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ADAPTİF KURBAĞA SIÇRAMA ALGORİTMASININ OPERATÖRÜ İLE GELİŞTİRİLMİŞ YAPAY ATOM ALGORİTMASI

Year 2022, Volume: 9 Issue: 17, 366 - 383, 31.08.2022

Ayşe Erdoğan Yıldırım

https://doi.org/10.54365/adyumbd.1080995

Abstract

Yapay Atom Algoritması, doğadan ilham alınarak geliştirilmiş bir optimizasyon tekniğidir. Bu algoritma önceki çalışmalarda hem sürekli problemler hem de ayrık problemler için kullanılmıştır. Bu çalışmada bu algoritmanın başarısını artıracak bir düzenleme öngörülmüştür. Bu amaçla, Yapay Atom Algoritmasının iyonik bağ işlevi, Adaptif Kurbağa Sıçrama Algoritmasının algoritmik bir adımından yararlanılarak geliştirilmiştir. Güncellemeler sonucunda iyonik bağ operatörü için arama alanı daraltılmıştır. Böylece her iterasyonda çözümden uzaklaşma durumu önlenmiştir. Geliştirilmiş Yapay Atom Algoritmasının başarısı, kıyaslama fonksiyonları ile test edilmiştir. Önerilen yöntem için deneysel sonuçlar karşılaştırmalı olarak yorumlanmıştır.

Keywords

Yapay atom algoritması Geliştirilmiş yapay atom algoritması Adaptif kurbağa sıçrama algoritması Optimizasyon Kıyaslama problemleri

