Metasezgisel Algoritma Seçiminde Kriter Önceliklerinin AHP Yöntemiyle Belirlenmesi

Öz

Analitik Hiyerarşi Prosesi (AHP) çok kriterli bir problemde, kriterleri ağırlıklandırmak ve alternatifler arasından seçim yapmak için kullanılan bir yöntemdir. Kişiler veya uzman kişilerden oluşan gruplar AHP kullanarak subjektif yargıları sayısal değerlere dönüştürebilirler. Yapılan karşılaştırmalar sonucu kriter önceliklerini belirleyebilir ve karar verme aşamasında bu sonuçları kullanabilirler. Günümüzde optimizasyon problemlerinin çözümü için kullanılan evrimsel, doğa esinli, fizik-matematik temelli veya sürü zekası gibi farklı özelliklere sahip birçok metasezgisel algoritma vardır. Bu çalışmada, bir metasezgisel algoritmanın seçimi aşamasında etkili olan kriterlerin öncelik sırasının araştırılması amaçlanmıştır. Seçim için belirleyici olan kriterler uzman kişiler tarafından değerlendirilmiş, elde edilen sonuçlara göre kriterlerin önem düzeyleri sıralanmış ve ağırlıkları hesaplanmıştır. Elde edilen bulgulara göre bir metasezgisel algoritmanın seçiminde en önemli kriterin, çözüm kalitesi olduğu belirlenmiştir. Bunun yanında, önem sırasına göre en sonda olan kriterin algoritmanın yapısı olduğu gösterilmiştir.

Anahtar Kelimeler

• Metasezgisel algoritma
• analitik hiyerarşi süreci
• çok kriterli karar verme
• öncelik vektörü
• ikili karşılaştırma

Determination of Criteria Priorities in Metaheuristic Algorithm Selection Using the AHP Method

Öz

The Analytic Hierarchy Process (AHP) is a method used in multi-criteria problems to assign weights to criteria and make selections among alternatives. Individuals or groups of experts can use AHP to convert subjective judgments into numerical values. Based on the comparisons made, they can determine the priorities of the criteria and use these results during the decision-making process. Today, there are many metaheuristic algorithms with various characteristics—such as evolutionary, nature-inspired, physics–mathematics-based, or swarm intelligence—used to solve optimization problems. In this study, the aim is to investigate the priority order of the criteria that are effective in the selection of a metaheuristic algorithm. The criteria that determine the selection were evaluated by experts; based on the results obtained, the importance levels of the criteria were ranked, and their weights were calculated. According to the findings, the most important criterion in selecting a metaheuristic algorithm is the solution quality. In contrast, the algorithm structure was shown to be the least important criterion in terms of priority.

Anahtar Kelimeler

• Metaheuristic algorithm
• analytical hierarchy process
• multi-criteria decision making
• priority vector
• pairwise comparison

Kaynakça

1. S. M. Almufti, A. A. Shaban, Z. A. Ali, R. I. Ali and J. D. Fuente, “Overview of metaheuristic algorithms,” Polaris Glob. J. Sch. Res. Trends, 2, 10-32, 2023.
2. D. Karaboga and B. Basturk, “Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems,” IFSA world congress, Berlin, Germany, 2007, 789-798.
3. J. H. Holland, “Genetic algorithms and adaptation,” Adaptive Control of Ill-Defined Systems, O. G. Selfridge, E. L. Rissland, and M. A. Arbib, Eds. Boston, MA, USA: Springer, 1984, 16, 317–333.
4. M. Dorigo, V. Maniezzo and A. Colorni, “Ant system: optimization by a colony of cooperating agents,” IEEE Trans. Systs, Man., A Cybernetics, Part B (Cybernetics), 26, 29-41, 1996.
5. J. Kennedy and R. Eberhart, “Particle swarm optimization,” Proc. IEEE Int. Conf. Neural Netw. (ICNN), 4, Perth, Australia, 1995, 1942–1948.
6. S. Mirjalili, S. M. Mirjalili and A. Lewis, "Grey wolf optimizer," Adv. Eng. Software, 69, 46-61, 2014.
7. H. Eskandar, A. Sadollah, A. Bahreininejad and M. Hamdi, "Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems," Comput. Struct., 110, 151-166, 2012.
8. X.-S. Yang, “A new metaheuristic bat-inspired algorithm,” NICSO 2010, J. R. Gonzalez, D. A. Pelta, C. Cruz, G. Terrazas, and N. Krasnogor, Eds. Berlin, Heidelberg: Springer, 2010, SCI 284, 65–74.

