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A binary enhanced moth flame optimization algorithm for uncapacitated facility location problems

Yıl 2023, Cilt: 29 Sayı: 7, 737 - 751, 30.12.2023

Öz

Moth Flame Optimization is a nature-inspired meta-heuristic algorithm for constantly solving real-world problems. In this study, a modified version of MFO called binary Enhanced MFO Desert Bush (binEMFO-DB) algorithm is proposed to solve uncapacitated facility location problems. The proposed algorithm includes three modifications: i) chaotic mapbased population initialization, ii) random flame selection, and iii) desert bush strategy. The performance of the proposed binEMFO-DB algorithm was tested on 15 different UFL problems from the OR-Library and Taguchi orthogonal array design was used for parameter analysis. The average, gap and hit values of the results obtained by the algorithms were used as performance metrics. The performance of binEMFO-DB is compared with the performance of state-of-the-art algorithms. The results show that the proposed binEMFO-DB has a successful and competitive performance in the test environment.

Kaynakça

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Kapasitesiz tesis yerleşim problemleri için geliştirilmiş ikili güve alevi optimizasyon algoritması

Yıl 2023, Cilt: 29 Sayı: 7, 737 - 751, 30.12.2023

Öz

Güve Alevi Optimizasyonu, sürekli gerçek dünya problemlerini çözmek için doğadan ilham alan bir meta-sezgisel algoritmadır. Bu çalışmada, kapasitesiz tesis yerleşim problemlerini çözmek için ikili Enhanced MFO Desert Bush (binEMFO-DB) algoritması olarak adlandırılan MFO'nun değiştirilmiş bir versiyonu önerilmiştir. Önerilen algoritma üç değişiklik içermektedir: i) kaotik harita tabanlı popülasyon başlatma, ii) rastgele alev seçimi ve iii) çöl çalısı stratejisi. Önerilen binEMFO-DB algoritmasının performansı, OR-Library'den alınan 15 farklı UFL problemi üzerinde test edilmiş ve parametre analizi için Taguchi ortogonal dizi tasarımı kullanılmıştır. Algoritmalar ile elde edilen sonuçların ortalama, boşluk ve isabet değerleri performans metriği olarak kullanılmıştır. binEMFO-DB'nin performansı, son teknoloji algoritmaların performanslarıyla karşılaştırılmıştır. Elde edilen sonuçlar, önerilen binEMFO-DB'nin test ortamında başarılı ve rekabetçi bir performansa sahip olduğunu göstermektedir.

