Göç oranı ve göç aralığı değerlerinin yanıt yüzeyi yöntemi ile belirlenmesi: Paralel göçmen kuşlar optimizasyon algoritması örneği

Göç, paralel metasezgisel optimizasyon algoritmalarının başarılı sonuçlar üretmesini sağlayan başlıca işlem adımıdır. Göç parametreleri olan göç oranı (MR) ve göç aralığının (MI) doğru olarak belirlenmesi algoritmanın başarısını etkiler. Bu nedenle göç parametrelerinin değerlerinin belirlenmesi üzerine yapılacak çalışmalar önemlidir. Bu çalışmada, MR ve MI değerleri, yanıt yüzeyi yöntemlerinden biri olan merkezi kompozit tasarım deney düzeni kullanılarak belirlenmiştir. Tespit edilen MR ve MI değerleri, Paralel Göçmen Kuşlar Optimizasyon (PGKO) algoritmasında uygulanmış ve fonksiyonun uygunluk değerleri hesaplanmıştır. Bu sonuçlara göre; MI, MR, MI*MR, MI*MI ve MR*MR katsayılarını içeren bir denklem oluşturulmuştur. Böylece, uygunluk değeri ile MR ve MI faktörlerinin doğrusal etkisi, etkileşim etkisi ve kuadratik etkisi ortaya konmuştur. Modelin oluşturulmasından sonra çekicilik fonksiyonu kullanılarak MR ve MI parametrelerinin optimizasyonu sağlanmış ve en iyi değerleri belirlenmiştir. Önerilen MR ve MI değerleri kullanılarak yapılan doğrulama deneylerine göre başarılı sonuçlar elde edilmiştir.

Anahtar Kelimeler:

Paralel göçmen kuşlar optimizasyon algoritması, göç oranı, göç aralığı, yanıt yüzeyi yöntemi, merkezi kompozit tasarım

