Parçacık sürüsü optimizasyon algoritmasının bibliyometrik analizi

Yazarlar

DOI:

https://doi.org/10.5281/zenodo.14202430

Anahtar Kelimeler:

Optimizasyon- Bibliyometrik- Analiz- Parçacık- Sürüsü

Öz

Günümüzde büyük boyutlardaki çalışma ortamlarında, verimli arama işlemlerini kullanarak çözüm uzayındaki optimum çözüme daha hızlı ulaşıp optimizasyon problemlerine çözüm bulmak amacıyla metasezgisel algoritmalar kullanılmaktadır. Bu algoritmalardan biri olan Parçacık Sürüsü Optimizasyon algoritmasının, kodlamasının kolay olması ve yüksek performansı ile çeşitli optimizasyon problemlerinde, özellikle karmaşık ve çok boyutlu problemlerde, başarılı sonuçlar elde etmesi nedeniyle son yıllarda popüler olarak tercih edildiği görülmektedir. Bu çalışma kapsamında, literatürde son beş yılda (2020-2024) Parçacık Sürüsü Optimizasyon algoritması kullanılarak yapılan araştırmaların bibliyometrik analizi yapılmıştır. Yapılan çalışmada Web of Science veri tabanında “Particle”, “Swarm” ve “Optimization” anahtar kelimeleri aratılarak 29867 yayına ulaşılmıştır. Web of Science veri tabanından edilen veriler VOSviewer programında literatür analizi, ağ analizi ve görsel veri keşfi yöntemleri kullanılarak analiz edilmiştir. Yapılan analizler sonucunda kategori dağılımı, yayınların yıllar içindeki dağılımı, yayınların döküman tipleri, yayınların dizin dağılımı, yayınların ülkelere göre dağılımı gibi çeşitli kriterler ile ilgili çalışmalar tüm detayları ile niceliksel olarak verilmiştir.

Referanslar

Floudas, C.A., Pardalos, P.M.: A Collection of Test Problems for Constrained Global Optimization Algorithms. LNCS, vol. 455. Springer, Heidelberg (1990)

Kirkpatrick, S., Gellat, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 670–680 (1983)

Dorigo, M.: Optimization, Learning and Natural Algorithms. PhD thesis. Politecnico di Milano, Italy (1992)

Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proc. of IEEE International Conference on Neural Networks, Piscataway, NJ, pp. 1942–1948 (1995)

Yang, X.S., Deb, S.: Engineering optimization by cuckoo search. Int. J. Math. Modelling & Num. Optimization 1, 330–343 (2010)

Yang, X. S. (2011, May). Metaheuristic optimization: algorithm analysis and open problems. In International symposium on experimental algorithms (pp. 21-32). Berlin, Heidelberg: Springer Berlin Heidelberg.

Akkaya, A., Közkurt C. & Durgut R. (2023). A study on meta-heuristic algorithms used for problem solving in recent years. Pioneer and Contemporary Studies in Engineering 18, 327-352.

Bai, Q. (2010). Analysis of particle swarm optimization algorithm. Computer and information science, 3(1), 180.

Shi Y, Eberhart R C. (1998). A modified particle swam optimizer. IEEE Word Congress on Computational Intelligence, 1998: 69-73.

Yan, X., Wu, Q., Liu, H., & Huang, W. (2013). An improved particle swarm optimization algorithm and its application. International Journal of Computer Science Issues (IJCSI), 10(1), 316.

Yonggang, C., Fengjie, Y., & Jigui, S. (2006). A new Particle swam optimization Algorithm. Journal of Jilin University, 24(2), 181-183.

Guedria, N. B. (2016). Improved accelerated PSO algorithm for mechanical engineering optimization problems. Applied Soft Computing, 40, 455-467.

Miranda, V., & Fonseca, N. (2002, May). EPSO-best-of-two-worlds meta-heuristic applied to power system problems. In Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No. 02TH8600) (Vol. 2, pp. 1080-1085). IEEE.

Garg, H. (2016). A hybrid PSO-GA algorithm for constrained optimization problems. Applied Mathematics and Computation, 274, 292-305.

Şenel, F. A., Gökçe, F., Yüksel, A. S., & Yiğit, T. (2019). A novel hybrid PSO–GWO algorithm for optimization problems. Engineering with Computers, 35, 1359-1373.

Kamboj, V. K. (2016). A novel hybrid PSO–GWO approach for unit commitment problem. Neural Computing and Applications, 27, 1643-1655.

Holden, N. P., & Freitas, A. A. (2007, July). A hybrid PSO/ACO algorithm for classification. In Proceedings of the 9th annual conference companion on Genetic and evolutionary computation (pp. 2745-2750).

Huang, C. L., & Dun, J. F. (2008). A distributed PSO–SVM hybrid system with feature selection and parameter optimization. Applied soft computing, 8(4), 1381-1391.

