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Karşıt Tabanlı Öğrenme İle Geliştirilmiş Yapay Denizanası Arama Algoritması

Yıl 2022, Sayı: 44, 27 - 34, 31.12.2022
https://doi.org/10.31590/ejosat.1219071

Öz

Bu çalışmada denizanalarının okyanustaki yiyecek arama davranışının modellenmesi ile oluşturulan yapay denizanası arama algoritmasının (JS) performansını geliştirmek amacıyla yeni gelişmiş bir algoritma önerilmiştir. Bunun için JS’ye karşıt tabanlı öğrenme yaklaşımı dahil edilerek popülasyondaki bireylerin arama uzayına daha doğru şekilde dağıtılması sağlanmıştır. Geliştirilmiş algoritma(KJS), standart kıyaslama fonksiyonları üzerinde 10,30,50,100,500 ve 1000 boyut için test edilmiştir. Elde edilen sonuçlar JS ve literatürdeki algoritmalarla karşılaştırılmış, istatistik testler ile yorumlanmıştır. Sonuçlar değerlendirildiğinde önerilen KJS algoritmasının başarılı ve kabul edilebilir sonuçlar ürettiği tespit edilmiştir.

Kaynakça

  • Abdel-Basset, M., Mohamed, R., Abouhawwash, M., Chakrabortty, R. K., Ryan, M. J., & Nam, Y. (2021). An Improved Jellyfish Algorithm for Multilevel Thresholding of Magnetic Resonance Brain Image Segmentations.
  • Almodfer, R., Zayed, M. E., Abd Elaziz, M., Aboelmaaref, M. M., Mohammed, M., & Elsheikh, A. H. (2022). Modeling of a solar-powered thermoelectric air-conditioning system using a random vector functional link network integrated with jellyfish search algorithm. Case Studies in Thermal Engineering, 101797.
  • Chou, J.-S., & Truong, D.-N. (2021). A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean. Applied Mathematics and Computation, 389, 125535.
  • Dhevanandhini, G., & Yamuna, G. An Efficient Lossless Video Watermarking With Multiple Watermarks Using Artificial Jellyfish Algorithm.
  • Gandomi, A. H., Yang, X.-S., Talatahari, S., & Alavi, A. H. (2013). Firefly algorithm with chaos. Communications in Nonlinear Science and Numerical Simulation, 18(1), 89-98.
  • García, S., Molina, D., Lozano, M., & Herrera, F. (2009). A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 special session on real parameter optimization. Journal of Heuristics, 15(6), 617-644.
  • Ginidi, A., Elsayed, A., Shaheen, A., Elattar, E., & El-Sehiemy, R. (2021). An Innovative Hybrid Heap-Based and Jellyfish Search Algorithm for Combined Heat and Power Economic Dispatch in Electrical Grids. Mathematics, 9(17), 2053.
  • Gouda, E. A., Kotb, M. F., & El-Fergany, A. A. (2021). Jellyfish search algorithm for extracting unknown parameters of PEM fuel cell models: Steady-state performance and analysis. Energy, 221, 119836.
  • Gupta, S., & Deep, K. (2019). A hybrid self-adaptive sine cosine algorithm with opposition based learning. Expert Systems with Applications, 119, 210-230.
  • Jiang, S.-J., Dao, T.-K., Vu, V.-D., & Ngo, T.-G. (2021). A Power System Economic Load Dispatch Using Jellyfish Search Algorithm. In Soft Computing for Problem Solving (pp. 321-331): Springer.
  • Kaveh, A., Biabani Hamedani, K., Kamalinejad, M., & Joudaki, A. (2021). Quantum-based jellyfish search optimizer for structural optimization, 11(2), 329-356.
  • Li, Y., Zhao, Y., & Liu, J. (2021). Dimension by dimension dynamic sine cosine algorithm for global optimization problems. Applied Soft Computing, 98, 106933.
  • Mahdavi, S., Rahnamayan, S., & Deb, K. (2018). Opposition based learning: A literature review. Swarm and evolutionary computation, 39, 1-23.
  • Manita, G., & Zermani, A. (2021). A Modified Jellyfish Search Optimizer With Orthogonal Learning Strategy. Procedia Computer Science, 192, 697-708.
  • Rajpurohit, J., & Sharma, T. K. (2022). Chaotic active swarm motion in jellyfish search optimizer. International Journal of System Assurance Engineering and Management, 1-17.
  • Singh, A. (2019). Laplacian whale optimization algorithm. International Journal of System Assurance Engineering and Management, 10(4), 713-730.
  • TEMURTAŞ, H., Yaşar, C., & ÖZYÖN, S. (2017). Nümerik fonksiyonların optimizasyonu için karşıt tabanlı yeni bir meta-sezgisel algoritma. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 17(3), 922-937.
  • Tizhoosh, H. R. (2005). Opposition-based learning: a new scheme for machine intelligence. Paper presented at the International conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC'06).
  • Xu, Q., Wang, L., Wang, N., Hei, X., & Zhao, L. (2014). A review of opposition-based learning from 2005 to 2012. Engineering Applications of Artificial Intelligence, 29, 1-12.
  • Yıldızdan, G., & Baykan, Ö. K. (2021). A Novel Artificial Jellyfish Search Algorithm Improved with Detailed Local Search Strategy. Paper presented at the 2021 6th International Conference on Computer Science and Engineering (UBMK).
  • Zhu, A., Xu, C., Li, Z., Wu, J., & Liu, Z. (2015). Hybridizing grey wolf optimization with differential evolution for global optimization and test scheduling for 3D stacked SoC. Journal of Systems Engineering and Electronics, 26(2), 317-328.

