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An advanced Salp Swarm Algorithm for optimization problems

Yıl 2023, Cilt: 12 Sayı: 4, 1071 - 1078, 15.10.2023
https://doi.org/10.28948/ngumuh.1195013

Öz

The Salp Swarm Algorithm (SSA) is a metaheuristic optimization algorithm inspired by Salp swarms' biological characteristics and colony strategies. There is a wide variety of studies conducted with SSA in the literature. These studies have revealed some significant disadvantages of SSA, the most critical being the imbalance of exploration and exploitation. In this study, an equilibrium operator has been developed using the Ikeda map. Thanks to this enhancement, the performance of the SSA algorithm has increased, and issues such as premature convergence and local optima have been overcome. To evaluate the proposed method, ten fixed-dimension benchmark problems and three engineering design optimization problems were solved. The proposed method is validated by comparing four well-known metaheuristic approaches and the original SSA. Experimental results demonstrated that the proposed method outperforms the compared methods.

Kaynakça

  • M. A. Şahman and S. Korkmaz, "Discrete Artificial Algae Algorithm for solving Job-Shop Scheduling Problems," Knowledge-Based Systems, vol. 256, p. 109711,2022. https://doi.org/10.1016/j.knosys.2022.109711
  • A. C. Cinar, "Training feed-forward multi-layer perceptron artificial neural networks with a tree-seed algorithm," Arabian Journal for Science and Engineering, vol. 45, no. 12, pp. 10915-10938, 2020. https://doi.org/10.1007/s13369-020-04872-1
  • M. Gündüz, M. S. Kiran, and E. Özceylan, "A hierarchic approach based on swarm intelligence to solve the traveling salesman problem," Turkish Journal of Electrical Engineering & Computer Sciences, vol. 23, no.1,pp.103-117,2015. https://doi.org/10.3906/elk-1210-147
  • A. C. Cinar, S. Korkmaz, and M. S. Kiran, "A discrete tree-seed algorithm for solving symmetric traveling salesman problem," Engineering Science and Technology, an International Journal, vol. 23, no. 4, pp. 879-890, 2020. https://doi.org/10.1016/j.jestch.2019.11.005
  • 2003 M. Kumar and J. S. Dhillon, "Hybrid artificial algae algorithm for economic load dispatch," (in English), Applied Soft Computing, vol. 71, pp. 89-109, Oct 2018, doi: 10.1016/j.asoc.2018.06.035. https://doi.org/10.1016/j.asoc.2018.06.035
  • M. Beşkirli, İ. Koç, H. Haklı, and H. Kodaz, "A new optimization algorithm for solving wind turbine placement problem: Binary artificial algae algorithm," Renewable energy, vol. 121, pp. 301-308. https://doi.org/10.1016/j.renene.2017.12.087
  • E. Kaya, "BinGSO: galactic swarm optimization powered by binary artificial algae algorithm for solving uncapacitated facility location problems," Neural Computing and Applications, pp. 1-20, 2022. https://doi.org/10.1007/s00521-022-07058-y
  • S. Ozsari, H. Uguz, and H. Hakli, "Implementation of meta-heuristic optimization algorithms for interview problem in land consolidation: A case study in Konya/Turkey," Land Use Policy, vol. 108, p. 105511, 2021 https://doi.org/10.1016/j.landusepol.2021.105511
  • A. C. Cinar and N. Natarajan, "An artificial neural network optimized by grey wolf optimizer for prediction of hourly wind speed in Tamil Nadu, India," Intelligent Systems with Applications, p. 200138, 2022. https://doi.org/10.1016/j.iswa.2022.200138
  • B. Turkoglu and E. Kaya, "Training multi-layer perceptron with artificial algae algorithm," Engineering Science and Technology, an International Journal, 2020. https://doi.org/10.1016/j.jestch.2020.07.001