References

  • Yıldırım AE. Yapay atom algoritması ve ayrık problemlere uygulanması (in Turkish). PhD. Thesis, Institute of Natural and Applied Science, Malatya: Inonu University, Turkey; 2018.
  • Pérez J, Valdez F, Castillo O. Bat algorithm comparison with genetic algorithm using benchmark functions. In: Castillo O, Melin P, Pedrycz W, Kacprzyk J. (eds) Recent Advances on Hybrid Approaches for Designing Intelligent Systems. Studies in Computational Intelligence, 547, Springer, Cham; 2014
  • Onwubolu GC, Clerc M. Optimal path for automated drilling operations by a new heuristic approach using particle swarm optimization. Int J Prod Res 2004; 42(3), 473–491.
  • Karcı A. Theory of saplings growing up algorithm. In: Beliczynski B, Dzielinski A, Iwanowski M, Ribeiro B. (Ed.), Adaptive and Natural Computing Algorithms. Lecture Notes in Computer Science, 4431, 450-460, Springer, Berlin, Heidelberg; 2007.
  • Wong LP, Low MYH, Chong CS. A bee colony optimization algorithm for traveling salesman problem. Proceedings of Second Asia International Conference on Modelling & Simulation (AMS), Kuala Lumpur, Malaysia; 2008.
  • Dorigo M, Stutzle T. Ant colony optimization. MIT Press, Cambridge, MA; 2004.
  • Karcı A. Differential evolution algorithm and its variants. Anatolian Journal of Computer Sciences 2017; 2(1), 10-14.
  • Kızıloluk S, Alataş B. Automatic mining of numerical classification rules with parliamentary optimization algorithm. Advances in Electrical and Computer Engineering 2015; 15(4), 17-24.
  • Alataş B. Uniform big bang - chaotic big crunch optimization. Communications in Nonlinear Science and Numerical Simulation 2011; 16, 3696-3703.
  • Alataş B, Özer, AB. Mining of generalized interesting classification rules with artificial chemical reaction optimization algorithm. Journal of the Faculty of Engineering and Architecture of Gazi University 2017; 32, 101-118.
  • Canayaz M, Karcı A. Cricket behaviour-based evolutionary computation technique in solving engineering optimization problems. Journal of Applied Intelligence 2016; 44, 362-376.
  • Yang XS, Gandomi AH. Bat algorithm: a novel approach for global engineering optimization. Journal of Engineering Computations 2012; 29, 464-483.
  • Lee KS, Geem ZW. A new structural optimization method based on the harmony search algorithm. Journal of Computers and Structures 2004; 82(9-10), 781-798.
  • Eusuff M, Lansey K, Pasha F. Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Journal of Engineering Optimization 2006; 38, 129-154.
  • Yıldırım AE, Karcı A. Applications of artificial atom algorithm to small-scale traveling salesman problems. Soft Computing 2018; 22(22), 7619-7631.
  • Sastry K, Goldberg D, Kendall G. Genetic Algorithms. In: Burke EK, Kendall G. (eds) Search Methodologies. Springer, Boston, MA; 2005.
  • Jain NK, Nangia U, Jain J. A Review of Particle Swarm Optimization. J. Inst. Eng. India Ser. B 2018; 99, 407–411.
  • Demir M, Karcı A, Özdemir M. Fidan gelişim algoritması yardımı ile DNA motiflerinin keşfi (in Turkish). Çankaya University Journal of Science and Engineering 2011; 8, 51-62.
  • Mallipeddi R, Suganthana PN, Panb QK, Tasgetiren MF. Differential evolution algorithm with ensemble of parameters and mutation strategies. Applied Soft Computing 2011; 11(2), 1679-1696.
  • Altunbey F, Alataş B. Overlapping community detection in social networks using parliamentary optimization algorithm. International Journal of Computer Networks and Applications 2015; 2, 12-19.
  • Erol OK, Eksin I. A new optimization method: Big Bang–Big Crunch. Advances in Engineering Software 2006; 37, 106-111.
  • Karaboğa D. Yapay Zekâ Optimizasyon Algoritmaları (in Turkish), Nobel Publisher, Istanbul, Turkey; 2017.
  • Canayaz M. Cırcır böceği algoritması: Yeni bir meta-sezgisel yaklaşım ve uygulamaları (in Turkish). PhD. Thesis, Institute of Natural and Applied Science, Malatya: Inonu University, Turkey; 2015.
  • Yetkin, M. Metasezgisel algoritmaların Jeodezi’de kullanımı (in Turkish). Journal of Geomatic Research 2016; 1(1), 8-13.
  • Altun S, Varjovi MH, Karcı, A. Performance comparison of different optimization methods under the same conditions. Proceedings of 3nd International Conference on Artificial Intelligence and Data Processing (IDAP), Malatya, Turkey; 2018.
  • Yıldırım AE, Karcı A. Solutions of travelling salesman problem using genetic algorithm and atom algorithm. Proceedings of 2nd International Eurasian Conference on Mathematical Sciences and Applications (IECMSA), Sarajevo, Bosnia and Herzegovina; 2013.
  • Canayaz M, Demir M. Veri kümelemede yapay atom algoritması ve cırcır böceği algoritmasının karşılaştırılmalı analizi. Proceedings of 4th International Symposium on Innovative Technologies in Engineering and Science (ISITES), Antalya, Turkey; 2016.
  • Yıldırım AE, Karcı A. Group elevator control optimization using artificial atom algorithm. Proceedings of 2nd International Conference on Artificial Intelligence and Data Processing (IDAP), Malatya, Turkey; 2017.
  • Yıldırım AE, Karcı A. Application of traveling salesman problem for 81 provinces in Turkey using artificial atom algorithm. Proceedings of 7th International Conference on Advanced Technology & Science (ICAT), Antalya, Turkey; 2018.
  • Yıldırım AE, Karcı A. Application of three bar truss problem among engineering design optimization problems using artificial atom algorithm. Proceedings of 3nd International Conference on Artificial Intelligence and Data Processing (IDAP), Malatya, Turkey; 2018.
  • Hussain K, Salleh MNM, Cheng S, Naseem R. Common benchmark functions for metaheuristic evaluation: A review. International Journal of Informatics Visualization 2017; 1, 218-223.
  • Agushaka OJ, Ezugwu AE-S. Influence of initializing krill herd algorithm with low-discrepancy sequences. IEEE Access 2020; 8, 210891.
  • Dündar A, İzci D, Ekinci S, Eker E. A novel modified lévy flight distribution algorithm based on nelder-mead method for function optimization. DUJE 2021; 12(3), 487-497.
  • Bingöl H, Yıldırım M. Global optimizasyon için sürü tabanlı bir yaklaşım salp sürü algoritması. Fırat Üniversitesi Fen Bilimleri Dergisi 2021; 33, 51-59.

AN IMPROVED ARTIFICIAL ATOM ALGORITHM WITH THE OPERATOR OF SHUFFLED FROG LEAPING ALGORITHM

Year 2022, Volume: 9 Issue: 17, 366 - 383, 31.08.2022

Ayşe Erdoğan Yıldırım

https://doi.org/10.54365/adyumbd.1080995

Abstract

Artificial Atom Algorithm is an optimization technique that developed inspired by nature. This algorithm used for both continues problems and discrete problems in previous studies. In this study, an arrangement that would increase the success of this algorithm was envisaged. For this purpose, the ionic bond function of Artificial Atom Algorithm has been improved benefiting an algorithmic step of Shuffled Frog Leaping Algorithm. As a result of the updates, the search space was narrowed for the ionic bond operator. Thus, the state of getting away from the solution in each iteration was prevented. The success of Improved Artificial Atom Algorithm was tested with benchmark functions. Experimental results for the proposed method were interpreted comparatively.