Kaynakça

  • [1] Babalik A, Cinar AC, Kiran MS. "A modification of tree-seed algorithm using Deb’s rules for constrained optimization". Applied Soft Computing, 63, 289-305, 2018.
  • [2] Yuan X, Nie H, Su A, Wang L, Yuan Y. "An improved binary particle swarm optimization for unit commitment problem". Expert Systems with Applications, 36(4), 8049-8055, 2009.
  • [3] Van Beers F, Lindström A, Okafor E,Wiering MA. "Deep Neural Networks with Intersection over Union Loss for Binary Image Segmentation". ICPRAM. Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods, Prague, Czech Republic, 19-21 February 2019.
  • [4] Banitalebi A, Aziz MIA, Aziz ZA. "A self-adaptive binary differential evolution algorithm for large scale binary optimization problems". Information Sciences, 367, 487-511, 2016.
  • [5] Ayaz HI, Ozturk ZK. "A mathematical model and a heuristic approach for train seat scheduling to minimize dwell time". Computers & Industrial Engineering, 160, 1-7, 2021.
  • [6] Aslan M, Gunduz M, Kiran MS. "JayaX: Jaya algorithm with xor operator for binary optimization". Applied Soft Computing, 82, 1-17, 2019.
  • [7] Baş E, Ülker E. "A binary social spider algorithm for uncapacitated facility location problem". Expert Systems with Applications, 161, 1-27, 2020.
  • [8] Rizk-Allah RM, Hassanien AE, Elhoseny M, Gunasekaran M. "A new binary salp swarm algorithm: development and application for optimization tasks". Neural Computing and Applications, 31(5), 1641-1663, 2019.
  • [9] Eberhart R, Kennedy J. "A new optimizer using particle swarm theory". MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, 04-06 October 1995.
  • [10] Mirjalili S, Mirjalili SM,Lewis A. "Grey Wolf Optimizer". Advances in Engineering Software, 69, 46-61, 2014.
  • [11] Karaboga D, Akay B. "A comparative study of artificial bee colony algorithm". Applied mathematics and computation, 214(1), 108-132, 2009.
  • [12] Uymaz SA, Tezel G, Yel E. "Artificial algae algorithm (AAA) for nonlinear global optimization", Applied Soft Computing, 31, 153-171, 2015.
  • [13] Kennedy J, Eberhart RC. "A discrete binary version of the particle swarm algorithm". 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, Orlando, USA, 12-15 October 1997.
  • [14] Beheshti Z, Shamsuddin SM, Hasan S. "Memetic binary particle swarm optimization for discrete optimization problems". Information Sciences, 299, 58-84, 2015.
  • [15] Mirjalili S, Lewis A. "S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization". Swarm and Evolutionary Computation, 9, 1-14, 2013.
  • [16] Pampara G, Engelbrecht AP, Franken N. "Binary Differential Evolution". 2006 IEEE International Conference on Evolutionary Computation, Vancouver, Canada, 16-21 July 2006.
  • [17] Lim SM, Sultan ABM, Sulaiman MN, Mustapha A, Leong KY. "Crossover and mutation operators of genetic algorithms". International Journal of Machine Learning and Computing, 7(1), 9-12, 2017.
  • [18] Braun H. "On solving travelling salesman problems by genetic algorithms". International Conference on Parallel Problem Solving from Nature, Dortmund, Germany, 01-03 October 1990.
  • [19] Ozturk C, Hancer E, Karaboga D. "A novel binary artificial bee colony algorithm based on genetic operators". Information Sciences, 297, 154-170, 2015.
  • [20] Iwasaki Y, Kusne AG, Takeuchi I. "Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries". NPJ Computational Materials, 3(1), 1-9, 2017.
  • [21] Chen Y, Xie W,Zou X. "A binary differential evolution algorithm learning from explored solutions". Neurocomputing, 149, 1038-1047, 2015.
  • [22] Nezamabadi-pour H. "A quantum-inspired gravitational search algorithm for binary encoded optimization problems". Engineering Applications of Artificial Intelligence, 40, 62-75, 2015.
  • [23] Mojtaba Ahmadieh K, Mohammad T, Mahdi Aliyari S. "A novel binary particle swarm optimization". 2007 Mediterranean Conference on Control & Automation, Athens, Greece, 27-29 June 2007.
  • [24] Lin JC-W, Yang L, Fournier-Viger P, Hong T-P,Voznak M. "A binary PSO approach to mine high-utility itemsets". Soft Computing, 21(17), 5103-5121, 2017.
  • [25] Nezamabadi-pour H, Rostami-Shahrbabaki M, MaghfooriFarsangi M. "Binary particle swarm optimization: challenges and new solutions". CSI Journal on Computer Science and Engineering, 6(1), 21-32, 2008.
  • [26] Guner AR, Sevkli M. "A discrete particle swarm optimization algorithm for uncapacitated facility location problem". Journal of Artificial Evolution and Applications, 1, 1-9, 2008.
  • [27] Saha S, Kole A, Dey K. "A modified continuous particle swarm optimization algorithm for uncapacitated facility location problem". International Conference on Advances in Information Technology and Mobile Communication, Nagpur, India, 21-22 April 2011.
  • [28] Karaboga D. "An İdea Based on Honey Bee Swarm for Numerical Optimization". Department of Computer Engineering, Erciyes University, Kayseri, Turkey, Technical Report-TR06, 2005.
  • [29] Kashan MH, Nahavandi N, Kashan AH. "DisABC: a new artificial bee colony algorithm for binary optimization". Applied Soft Computing, 12(1), 342-352, 2012.
  • [30] Kiran MS, Gündüz M. "XOR-based artificial bee colony algorithm for binary optimization". Turkish Journal of Electrical Engineering and Computer Sciences, 21(8), 2307-2328, 2013.
  • [31] Kiran MS. "A binary artificial bee colony algorithm and its performance assessment". Expert Systems with Applications, 175, 1-15, 2021.
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  • [35] Su H, Yang Y. "Quantum-Inspired Differential Evolution for Binary Optimization". 2008 Fourth International Conference on Natural Computation, Jinan, China, 18-20 October 2008.
  • [36] He X, Zhang Q, Sun N, Dong Y. "Feature Selection with Discrete Binary Differential Evolution". 2009 International Conference on Artificial Intelligence and Computational Intelligence, Shanghai, China, 7-8 November 2009.
  • [37] Deng C, Zhao B, Yang Y, Deng A. "Novel Binary Differential Evolution Algorithm for Discrete Optimization". 2009 Fifth International Conference on Natural Computation, Tianjian, China, 14-16 August 2009.
  • [38] Qingyun Y. "A comparative study of discrete differential evolution on binary constraint satisfaction problems". 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), Hong Kong, 01-06 June 2008.
  • [39] Wang L, Fu X, Menhas MI, Fei M. "A Modified Binary Differential Evolution Algorithm". International Conference on Life System Modeling and Simulation, Wuxi, China, 17-20 September 2010.
  • [40] Kashan MH, Kashan AH, Nahavandi N. "A novel differential evolution algorithm for binary optimization". Computational Optimization and Applications, 55(2), 481-513, 2013.
  • [41] Rashedi E, Nezamabadi-pour H, Saryazdi S. "GSA: A Gravitational Search Algorithm". Information Sciences, 179(13), 2232-2248, 2009.
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Toplam 97 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Algoritmalar ve Hesaplama Kuramı
Bölüm Makale
Yazarlar