  • B. Turkoglu, S. A. Uymaz, and E. Kaya, "Clustering analysis through artificial algae algorithm," International Journal of Machine Learning and Cybernetics, vol. 13, no. 4, pp. 1179-1196, 2022. https://doi.org/10.1016/j.asoc.2022.108630
  • B. Turkoglu, S. A. Uymaz, and E. Kaya, "Binary Artificial Algae Algorithm for feature selection," Applied Soft Computing, vol. 120, p. 108630, 2022. https://doi.org/10.1007/s13042-022-01518-6
  • E. Kaya, S. Korkmaz, M. A. Sahman, and A. C. Cinar, "DEBOHID: A differential evolution based oversampling approach for highly imbalanced datasets," Expert Systems with Applications, vol. 169, 2021. https://doi.org/10.1016/j.eswa.2020.114482
  • S. A. Uymaz, G. Tezel, and E. Yel, "Artificial algae algorithm (AAA) for nonlinear global optimization," Applied Soft Computing, vol. 31, pp. 153-171, 2015. https://doi.org/10.1016/j.asoc.2015.03.003
  • M. A. Akbari, M. Zare, R. Azizipanah-Abarghooee, S. Mirjalili, and M. Deriche, "The cheetah optimizer: A nature-inspired metaheuristic algorithm for large-scale optimization problems," Scientific reports, vol. 12, no. 1, pp. 1-20, 2022. https://doi.org/10.1038/s41598-022-14338-z
  • M. Jafari, E. Salajegheh, and J. Salajegheh, "Elephant clan optimization: A nature-inspired metaheuristic algorithm for the optimal design of structures," Applied Soft Computing, vol. 113, p. 107892, 2021. https://doi.org/10.1016/j.asoc.2021.107892
  • B. Abdollahzadeh, F. Soleimanian Gharehchopogh, and S. Mirjalili, "Artificial gorilla troops optimizer: a new nature‐inspired metaheuristic algorithm for global optimization problems," International Journal of Intelligent Systems, vol. 36, no. 10, pp. 5887-5958, 2021. https://doi.org/10.1002/int.22535
  • F. A. Hashim and A. G. Hussien, "Snake Optimizer: A novel meta-heuristic optimization algorithm," Knowledge-Based Systems, vol. 242, p. 108320, 2022. https://doi.org/10.1016/j.knosys.2022.108320
  • B. Abdollahzadeh, F. S. Gharehchopogh, and S. Mirjalili, "African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems," Computers & Industrial Engineering, vol. 158, p. 107408, 2021. https://doi.org/10.1016/j.cie.2021.107408
  • H. Jia, X. Peng, and C. Lang, "Remora optimization algorithm," Expert Systems with Applications, vol. 185, p. 115665, 2021. https://doi.org/10.1016/j.eswa.2021.115665
  • W. Zhao, L. Wang, and S. Mirjalili, "Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications," Computer Methods in Applied Mechanics and Engineering, vol. 388, p. 114194, 2022. https://doi.org/10.1016/j.cma.2021.114194
  • M. Braik, A. Hammouri, J. Atwan, M. A. Al-Betar, and M. A. Awadallah, "White Shark Optimizer: A novel bio-inspired meta-heuristic algorithm for global optimization problems," Knowledge-Based Systems, vol. 243, p. 108457, 2022. https://doi.org/10.1016/j.knosys.2022.108457
  • A. Faramarzi, M. Heidarinejad, S. Mirjalili, and A. H. Gandomi, "Marine Predators Algorithm: A nature-inspired metaheuristic," Expert Systems with Applications, vol. 152, p. 113377, 2020. https://doi.org/10.1016/j.eswa.2020.113377
  • Y. Jiang, Q. Wu, S. Zhu, and L. Zhang, "Orca predation algorithm: A novel bio-inspired algorithm for global optimization problems," Expert Systems with Applications, vol. 188, p. 116026, 2022. https://doi.org/10.1016/j.eswa.2021.116026
  • S. Mirjalili, A. H. Gandomi, S. Z. Mirjalili, S. Saremi, H. Faris, and S. M. Mirjalili, "Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems," Advances in Engineering Software, vol. 114, pp. 163-191, 2017. https://doi.org/10.1016/j.advengsoft.2017.07.002