9. X.-S. Yang, “Firefly algorithms for multimodal optimization,” Proc. Int. Symp. Stochastic Algorithms, Berlin, Heidelberg: Springer, 2009, 169–178.
10. S. Mirjalili and L. Andrew, "The whale optimization algorithm," Adv. Eng. Softw., 95, 51-67, 2016.
11. Ş. Fidan, M. Zaloğlu and E. Erkan, “TOPSIS yaklaşımı ile metasezgisel optimizasyon algoritmalarının performans değerlendirmesi,” AKUFEMUBİD, 24(3), 726-748, 2024.
12. A. Tunç, Ş. Taşdemir, and T. Sağ, “Comparison of heuristic and metaheuristic algorithms,” 7th Int. Conf. Computer Science and Engineering (UBMK), Diyarbakır, Türkiye, 2022, 76–81.
13. M., Shanmugapriya and K. K. Manivannan, "Compare the performance of meta-heuristics algorithm: a review," Metaheuristics Algorithm and Optimization of Engineering and Complex Systems, 242-253, 2024.
14. R., Rishabh and K. N. Das, “A critical review on metaheuristic algorithms based multi-criteria decision-making approaches and applications,” Arch. Comput. Methods Eng., 32(2), 963-993, 2025.
15. M. F. Tabassum and S. Akram, “Rank based TOPSIS approach for evaluating the performance of metaheuristics,” Int. J. Comput. Intell. Ctrl., 13(2), 577-590, 2021.
16. C. M. M., Rocha, A. S. Benitez and D. A. Buelvas, "Review and bibliographic analysis of metaheuristic methods in multicriteria decision‐making: A 45‐year perspective across international, Latin American, and Colombian contexts," J. Appl. Math., 2024(1), 1-17, 2024.
17. E. Shadkam, S. Safari and S. S. Abdollahzadeh, "Finally, which meta-heuristic algorithm is the best one?," IJDSRM, 10, 32-50, 2021.
18. T. L. Saaty, “Decision making with the analytic hierarchy process,” Int. J. Serv. Sci., 1(1), 83-98, 2008.
19. K. E. Yoo and Y. C. Choi, "Analytic hierarchy process approach for identifying relative importance of factors to improve passenger security checks at airports," J. Air Transp. Manag., 12(3), 135-142, 2006.
20. R. W. Saaty, “The analytic hierarchy process—what it is and how it is used,” Mathematical Modelling, 9(3-5), 161-176, 1987.
21. V. Erol and H. Başlıgil, “Analytic hierarchy process and artificial neural networks model for management information systems software selection in companies,” Sigma, 4, 107-120, 2005.
22. W. Ho, "Integrated analytic hierarchy process and its applications–A literature review," Eur. J. Oper. Res., 186(1), 211-228, 2008.
23. S. Felek, Y. Yuluğkural ve Z. Aladağ, "Mobil iletişim sektöründe pazar paylaşımının tahmininde AHP ve ANP yöntemlerinin kıyaslanması," IENG, 18(1), 6-22, 2007.
24. T. L. Saaty, “Priority setting in complex problems,” IEEE Trans. Eng. Manag., 3, 140-155, 1983.
25. N. Ömürbek ve Z. Tunca, "Analitik hiyerarşi süreci ve analitik ağ süreci yöntemlerinde grup kararı verilmesi aşamasına ilişkin bir örnek uygulama," Süleyman Demirel Üniversitesi İİBF Dergisi, 18(3), 47-70, 2013.
26. P. L., Lai, A., Potter, M., Beynon and A. Beresford, “Evaluating the efficiency performance of airports using an integrated AHP/DEA-AR technique,” Transport Policy, 42, 75-85, 2015.
27. M. M. Kablan, “Decision support for energy conservation promotion: an analytic hierarchy process approach,” Energy Policy, 32(10), 1151-1158, 2004.
28. E. J. Repetski, S. Sarkani and T. Mazzuchi, “Applying the analytic hierarchy process (AHP) to expert documents,” IJAHP, 14(1), 1-14, 2022.
29. W. Ossadnik, S. Schinke and R. H. Kaspar, “Group aggregation techniques for analytic hierarchy process and analytic network process: a comparative analysis,” Group Decis Negot., 25(2), 421-457, 2016.
30. Q. Dong and T. L. Saaty, “An analytic hierarchy process model of group consensus,” J. Syst. Sci. Syst. Eng., 23(3), 362-374, 2014.
31. S. Carpitella, V. Kratochvíl and M. Pištěk, “Multi-criteria decision making beyond consistency: An alternative to AHP for real-world industrial problems,” Comput. Ind. Eng., 198, 110661, 2024.
32. A. Siekelova, I. Podhorska and J. J. Imppola, “Analytic hierarchy process in multiple–criteria decision–making: a model example,” SHS Web of Conferences.EDP Sciences, 90, 01019, 2021.
33. G. Kuvat and N. Adar, “The analysis of the interaction of the migration diversity and permeability in parallel genetic algorithms,” IJOCTA, 6(1), 23-31, 2016.