Artificial Jellyfish Search Algorithm Developed With Opposition-Based Learning

Yıl 2022, Sayı: 44, 27 - 34, 31.12.2022
https://doi.org/10.31590/ejosat.1219071

Öz

In this study, a newly developed algorithm was proposed to improve the performance of the artificial jellyfish search algorithm (JS), which is created by modeling the foraging behavior of jellyfish in the ocean. For this, an oppositional-based learning approach was included in JS to provide a more accurate distribution of individuals in the population to the search space. The developed algorithm (KJS) was tested on standard benchmark functions for 10,30,50,100,500 and 1000 dimensions. The obtained results were compared with JS and algorithms in the literature and interpreted with statistical tests. When the results were evaluated, it was determined that the proposed KJS algorithm produced successful and acceptable results.

Kaynakça

  • Abdel-Basset, M., Mohamed, R., Abouhawwash, M., Chakrabortty, R. K., Ryan, M. J., & Nam, Y. (2021). An Improved Jellyfish Algorithm for Multilevel Thresholding of Magnetic Resonance Brain Image Segmentations.
  • Almodfer, R., Zayed, M. E., Abd Elaziz, M., Aboelmaaref, M. M., Mohammed, M., & Elsheikh, A. H. (2022). Modeling of a solar-powered thermoelectric air-conditioning system using a random vector functional link network integrated with jellyfish search algorithm. Case Studies in Thermal Engineering, 101797.
  • Chou, J.-S., & Truong, D.-N. (2021). A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean. Applied Mathematics and Computation, 389, 125535.
  • Dhevanandhini, G., & Yamuna, G. An Efficient Lossless Video Watermarking With Multiple Watermarks Using Artificial Jellyfish Algorithm.
  • Gandomi, A. H., Yang, X.-S., Talatahari, S., & Alavi, A. H. (2013). Firefly algorithm with chaos. Communications in Nonlinear Science and Numerical Simulation, 18(1), 89-98.
  • García, S., Molina, D., Lozano, M., & Herrera, F. (2009). A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 special session on real parameter optimization. Journal of Heuristics, 15(6), 617-644.
  • Ginidi, A., Elsayed, A., Shaheen, A., Elattar, E., & El-Sehiemy, R. (2021). An Innovative Hybrid Heap-Based and Jellyfish Search Algorithm for Combined Heat and Power Economic Dispatch in Electrical Grids. Mathematics, 9(17), 2053.
  • Gouda, E. A., Kotb, M. F., & El-Fergany, A. A. (2021). Jellyfish search algorithm for extracting unknown parameters of PEM fuel cell models: Steady-state performance and analysis. Energy, 221, 119836.
  • Gupta, S., & Deep, K. (2019). A hybrid self-adaptive sine cosine algorithm with opposition based learning. Expert Systems with Applications, 119, 210-230.
  • Jiang, S.-J., Dao, T.-K., Vu, V.-D., & Ngo, T.-G. (2021). A Power System Economic Load Dispatch Using Jellyfish Search Algorithm. In Soft Computing for Problem Solving (pp. 321-331): Springer.
  • Kaveh, A., Biabani Hamedani, K., Kamalinejad, M., & Joudaki, A. (2021). Quantum-based jellyfish search optimizer for structural optimization, 11(2), 329-356.
  • Li, Y., Zhao, Y., & Liu, J. (2021). Dimension by dimension dynamic sine cosine algorithm for global optimization problems. Applied Soft Computing, 98, 106933.
  • Mahdavi, S., Rahnamayan, S., & Deb, K. (2018). Opposition based learning: A literature review. Swarm and evolutionary computation, 39, 1-23.
  • Manita, G., & Zermani, A. (2021). A Modified Jellyfish Search Optimizer With Orthogonal Learning Strategy. Procedia Computer Science, 192, 697-708.
  • Rajpurohit, J., & Sharma, T. K. (2022). Chaotic active swarm motion in jellyfish search optimizer. International Journal of System Assurance Engineering and Management, 1-17.
  • Singh, A. (2019). Laplacian whale optimization algorithm. International Journal of System Assurance Engineering and Management, 10(4), 713-730.
  • TEMURTAŞ, H., Yaşar, C., & ÖZYÖN, S. (2017). Nümerik fonksiyonların optimizasyonu için karşıt tabanlı yeni bir meta-sezgisel algoritma. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 17(3), 922-937.
  • Tizhoosh, H. R. (2005). Opposition-based learning: a new scheme for machine intelligence. Paper presented at the International conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC'06).
  • Xu, Q., Wang, L., Wang, N., Hei, X., & Zhao, L. (2014). A review of opposition-based learning from 2005 to 2012. Engineering Applications of Artificial Intelligence, 29, 1-12.
  • Yıldızdan, G., & Baykan, Ö. K. (2021). A Novel Artificial Jellyfish Search Algorithm Improved with Detailed Local Search Strategy. Paper presented at the 2021 6th International Conference on Computer Science and Engineering (UBMK).
  • Zhu, A., Xu, C., Li, Z., Wu, J., & Liu, Z. (2015). Hybridizing grey wolf optimization with differential evolution for global optimization and test scheduling for 3D stacked SoC. Journal of Systems Engineering and Electronics, 26(2), 317-328.
Toplam 21 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Gülnur Yıldızdan 0000-0001-6252-9012

Erken Görünüm Tarihi 31 Aralık 2022
Yayımlanma Tarihi 31 Aralık 2022
Yayımlandığı Sayı Yıl 2022 Sayı: 44

Kaynak Göster

APA Yıldızdan, G. (2022). Karşıt Tabanlı Öğrenme İle Geliştirilmiş Yapay Denizanası Arama Algoritması. Avrupa Bilim Ve Teknoloji Dergisi(44), 27-34. https://doi.org/10.31590/ejosat.1219071