  • D. H. Wolpert and W. G. Macready, "No free lunch theorems for optimization," IEEE transactions on evolutionary computation, vol. 1, no. 1, pp. 67-82, 1997. https://doi.org/10.1109/ 4235.585893
  • Y.-C. Ho and D. L. Pepyne, "Simple explanation of the no-free-lunch theorem and its implications," Journal of optimization theory and applications, vol. 115, no. 3, pp. 549-570, 2002. https://doi.org/10.1023/A:1021251113462
  • H. Bingol and M. Yildirim, "Global Optimizasyon İçin Sürü Tabanlı Bir Yaklaşım Salp Sürü Algoritması," Fırat Üniversitesi Fen Bilimleri Dergisi, vol. 33, no. 1, pp. 51-59, 2021.
  • M. Castelli, L. Manzoni, L. Mariot, M. S. Nobile, and A. Tangherloni, "Salp Swarm Optimization: A critical review," Expert Systems with Applications, vol. 189, p. 116029, 2022. https://doi.org/10.1016/j.eswa.2021.116029
  • Y. Şekertekin and Ö. Atan, "An image encryption algorithm using Ikeda and Henon chaotic maps," in 2016 24th Telecommunications Forum (TELFOR), 2016: IEEE, pp. 1-4.
  • S. Mirjalili, S. M. Mirjalili, and A. Lewis, "Grey wolf optimizer," Advances in engineering software, vol. 69, pp. 46-61, 2014. https://doi.org/10.1016/j.advengsoft.2013.12.007
  • S. Mirjalili and A. Lewis, "The whale optimization algorithm," Advances in engineering software, vol. 95, pp. 51-67, 2016. https://doi.org/10.1016/j.advengsoft.2016.01.008
  • L. Abualigah, A. Diabat, S. Mirjalili, M. Abd Elaziz, and A. H. Gandomi, "The arithmetic optimization algorithm," Computer methods in applied mechanics and engineering, vol. 376, p. 113609, 2021. https://doi.org/10.1016/j.cma.2020.113609
  • N. Panagant, N. Pholdee, S. Bureerat, K. Kaen, A. R. Yıldız, and S. M. Sait, "Seagull optimization algorithm for solving real-world design optimization problems," Materials Testing, vol. 62, no. 6, pp. 640-644, 2020. https://doi.org/10.3139/120.111529
  • S. Hassan, K. Kumar, C. D. Raj, and K. Sridhar, "Design and optimisation of pressure vessel using metaheuristic approach," in Applied Mechanics and Materials, 2014, vol. 465: Trans Tech Publ, pp. 401-406. https://doi.org/10.4028/www.scientific.net/AMM.465-466.401
  • A. T. Kamil, H. M. Saleh, and I. H. Abd-Alla, "A Multi-Swarm Structure for Particle Swarm Optimization: Solving the Welded Beam Design Problem," in Journal of Physics: Conference Series, 2021, vol. 1804, no. 1: IOP Publishing, p. 012012.
  • Y. Çelik and H. Kutucu, "Solving the Tension/Compression Spring Design Problem by an Improved Firefly Algorithm," IDDM, vol. 1, no. 2255, pp. 1-7, 2018.
  • G. Kaur and S. Arora, "Chaotic whale optimization algorithm," Journal of Computational Design and Engineering, vol. 5, no. 3, pp. 275-284, 2018. https://doi.org/10.1016/j.jcde.2017.12.006
  • M. Kohli and S. Arora, "Chaotic grey wolf optimization algorithm for constrained optimization problems," Journal of computational design and engineering, vol. 5, no. 4, pp. 458-472, 2018. https://doi.org/10.1016/j.jcde.2017.02.005
  • A. H. Gandomi, X.-S. Yang, S. Talatahari, and A. H. Alavi, "Firefly algorithm with chaos," Communications in Nonlinear Science and Numerical Simulation, vol. 18, no. 1, pp. 89-98, 2013. https://doi.org/10.1016/j.cnsns.2012.06.009
  • G. I. Sayed, G. Khoriba, and M. H. Haggag, "A novel chaotic salp swarm algorithm for global optimization and feature selection," Appl Intell, vol. 48, no. 10, pp. 3462-3481, 2018. https://doi.org/10.1007/s10489-018-1158-6