PDF

___

1. Özyön S., Yaşar C., Temurtaş H., Incremental gravitational search algorithm for high-dimensional benchmark functions, Neural Computing and Applications, 31 (8), 3779–3803, 2019.
2. Aydın D., Yavuz G., Stützle T., ABC-X: a generalized, automatically configurable artificial bee colony framework, Swarm Intelligence, 11 (1), 1–38, 2017.
3. Basturk A., Akay R., Performance analysis of the coarse-grained parallel model of the artificial bee colony algorithm, Information Sciences, 253, 34-55, 2013.
4. Huo J., Liu L., Zhang Y., An improved multi-cores parallel artificial bee colony optimization algorithm for parameters calibration of hydrological model, Future Generation Computer Systems, 81, 492-504, 2018.
5. Gülcü Ş., Kodaz H., A novel parallel multi-swarm algorithm based on comprehensive learning particle swarm optimization, Engineering Applications of Artificial Intelligence, 45, 33-45, 2015.
6. Tu K., Liang Z., Parallel computation models of particle swarm optimization implemented by multiple threads, Expert Systems with Applications, 38 (5), 5858-5866, 2011.
7. Adar N., Kuvat G., Parallel Genetic Algorithms with Dynamic Topology using Cluster Computing, Advances in Electrical and Computer Engineering, 16 (3), 73-80, 2016.
8. Abdelhafez A., Alba E., Luque G., Performance analysis of synchronous and asynchronous distributed genetic algorithms on multiprocessors, Swarm and Evolutionary Computation, 49, 147-157, 2019.
9. Randall M., Lewis A., A Parallel Implementation of Ant Colony Optimization, Journal of Parallel and Distributed Computing, 62 (9), 1421-1432, 2002.
10. Ling C., Hai-Ying S., Shu W., A parallel ant colony algorithm on massively parallel processors and its convergence analysis for the travelling salesman problem, Information Sciences, 199, 31-42, 2012.
11. Strofylas G.A., Porfyri K.N., Nikolos I.K., Delis A.I., Papageorgiou M., Using synchronous and asynchronous parallel Differential Evolution for calibrating a second-order traffic flow model, Advances in Engineering Software, 125, 1-18, 2018.
12. Wang H., Rahnamayan S., Wu Z., Parallel differential evolution with self-adapting control parameters and generalized opposition-based learning for solving high-dimensional optimization problems, Journal of Parallel and Distributed Computing, 73 (1), 62-73, 2013.
13. Yuan X., Zhang T., Xiang Y., Dai X., Parallel chaos optimization algorithm with migration and merging operation, Applied Soft Computing, 35, 591-604, 2015.
14. Çınar A. C., Kıran M. S., A parallel implementation of Tree-Seed Algorithm on CUDA-supported graphical processing unit, Journal of the Faculty of Engineering and Architecture of Gazi University, 33 (4), 1397-1409, 2018.
15. Mehne H. H., Mirjalili S., A parallel numerical method for solving optimal control problems based on whale optimization algorithm, Knowledge-Based Systems, 151, 114-123, 2018.
16. Rebaudengo M., Reorda M. S., An experimental analysis of the effects of migration in parallel genetic algorithms, 1993 Euromicro Workshop on Parallel and Distributed Processing, Gran Canaria, Spain, 232-238, 27-29 January 1993.
17. Duman E., Uysal M., Alkaya A. F., Migrating Birds Optimization: A new metaheuristic approach and its performance on quadratic assignment problem, Information Sciences, 217, 65-77, 2012.
18. Lissaman P. B., Shollenberger C. A., Formation Flight of Birds, Science, 168, 1003-1005, 1970.
19. Makas H., Yumuşak N., System identification by using migrating birds optimization algorithm: a comparative performance analysis, Turkish Journal of Electrical Engineering & Computer Sciences, 24, 1879-1900, 2016.
20. Makas H., Yumuşak N., Balancing exploration and exploitation by using sequential execution cooperation between artificial bee colony and migrating birds optimization algorithms, Turkish Journal of Electrical Engineering & Computer Sciences., 24, 4935-4956, 2016.
21. Tülek A., Göçmen kuşlar optimizasyon algoritmasının paralel bilgisayarlarda uygulanması, Yüksek Lisans Tezi, Balıkesir Üniversitesi, Fen Bilimleri Enstitüsü, Balıkesir, 2019.
22. Tongur V., Ülker E., The Analysis of Migrating Birds Optimization Algorithm with Neighborhood Operator on Traveling Salesman Problem, Intelligent and Evolutionary Systems, 227-237, 2015.
23. Makas H., Yumuşak N., New cooperative and modified variants of the migrating birds optimization algorithm, International Conference on Electronics, Computer and Computation (ICECCO), Ankara, Turkey, 176-179, 7-9 November 2013.
24. Niroomand S., Hadi-Vencheh A., Şahin R., Vizvári B., Modified migrating birds optimization algorithm for closed loop layout with exact distances in flexible manufacturing systems, Expert Systems with Applications, 42 (19), 6586-6597, 2015.
25. Oz D., An improvement on the Migrating Birds Optimization with a problem-specific neighboring function for the multi-objective task allocation problem, Expert Systems with Applications, 67, 304-311, 2017.
26. Xuejun Z., Xiangmin G., Yanbo Z., Jiaxing L., Strategic flight assignment approach based on multi-objective parallel evolution algorithm with dynamic migration interval, Chinese Journal of Aeronautics, 28 (2), 556-563, 2015.
27. Asadzadeh L., A parallel artificial bee colony algorithm for the job shop scheduling problem with a dynamic migration strategy, Computers & Industrial Engineering, 102, 359-367, 2016.
28. Hong T., Huang L., Lin W., Liu Y., Chakraborty G., Dynamic migration in multiple ant colonies, IEEE 2nd International Conference on Cybernetics (CYBCONF), Gdynia, Poland, 24-26 June 2015.
29. Hiroyasu T., Miki M., Negami M., Distributed genetic algorithms with randomized migration rate, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics, Tokyo, Japan, volume 1, 689-694, 12-15 October 1999.
30. Maeda Y., Ishita M., Li O., Fuzzy adaptive search method for parallel genetic algorithm with island combination process, International Journal of Approximate Reasoning, 41 (1), 59-73, 2006.
31. Alba E., Luna F., Nebro A.J., Troya J.M., Parallel heterogeneous genetic algorithms for continuous optimization, Parallel Computing, 30 (5–6), 699-719, 2004.
32. Anderson-Cook C. M., Borror C. M., Montgomery D. C., Response surface design evaluation and comparison, Journal of Statistical Planning and Inference, 139 (2), 629-641, 2009.
33. Murat D., Ensarioğlu C., Gürsakal N., Oral A., Çakir M., Evaluation of tool wear for hard turning operations through response surface methodology, Journal of the Faculty of Engineering and Architecture of Gazi University 33 (4), 1299-1308, 2018.
34. Myers R.H., Montgomery D.C., Response Surface Methodology: Process and Product Optimization Using Designed Experiments, Second Edition, John Wiley&Sons, pp. 798, 2002.
35. Kolarik W. J., Creating quality: concepts, systems, strategies, and tools, New York, NY: McGraw-Hill, 1995.
36. Montgomery, D. C., Introduction to Statistical Quality Control, Fifth Edition, John Wiley&Sons, 2005.
37. Khuri A., Kim H. J., Um Y., Quantile plots of the prediction variance for response surface designs, Computational statistics & data analysis, 22 (4), 395-407, 1996.
38. Clarke G. M., Kempson R. E., Introduction to the Design and Analysis of Experiments, Arnold Co published John Wiley & Sons, pp. 344, 1997.
39. Marget W. M., Morris M. D., Central Composite Experimental Designs for Multiple Responses with Different Models, Technometrics, 61 (4), 524-532, 2019.
40. Derringer G., Suich, R., Simultaneous optimization of several response variables, Journal of Quality Technology, 12 (4), 214-219, 1980.
41. He Y., He Z., Lee D. H., Kim K. J., Zhang L., Yang X., Robust fuzzy programming method for MRO problems considering location effect, dispersion effect and model uncertainty, Computers & Industrial Engineering, 105, 76-83, 2017.
42. Derringer G. C., Act A. B., Optimizing a Product's Properties, Quality Progress, 51-58, June 1994.
43. Virtual Library of Simulation Experiments, Test Functions and Datasets, https://www.sfu.ca/~ssurjano/rastr.html, Yayın tarihi August 2017, Erişim tarihi, February 04, 2020.
44. Montgomery, D. C., Design and analysis of experiments, John Wiley & Sons, 5 th ed., 684s., 2001.
45. Yoğurtçu H., Optimization of microwave apple drying using response surface method, Journal of the Faculty of Engineering and Architecture of Gazi University, 34 (3), 1365-1376, 2019.
46. Bilen M., Ateş Ç, Bayraktar B., Determination of optimal conditions in boron factory wastewater chemical treatment process via response surface methodology, Journal of the Faculty of Engineering and Architecture of Gazi University, 33 (1), 267-278, 2018.