laboratory Notes.
• 3. Yuan, X., et al., An improved binary particle swarm optimization for unit commitment problem. Expert Systems with applications, 2009. 36(4): p. 8049-8055.
• 4. He, Y., et al., Novel binary differential evolution algorithm based on Taper-shaped transfer functions for binary optimization problems. Swarm and Evolutionary Computation, 2021: p. 101022.
• 5. Hakli, H., BinEHO: a new binary variant based on elephant herding optimization algorithm. Neural Computing and Applications, 2020. 32(22): p. 16971-16991.
• 6. Sahinkoc, H.M. and Ü. Bilge, A reference set based many-objective co-evolutionary algorithm with an application to the knapsack problem. European Journal of Operational Research, 2021.
• 7. Tongur, V. and E. Ülker, Migrating Birds Optimization (MBO) Algorithm to Solve Graph Coloring Problem. International Journal of Engineering Science, 2017. 14545.
• 8. Aslan, M. and N.A. Baykan, A performance comparison of graph coloring algorithms. International Journal of Intelligent Systems and Applications in Engineering, 2016: p. 1-7.
• 9. Ibrahim, I.M., Task scheduling algorithms in cloud computing: A review. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021. 12(4): p. 1041-1053.
• 10. Abualigah, L. and A. Diabat, A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments. Cluster Computing, 2021. 24(1): p. 205-223.
• 11. Inan, O., M.S. Uzer, and N. Yılmaz, A new hybrid feature selection method based on association rules and PCA for detection of breast cancer. International Journal of Innovative Computing, Information and Control, 2013. 9(2): p. 727-729.
• 12. Dhiman, G., et al., BEPO: a novel binary emperor penguin optimizer for automatic feature selection. Knowledge-Based Systems, 2021. 211: p. 106560.
• 13. Baş, E. and E. Ülker, A binary social spider algorithm for uncapacitated facility location problem. Expert Systems with Applications, 2020. 161: p. 113618.
• 14. Cinar, A.C. and M.S. Kiran, Similarity and logic gate-based tree-seed algorithms for binary optimization. Computers & Industrial Engineering, 2018. 115: p. 631-646.
• 15. Sbihi, A., Adaptive perturbed neighbourhood search for the expanding capacity multiple-choice knapsack problem. Journal of the Operational Research Society, 2013. 64(10): p. 1461-1473.
• 16. Ghezelsoflu, A., et al., A set-covering formulation for a drayage problem with single and double container loads. Journal of Industrial Engineering International, 2018. 14(4): p. 665-676.
• 17. Rizk-Allah, R.M. and A.E. Hassanien, New binary bat algorithm for solving 0–1 knapsack problem. Complex & Intelligent Systems, 2018. 4(1): p. 31-53.
• 18. Banitalebi, A., M.I. Abd Aziz, and Z.A. Aziz, A self-adaptive binary differential evolution algorithm for large scale binary optimization problems. Information Sciences, 2016. 367: p. 487-511.
• 19. Rao, R., Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 2016. 7(1): p. 19-34.
• 20. Aslan, M., M. Gunduz, and M.S. Kiran, JayaX: Jaya algorithm with xor operator for binary optimization. Applied Soft Computing, 2019. 82: p. 105576.
• 21. Eberhart, R. and J. Kennedy. A new optimizer using particle swarm theory. in MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science. 1995. Ieee.
• 22. Kennedy, J. and R.C. Eberhart. A discrete binary version of the particle swarm algorithm. in 1997 IEEE International conference on systems, man, and cybernetics. Computational cybernetics and simulation. 1997. IEEE.
• 23. Khanesar, M.A., M. Teshnehlab, and M.A. Shoorehdeli. A novel binary particle swarm optimization. in 2007 Mediterranean conference on control & automation. 2007. IEEE.
• 24. Beheshti, Z., S.M. Shamsuddin, and S. Hasan, Memetic binary particle swarm optimization for discrete optimization problems. Information Sciences, 2015. 299: p. 58-84.
• 25. Guner, A.R. and M. Sevkli, A discrete particle swarm optimization algorithm for uncapacitated facility location problem. Journal of Artificial Evolution and Applications, 2008. 2008.
• 26. Nezamabadi-pour, H., M. Rostami-Shahrbabaki, and M. Maghfoori-Farsangi, Binary particle swarm optimization: challenges and new solutions. CSI J Comput Sci Eng, 2008. 6(1): p. 21-32.
• 27. Saha, S., A. Kole, and K. Dey. A modified continuous particle swarm optimization algorithm for uncapacitated facility location problem. in International Conference on Advances in Information Technology and Mobile Communication. 2011. Springer.
• 28. Storn, R. and K. Price, Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 1997. 11(4): p. 341-359.
• 29. Pampara, G., A.P. Engelbrecht, and N. Franken. Binary differential evolution. in 2006 IEEE International Conference on Evolutionary Computation. 2006. IEEE.
• 30. Engelbrecht, A.P. and G. Pampara. Binary differential evolution strategies. in 2007 IEEE congress on evolutionary computation. 2007. IEEE.