Optimizasyon problemleri için Gelişmiş Salp Sürüsü Algoritması

Yıl 2023, Cilt: 12 Sayı: 4, 1071 - 1078, 15.10.2023
https://doi.org/10.28948/ngumuh.1195013

Öz

Salp Sürüsü Algoritması (SSA), Salp sürülerinin biyolojik özelliklerinden ve koloni stratejilerinden ilham alarak geliştirilmiş metasezgisel bir optimizasyon algoritmasıdır. Literatürde SSA ile yapılmış çok çeşitli çalışmalar vardır. Bu çalışmalarda SSA'nın temel dezavantajlarının olduğu vurgulanmıştır. Bunlardan en önemlisi keşif ve sömürü dengesizliğidir. Bu çalışmada Ikeda haritası kullanılarak bir denge operatörü geliştirilmiştir. Bu geliştirme sayesinde SSA algoritmasının performansı artırılarak erken yakınsama ve lokal minimumlara takılma sorunu giderilmeye çalışılmıştır. Önerilen yöntemin başarısını değerlendirmek için on sabit boyutlu benchmark seti ve üç iyi bilinen mühendislik optimizasyon problemi çözülmüştür. Geliştirilen yöntemin güvenilirliği dört iyi bilinen metasezgisel yaklaşımla ve orijinal SSA ile kıyaslanarak doğrulanmıştır. Deneysel çalışma sonuçları, önerilen yöntemin kıyaslanan yöntemlerden daha performanslı olduğunu göstermiştir.