Keywords

Artificial atom algorithm Improved artificial atom algorithm Shuffled frog leaping algorithm Optimization Benchmark problems

References

  • Yıldırım AE. Yapay atom algoritması ve ayrık problemlere uygulanması (in Turkish). PhD. Thesis, Institute of Natural and Applied Science, Malatya: Inonu University, Turkey; 2018.
  • Pérez J, Valdez F, Castillo O. Bat algorithm comparison with genetic algorithm using benchmark functions. In: Castillo O, Melin P, Pedrycz W, Kacprzyk J. (eds) Recent Advances on Hybrid Approaches for Designing Intelligent Systems. Studies in Computational Intelligence, 547, Springer, Cham; 2014
  • Onwubolu GC, Clerc M. Optimal path for automated drilling operations by a new heuristic approach using particle swarm optimization. Int J Prod Res 2004; 42(3), 473–491.
  • Karcı A. Theory of saplings growing up algorithm. In: Beliczynski B, Dzielinski A, Iwanowski M, Ribeiro B. (Ed.), Adaptive and Natural Computing Algorithms. Lecture Notes in Computer Science, 4431, 450-460, Springer, Berlin, Heidelberg; 2007.
  • Wong LP, Low MYH, Chong CS. A bee colony optimization algorithm for traveling salesman problem. Proceedings of Second Asia International Conference on Modelling & Simulation (AMS), Kuala Lumpur, Malaysia; 2008.
  • Dorigo M, Stutzle T. Ant colony optimization. MIT Press, Cambridge, MA; 2004.
  • Karcı A. Differential evolution algorithm and its variants. Anatolian Journal of Computer Sciences 2017; 2(1), 10-14.
  • Kızıloluk S, Alataş B. Automatic mining of numerical classification rules with parliamentary optimization algorithm. Advances in Electrical and Computer Engineering 2015; 15(4), 17-24.
  • Alataş B. Uniform big bang - chaotic big crunch optimization. Communications in Nonlinear Science and Numerical Simulation 2011; 16, 3696-3703.
  • Alataş B, Özer, AB. Mining of generalized interesting classification rules with artificial chemical reaction optimization algorithm. Journal of the Faculty of Engineering and Architecture of Gazi University 2017; 32, 101-118.
  • Canayaz M, Karcı A. Cricket behaviour-based evolutionary computation technique in solving engineering optimization problems. Journal of Applied Intelligence 2016; 44, 362-376.
  • Yang XS, Gandomi AH. Bat algorithm: a novel approach for global engineering optimization. Journal of Engineering Computations 2012; 29, 464-483.
  • Lee KS, Geem ZW. A new structural optimization method based on the harmony search algorithm. Journal of Computers and Structures 2004; 82(9-10), 781-798.
  • Eusuff M, Lansey K, Pasha F. Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Journal of Engineering Optimization 2006; 38, 129-154.
  • Yıldırım AE, Karcı A. Applications of artificial atom algorithm to small-scale traveling salesman problems. Soft Computing 2018; 22(22), 7619-7631.
  • Sastry K, Goldberg D, Kendall G. Genetic Algorithms. In: Burke EK, Kendall G. (eds) Search Methodologies. Springer, Boston, MA; 2005.
  • Jain NK, Nangia U, Jain J. A Review of Particle Swarm Optimization. J. Inst. Eng. India Ser. B 2018; 99, 407–411.
  • Demir M, Karcı A, Özdemir M. Fidan gelişim algoritması yardımı ile DNA motiflerinin keşfi (in Turkish). Çankaya University Journal of Science and Engineering 2011; 8, 51-62.
  • Mallipeddi R, Suganthana PN, Panb QK, Tasgetiren MF. Differential evolution algorithm with ensemble of parameters and mutation strategies. Applied Soft Computing 2011; 11(2), 1679-1696.
  • Altunbey F, Alataş B. Overlapping community detection in social networks using parliamentary optimization algorithm. International Journal of Computer Networks and Applications 2015; 2, 12-19.
  • Erol OK, Eksin I. A new optimization method: Big Bang–Big Crunch. Advances in Engineering Software 2006; 37, 106-111.
  • Karaboğa D. Yapay Zekâ Optimizasyon Algoritmaları (in Turkish), Nobel Publisher, Istanbul, Turkey; 2017.
  • Canayaz M. Cırcır böceği algoritması: Yeni bir meta-sezgisel yaklaşım ve uygulamaları (in Turkish). PhD. Thesis, Institute of Natural and Applied Science, Malatya: Inonu University, Turkey; 2015.
  • Yetkin, M. Metasezgisel algoritmaların Jeodezi’de kullanımı (in Turkish). Journal of Geomatic Research 2016; 1(1), 8-13.
  • Altun S, Varjovi MH, Karcı, A. Performance comparison of different optimization methods under the same conditions. Proceedings of 3nd International Conference on Artificial Intelligence and Data Processing (IDAP), Malatya, Turkey; 2018.
  • Yıldırım AE, Karcı A. Solutions of travelling salesman problem using genetic algorithm and atom algorithm. Proceedings of 2nd International Eurasian Conference on Mathematical Sciences and Applications (IECMSA), Sarajevo, Bosnia and Herzegovina; 2013.
  • Canayaz M, Demir M. Veri kümelemede yapay atom algoritması ve cırcır böceği algoritmasının karşılaştırılmalı analizi. Proceedings of 4th International Symposium on Innovative Technologies in Engineering and Science (ISITES), Antalya, Turkey; 2016.
  • Yıldırım AE, Karcı A. Group elevator control optimization using artificial atom algorithm. Proceedings of 2nd International Conference on Artificial Intelligence and Data Processing (IDAP), Malatya, Turkey; 2017.
  • Yıldırım AE, Karcı A. Application of traveling salesman problem for 81 provinces in Turkey using artificial atom algorithm. Proceedings of 7th International Conference on Advanced Technology & Science (ICAT), Antalya, Turkey; 2018.
  • Yıldırım AE, Karcı A. Application of three bar truss problem among engineering design optimization problems using artificial atom algorithm. Proceedings of 3nd International Conference on Artificial Intelligence and Data Processing (IDAP), Malatya, Turkey; 2018.
  • Hussain K, Salleh MNM, Cheng S, Naseem R. Common benchmark functions for metaheuristic evaluation: A review. International Journal of Informatics Visualization 2017; 1, 218-223.
  • Agushaka OJ, Ezugwu AE-S. Influence of initializing krill herd algorithm with low-discrepancy sequences. IEEE Access 2020; 8, 210891.
  • Dündar A, İzci D, Ekinci S, Eker E. A novel modified lévy flight distribution algorithm based on nelder-mead method for function optimization. DUJE 2021; 12(3), 487-497.
  • Bingöl H, Yıldırım M. Global optimizasyon için sürü tabanlı bir yaklaşım salp sürü algoritması. Fırat Üniversitesi Fen Bilimleri Dergisi 2021; 33, 51-59.
There are 34 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Makaleler
Authors