Chegini, S. N., Bagheri, A., & Najafi, F. (2018). PSOSCALF: A new hybrid PSO based on Sine Cosine Algorithm and Levy flight for solving optimization problems. Applied Soft Computing, 73, 697-726.

de Fátima Araújo, T., & Uturbey, W. (2013). Performance assessment of PSO, DE and hybrid PSO–DE algorithms when applied to the dispatch of generation and demand. International Journal of Electrical Power & Energy Systems, 47, 205-217.

Trivedi, I. N., Jangir, P., Kumar, A., Jangir, N., & Totlani, R. (2018). A novel hybrid PSO–WOA algorithm for global numerical functions optimization. In Advances in Computer and Computational Sciences: Proceedings of ICCCCS 2016, Volume 2 (pp. 53-60). Springer Singapore.

Jahed Armaghani, D., Shoib, R. S. N. S. B. R., Faizi, K., & Rashid, A. S. A. (2017). Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles. Neural Computing and Applications, 28, 391-405.

Borhanazad, H., Mekhilef, S., Ganapathy, V. G., Modiri-Delshad, M., & Mirtaheri, A. (2014). Optimization of micro-grid system using MOPSO. Renewable energy, 71, 295-306.

Lalwani, S., Singhal, S., Kumar, R., & Gupta, N. (2013). A comprehensive survey: Applications of multi-objective particle swarm optimization (MOPSO) algorithm. Transactions on combinatorics, 2(1), 39-101.

Coello, C. C., & Lechuga, M. S. (2002, May). MOPSO: A proposal for multiple objective particle swarm optimization. In Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No. 02TH8600) (Vol. 2, pp. 1051-1056). IEEE.

Alvarez-Benitez, J. E., Everson, R. M., & Fieldsend, J. E. (2005, March). A MOPSO algorithm based exclusively on pareto dominance concepts. In International conference on evolutionary multi-criterion optimization (pp. 459-473). Berlin, Heidelberg: Springer Berlin Heidelberg.

Fallah-Mehdipour, E., Haddad, O. B., & Mariño, M. A. (2011). MOPSO algorithm and its application in multipurpose multireservoir operations. Journal of Hydroinformatics, 13(4), 794-811.

Tsou, C. S. (2008). Multi-objective inventory planning using MOPSO and TOPSIS. Expert Systems with Applications, 35(1-2), 136-142.

Hu, L., Yang, Y., Tang, Z., He, Y., & Luo, X. (2023). FCAN-MOPSO: an improved fuzzy-based graph clustering algorithm for complex networks with multiobjective particle swarm optimization. IEEE Transactions on Fuzzy Systems, 31(10), 3470-3484.

Ghorbani, N., Kasaeian, A., Toopshekan, A., Bahrami, L., & Maghami, A. (2018). Optimizing a hybrid wind-PV-battery system using GA-PSO and MOPSO for reducing cost and increasing reliability. Energy, 154, 581-591.

Zeren, D., & Kaya, N. (2020). Dijital pazarlama: Ulusal yazının bibliyometrik analizi. Çağ Üniversitesi Sosyal Bilimler Dergisi, 17(1), 35-52.

Üsdiken, B., & Pasadeos, Y. (1993). Türkiye’de örgütler ve yönetim yazını. Amme İdaresi Dergisi, 26(2), 73-93

Kılıçarslan, Y., & Közkurt, C. Bibliyometrik Analiz: Rüzgâr Enerjisi Hasadı. BİDGE Yayınları.

Bilal, Rani, D., Pant, M., & Jain, S. K. (2020). Dynamic programming integrated particle swarm optimization algorithm for reservoir operation. International Journal of System Assurance Engineering and Management, 11, 515-529.

Tran, D. D., Vafaeipour, M., El Baghdadi, M., Barrero, R., Van Mierlo, J., & Hegazy, O. (2020). Thorough state-of-the-art analysis of electric and hybrid vehicle powertrains: Topologies and integrated energy management strategies. Renewable and Sustainable Energy Reviews, 119, 109596.

Wang, F., Zhang, H., & Zhou, A. (2021). A particle swarm optimization algorithm for mixed-variable optimization problems. Swarm and Evolutionary Computation, 60, 100808.

Antonopoulos, I., Robu, V., Couraud, B., Kirli, D., Norbu, S., Kiprakis, A., ... & Wattam, S. (2020). Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review. Renewable and Sustainable Energy Reviews, 130, 109899.

Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research, 122, 502-517.

Yayınlanmış

01-12-2024

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[email protected] sürecindeki araştırmaların devamı sağlanmıştır.

 

Nasıl Atıf Yapılır

akkaya, ahmet, & Közkurt, C. (2024). Parçacık sürüsü optimizasyon algoritmasının bibliyometrik analizi. AINTELIA Science Notes Journal, 3(1), 12-28. https://doi.org/10.5281/zenodo.14202430