Kaynakça

  • M. A. Şahman and S. Korkmaz, "Discrete Artificial Algae Algorithm for solving Job-Shop Scheduling Problems," Knowledge-Based Systems, vol. 256, p. 109711,2022. https://doi.org/10.1016/j.knosys.2022.109711
  • A. C. Cinar, "Training feed-forward multi-layer perceptron artificial neural networks with a tree-seed algorithm," Arabian Journal for Science and Engineering, vol. 45, no. 12, pp. 10915-10938, 2020. https://doi.org/10.1007/s13369-020-04872-1
  • M. Gündüz, M. S. Kiran, and E. Özceylan, "A hierarchic approach based on swarm intelligence to solve the traveling salesman problem," Turkish Journal of Electrical Engineering & Computer Sciences, vol. 23, no.1,pp.103-117,2015. https://doi.org/10.3906/elk-1210-147
  • A. C. Cinar, S. Korkmaz, and M. S. Kiran, "A discrete tree-seed algorithm for solving symmetric traveling salesman problem," Engineering Science and Technology, an International Journal, vol. 23, no. 4, pp. 879-890, 2020. https://doi.org/10.1016/j.jestch.2019.11.005
  • 2003 M. Kumar and J. S. Dhillon, "Hybrid artificial algae algorithm for economic load dispatch," (in English), Applied Soft Computing, vol. 71, pp. 89-109, Oct 2018, doi: 10.1016/j.asoc.2018.06.035. https://doi.org/10.1016/j.asoc.2018.06.035
  • M. Beşkirli, İ. Koç, H. Haklı, and H. Kodaz, "A new optimization algorithm for solving wind turbine placement problem: Binary artificial algae algorithm," Renewable energy, vol. 121, pp. 301-308. https://doi.org/10.1016/j.renene.2017.12.087
  • E. Kaya, "BinGSO: galactic swarm optimization powered by binary artificial algae algorithm for solving uncapacitated facility location problems," Neural Computing and Applications, pp. 1-20, 2022. https://doi.org/10.1007/s00521-022-07058-y
  • S. Ozsari, H. Uguz, and H. Hakli, "Implementation of meta-heuristic optimization algorithms for interview problem in land consolidation: A case study in Konya/Turkey," Land Use Policy, vol. 108, p. 105511, 2021 https://doi.org/10.1016/j.landusepol.2021.105511
  • A. C. Cinar and N. Natarajan, "An artificial neural network optimized by grey wolf optimizer for prediction of hourly wind speed in Tamil Nadu, India," Intelligent Systems with Applications, p. 200138, 2022. https://doi.org/10.1016/j.iswa.2022.200138
  • B. Turkoglu and E. Kaya, "Training multi-layer perceptron with artificial algae algorithm," Engineering Science and Technology, an International Journal, 2020. https://doi.org/10.1016/j.jestch.2020.07.001
  • B. Turkoglu, S. A. Uymaz, and E. Kaya, "Clustering analysis through artificial algae algorithm," International Journal of Machine Learning and Cybernetics, vol. 13, no. 4, pp. 1179-1196, 2022. https://doi.org/10.1016/j.asoc.2022.108630
  • B. Turkoglu, S. A. Uymaz, and E. Kaya, "Binary Artificial Algae Algorithm for feature selection," Applied Soft Computing, vol. 120, p. 108630, 2022. https://doi.org/10.1007/s13042-022-01518-6
  • E. Kaya, S. Korkmaz, M. A. Sahman, and A. C. Cinar, "DEBOHID: A differential evolution based oversampling approach for highly imbalanced datasets," Expert Systems with Applications, vol. 169, 2021. https://doi.org/10.1016/j.eswa.2020.114482
  • S. A. Uymaz, G. Tezel, and E. Yel, "Artificial algae algorithm (AAA) for nonlinear global optimization," Applied Soft Computing, vol. 31, pp. 153-171, 2015. https://doi.org/10.1016/j.asoc.2015.03.003
  • M. A. Akbari, M. Zare, R. Azizipanah-Abarghooee, S. Mirjalili, and M. Deriche, "The cheetah optimizer: A nature-inspired metaheuristic algorithm for large-scale optimization problems," Scientific reports, vol. 12, no. 1, pp. 1-20, 2022. https://doi.org/10.1038/s41598-022-14338-z
  • M. Jafari, E. Salajegheh, and J. Salajegheh, "Elephant clan optimization: A nature-inspired metaheuristic algorithm for the optimal design of structures," Applied Soft Computing, vol. 113, p. 107892, 2021. https://doi.org/10.1016/j.asoc.2021.107892
  • B. Abdollahzadeh, F. Soleimanian Gharehchopogh, and S. Mirjalili, "Artificial gorilla troops optimizer: a new nature‐inspired metaheuristic algorithm for global optimization problems," International Journal of Intelligent Systems, vol. 36, no. 10, pp. 5887-5958, 2021. https://doi.org/10.1002/int.22535
  • F. A. Hashim and A. G. Hussien, "Snake Optimizer: A novel meta-heuristic optimization algorithm," Knowledge-Based Systems, vol. 242, p. 108320, 2022. https://doi.org/10.1016/j.knosys.2022.108320
  • B. Abdollahzadeh, F. S. Gharehchopogh, and S. Mirjalili, "African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems," Computers & Industrial Engineering, vol. 158, p. 107408, 2021. https://doi.org/10.1016/j.cie.2021.107408
  • H. Jia, X. Peng, and C. Lang, "Remora optimization algorithm," Expert Systems with Applications, vol. 185, p. 115665, 2021. https://doi.org/10.1016/j.eswa.2021.115665