Ahmet Özkış

Murat Karakoyun

Yayımlanma Tarihi 30 Aralık 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 29 Sayı: 7

Kaynak Göster

APA Özkış, A., & Karakoyun, M. (2023). A binary enhanced moth flame optimization algorithm for uncapacitated facility location problems. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 29(7), 737-751.
AMA Özkış A, Karakoyun M. A binary enhanced moth flame optimization algorithm for uncapacitated facility location problems. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. Aralık 2023;29(7):737-751.
Chicago Özkış, Ahmet, ve Murat Karakoyun. “A Binary Enhanced Moth Flame Optimization Algorithm for Uncapacitated Facility Location Problems”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 29, sy. 7 (Aralık 2023): 737-51.
EndNote Özkış A, Karakoyun M (01 Aralık 2023) A binary enhanced moth flame optimization algorithm for uncapacitated facility location problems. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 29 7 737–751.
IEEE A. Özkış ve M. Karakoyun, “A binary enhanced moth flame optimization algorithm for uncapacitated facility location problems”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, c. 29, sy. 7, ss. 737–751, 2023.
ISNAD Özkış, Ahmet - Karakoyun, Murat. “A Binary Enhanced Moth Flame Optimization Algorithm for Uncapacitated Facility Location Problems”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 29/7 (Aralık 2023), 737-751.
JAMA Özkış A, Karakoyun M. A binary enhanced moth flame optimization algorithm for uncapacitated facility location problems. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2023;29:737–751.
MLA Özkış, Ahmet ve Murat Karakoyun. “A Binary Enhanced Moth Flame Optimization Algorithm for Uncapacitated Facility Location Problems”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, c. 29, sy. 7, 2023, ss. 737-51.
Vancouver Özkış A, Karakoyun M. A binary enhanced moth flame optimization algorithm for uncapacitated facility location problems. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2023;29(7):737-51.





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