Ayşe Erdoğan Yıldırım 0000-0002-8983-8410

Publication Date August 31, 2022
Submission Date March 1, 2022
Published in Issue Year 2022 Volume: 9 Issue: 17

Cite

APA Erdoğan Yıldırım, A. (2022). AN IMPROVED ARTIFICIAL ATOM ALGORITHM WITH THE OPERATOR OF SHUFFLED FROG LEAPING ALGORITHM. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, 9(17), 366-383. https://doi.org/10.54365/adyumbd.1080995
AMA Erdoğan Yıldırım A. AN IMPROVED ARTIFICIAL ATOM ALGORITHM WITH THE OPERATOR OF SHUFFLED FROG LEAPING ALGORITHM. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi. August 2022;9(17):366-383. doi:10.54365/adyumbd.1080995
Chicago Erdoğan Yıldırım, Ayşe. “AN IMPROVED ARTIFICIAL ATOM ALGORITHM WITH THE OPERATOR OF SHUFFLED FROG LEAPING ALGORITHM”. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 9, no. 17 (August 2022): 366-83. https://doi.org/10.54365/adyumbd.1080995.
EndNote Erdoğan Yıldırım A (August 1, 2022) AN IMPROVED ARTIFICIAL ATOM ALGORITHM WITH THE OPERATOR OF SHUFFLED FROG LEAPING ALGORITHM. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 9 17 366–383.
IEEE A. Erdoğan Yıldırım, “AN IMPROVED ARTIFICIAL ATOM ALGORITHM WITH THE OPERATOR OF SHUFFLED FROG LEAPING ALGORITHM”, Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, vol. 9, no. 17, pp. 366–383, 2022, doi: 10.54365/adyumbd.1080995.
ISNAD Erdoğan Yıldırım, Ayşe. “AN IMPROVED ARTIFICIAL ATOM ALGORITHM WITH THE OPERATOR OF SHUFFLED FROG LEAPING ALGORITHM”. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 9/17 (August 2022), 366-383. https://doi.org/10.54365/adyumbd.1080995.
JAMA Erdoğan Yıldırım A. AN IMPROVED ARTIFICIAL ATOM ALGORITHM WITH THE OPERATOR OF SHUFFLED FROG LEAPING ALGORITHM. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi. 2022;9:366–383.
MLA Erdoğan Yıldırım, Ayşe. “AN IMPROVED ARTIFICIAL ATOM ALGORITHM WITH THE OPERATOR OF SHUFFLED FROG LEAPING ALGORITHM”. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, vol. 9, no. 17, 2022, pp. 366-83, doi:10.54365/adyumbd.1080995.
Vancouver Erdoğan Yıldırım A. AN IMPROVED ARTIFICIAL ATOM ALGORITHM WITH THE OPERATOR OF SHUFFLED FROG LEAPING ALGORITHM. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi. 2022;9(17):366-83.
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