  • W. Zhao, L. Wang, and S. Mirjalili, "Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications," Computer Methods in Applied Mechanics and Engineering, vol. 388, p. 114194, 2022. https://doi.org/10.1016/j.cma.2021.114194
  • M. Braik, A. Hammouri, J. Atwan, M. A. Al-Betar, and M. A. Awadallah, "White Shark Optimizer: A novel bio-inspired meta-heuristic algorithm for global optimization problems," Knowledge-Based Systems, vol. 243, p. 108457, 2022. https://doi.org/10.1016/j.knosys.2022.108457
  • A. Faramarzi, M. Heidarinejad, S. Mirjalili, and A. H. Gandomi, "Marine Predators Algorithm: A nature-inspired metaheuristic," Expert Systems with Applications, vol. 152, p. 113377, 2020. https://doi.org/10.1016/j.eswa.2020.113377
  • Y. Jiang, Q. Wu, S. Zhu, and L. Zhang, "Orca predation algorithm: A novel bio-inspired algorithm for global optimization problems," Expert Systems with Applications, vol. 188, p. 116026, 2022. https://doi.org/10.1016/j.eswa.2021.116026
  • S. Mirjalili, A. H. Gandomi, S. Z. Mirjalili, S. Saremi, H. Faris, and S. M. Mirjalili, "Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems," Advances in Engineering Software, vol. 114, pp. 163-191, 2017. https://doi.org/10.1016/j.advengsoft.2017.07.002
  • D. H. Wolpert and W. G. Macready, "No free lunch theorems for optimization," IEEE transactions on evolutionary computation, vol. 1, no. 1, pp. 67-82, 1997. https://doi.org/10.1109/ 4235.585893
  • Y.-C. Ho and D. L. Pepyne, "Simple explanation of the no-free-lunch theorem and its implications," Journal of optimization theory and applications, vol. 115, no. 3, pp. 549-570, 2002. https://doi.org/10.1023/A:1021251113462
  • H. Bingol and M. Yildirim, "Global Optimizasyon İçin Sürü Tabanlı Bir Yaklaşım Salp Sürü Algoritması," Fırat Üniversitesi Fen Bilimleri Dergisi, vol. 33, no. 1, pp. 51-59, 2021.
  • M. Castelli, L. Manzoni, L. Mariot, M. S. Nobile, and A. Tangherloni, "Salp Swarm Optimization: A critical review," Expert Systems with Applications, vol. 189, p. 116029, 2022. https://doi.org/10.1016/j.eswa.2021.116029
  • Y. Şekertekin and Ö. Atan, "An image encryption algorithm using Ikeda and Henon chaotic maps," in 2016 24th Telecommunications Forum (TELFOR), 2016: IEEE, pp. 1-4.
  • S. Mirjalili, S. M. Mirjalili, and A. Lewis, "Grey wolf optimizer," Advances in engineering software, vol. 69, pp. 46-61, 2014. https://doi.org/10.1016/j.advengsoft.2013.12.007
  • S. Mirjalili and A. Lewis, "The whale optimization algorithm," Advances in engineering software, vol. 95, pp. 51-67, 2016. https://doi.org/10.1016/j.advengsoft.2016.01.008
  • L. Abualigah, A. Diabat, S. Mirjalili, M. Abd Elaziz, and A. H. Gandomi, "The arithmetic optimization algorithm," Computer methods in applied mechanics and engineering, vol. 376, p. 113609, 2021. https://doi.org/10.1016/j.cma.2020.113609
  • N. Panagant, N. Pholdee, S. Bureerat, K. Kaen, A. R. Yıldız, and S. M. Sait, "Seagull optimization algorithm for solving real-world design optimization problems," Materials Testing, vol. 62, no. 6, pp. 640-644, 2020. https://doi.org/10.3139/120.111529
  • S. Hassan, K. Kumar, C. D. Raj, and K. Sridhar, "Design and optimisation of pressure vessel using metaheuristic approach," in Applied Mechanics and Materials, 2014, vol. 465: Trans Tech Publ, pp. 401-406. https://doi.org/10.4028/www.scientific.net/AMM.465-466.401
  • A. T. Kamil, H. M. Saleh, and I. H. Abd-Alla, "A Multi-Swarm Structure for Particle Swarm Optimization: Solving the Welded Beam Design Problem," in Journal of Physics: Conference Series, 2021, vol. 1804, no. 1: IOP Publishing, p. 012012.
  • Y. Çelik and H. Kutucu, "Solving the Tension/Compression Spring Design Problem by an Improved Firefly Algorithm," IDDM, vol. 1, no. 2255, pp. 1-7, 2018.
  • G. Kaur and S. Arora, "Chaotic whale optimization algorithm," Journal of Computational Design and Engineering, vol. 5, no. 3, pp. 275-284, 2018. https://doi.org/10.1016/j.jcde.2017.12.006
  • M. Kohli and S. Arora, "Chaotic grey wolf optimization algorithm for constrained optimization problems," Journal of computational design and engineering, vol. 5, no. 4, pp. 458-472, 2018. https://doi.org/10.1016/j.jcde.2017.02.005
  • A. H. Gandomi, X.-S. Yang, S. Talatahari, and A. H. Alavi, "Firefly algorithm with chaos," Communications in Nonlinear Science and Numerical Simulation, vol. 18, no. 1, pp. 89-98, 2013. https://doi.org/10.1016/j.cnsns.2012.06.009
  • G. I. Sayed, G. Khoriba, and M. H. Haggag, "A novel chaotic salp swarm algorithm for global optimization and feature selection," Appl Intell, vol. 48, no. 10, pp. 3462-3481, 2018. https://doi.org/10.1007/s10489-018-1158-6
Toplam 41 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgisayar Yazılımı
Bölüm Makaleler
Yazarlar