• 31. Su, H. and Y. Yang. Quantum-inspired differential evolution for binary optimization. in 2008 Fourth International Conference on Natural Computation. 2008. IEEE.
• 32. Chen, Y., W. Xie, and X. Zou, A binary differential evolution algorithm learning from explored solutions. Neurocomputing, 2015. 149: p. 1038-1047.
• 33. He, X., et al. Feature selection with discrete binary differential evolution. in 2009 international conference on artificial intelligence and computational intelligence. 2009. IEEE.
• 34. Deng, C., et al. Novel binary differential evolution algorithm for discrete optimization. in 2009 Fifth International Conference on Natural Computation. 2009. IEEE.
• 35. Yang, Q. A comparative study of discrete differential evolution on binary constraint satisfaction problems. in 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence). 2008. IEEE. 36. Wang, L., et al., A modified binary differential evolution algorithm, in Life System Modeling and Intelligent Computing. 2010, Springer. p. 49-57.
• 37. Kashan, M.H., A.H. Kashan, and N. Nahavandi, A novel differential evolution algorithm for binary optimization. Computational Optimization and Applications, 2013. 55(2): p. 481-513.
• 38. Karaboga, D., An idea based on honey bee swarm for numerical optimization. 2005, Technical report-tr06, Erciyes university, engineering faculty, computer ….
• 39. Kashan, M.H., N. Nahavandi, and A.H. Kashan, DisABC: A new artificial bee colony algorithm for binary optimization. Applied Soft Computing, 2012. 12(1): p. 342-352.
• 40. Kiran, M.S. and M. Gündüz, XOR-based artificial bee colony algorithm for binary optimization. Turkish Journal of Electrical Engineering & Computer Sciences, 2013. 21(Sup. 2): p. 2307-2328.
• 41. Kiran, M.S., A binary artificial bee colony algorithm and its performance assessment. Expert Systems with Applications, 2021. 175: p. 114817.
• 42. Ozturk, C., E. Hancer, and D. Karaboga, A novel binary artificial bee colony algorithm based on genetic operators. Information Sciences, 2015. 297: p. 154-170.
• 43. Hakli, H. and Z. Ortacay, An improved scatter search algorithm for the uncapacitated facility location problem. Computers & Industrial Engineering, 2019. 135: p. 855-867.
• 44. James, J. and V.O. Li, A social spider algorithm for global optimization. Applied soft computing, 2015. 30: p. 614-627.
• 45. Korkmaz, S., A. Babalik, and M.S. Kiran, An artificial algae algorithm for solving binary optimization problems. International Journal of Machine Learning and Cybernetics, 2018. 9(7): p. 1233-1247.
• 46. Sörensen, K., Metaheuristics—the metaphor exposed. International Transactions in Operational Research, 2015. 22(1): p. 3-18.
• 47. Wolpert, D.H. and W.G. Macready, No free lunch theorems for optimization. IEEE transactions on evolutionary computation, 1997. 1(1): p. 67-82.
• 48. Cornuéjols, G., G. Nemhauser, and L. Wolsey, The uncapicitated facility location problem. 1983, Cornell University Operations Research and Industrial Engineering.
• 49. Glover, F., et al., A simple multi-wave algorithm for the uncapacitated facility location problem. Frontiers of engineering management, 2018. 5(4): p. 451-465.
• 50. Jakob, K. and P.M. Pruzan, The simple plant location problem: Survey and synthesis. European journal of operational research, 1983. 12: p. 36-81.
• 51. Monabbati, E. and H.T. Kakhki, On a class of subadditive duals for the uncapacitated facility location problem. Applied Mathematics and Computation, 2015. 251: p. 118-131.
• 52. Kole, A., P. Chakrabarti, and S. Bhattacharyya, An ant colony optimization algorithm for uncapacitated facility location problem. 2013.
• 53. Tuncbilek, N., F. Tasgetiren, and S. Esnaf, Artificial bee colony optimization algorithm for uncapacitated facility location problems. Journal of Economic and Social Research, 2012. 14(1): p. 1.
• 54. Beasley, J.E., OR-Library: distributing test problems by electronic mail. Journal of the operational research society, 1990. 41(11): p. 1069-1072. 55. Ingle, K.K. and R.K. Jatoth, An efficient JAYA algorithm with lévy flight for non-linear channel equalization. Expert Systems with Applications, 2020. 145: p. 112970.
• 56. Zhang, X., et al., Binary artificial algae algorithm for multidimensional knapsack problems. Applied Soft Computing, 2016. 43: p. 583-595.
• 57. Kiran, M.S., The continuous artificial bee colony algorithm for binary optimization. Applied Soft Computing, 2015. 33: p. 15-23.
• 58. Korkmaz, S. and M.S. Kiran, An artificial algae algorithm with stigmergic behavior for binary optimization. Applied Soft Computing, 2018. 64: p. 627-640.
• 59. Cura, T., A parallel local search approach to solving the uncapacitated warehouse location problem. Computers & Industrial Engineering, 2010. 59(4): p. 1000-1009.