Bahaeddin Türkoğlu 0000-0003-0255-8422

Erken Görünüm Tarihi 9 Ekim 2023
Yayımlanma Tarihi 15 Ekim 2023
Gönderilme Tarihi 26 Ekim 2022
Kabul Tarihi 29 Eylül 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 12 Sayı: 4

Kaynak Göster

APA Türkoğlu, B. (2023). An advanced Salp Swarm Algorithm for optimization problems. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 12(4), 1071-1078. https://doi.org/10.28948/ngumuh.1195013
AMA Türkoğlu B. An advanced Salp Swarm Algorithm for optimization problems. NÖHÜ Müh. Bilim. Derg. Ekim 2023;12(4):1071-1078. doi:10.28948/ngumuh.1195013
Chicago Türkoğlu, Bahaeddin. “An Advanced Salp Swarm Algorithm for Optimization Problems”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 12, sy. 4 (Ekim 2023): 1071-78. https://doi.org/10.28948/ngumuh.1195013.
EndNote Türkoğlu B (01 Ekim 2023) An advanced Salp Swarm Algorithm for optimization problems. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 12 4 1071–1078.
IEEE B. Türkoğlu, “An advanced Salp Swarm Algorithm for optimization problems”, NÖHÜ Müh. Bilim. Derg., c. 12, sy. 4, ss. 1071–1078, 2023, doi: 10.28948/ngumuh.1195013.
ISNAD Türkoğlu, Bahaeddin. “An Advanced Salp Swarm Algorithm for Optimization Problems”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 12/4 (Ekim 2023), 1071-1078. https://doi.org/10.28948/ngumuh.1195013.
JAMA Türkoğlu B. An advanced Salp Swarm Algorithm for optimization problems. NÖHÜ Müh. Bilim. Derg. 2023;12:1071–1078.
MLA Türkoğlu, Bahaeddin. “An Advanced Salp Swarm Algorithm for Optimization Problems”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, c. 12, sy. 4, 2023, ss. 1071-8, doi:10.28948/ngumuh.1195013.
Vancouver Türkoğlu B. An advanced Salp Swarm Algorithm for optimization problems. NÖHÜ Müh. Bilim. Derg. 2023;12(4):1071-8.

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