Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2021, , 513 - 529, 15.04.2021
https://doi.org/10.16984/saufenbilder.770367

Öz

Kaynakça

  • Wolpert, D. and Macready, W., 1997. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), pp.67-82.
  • J. H. Holland, “Genetic Algorithms,” Scientific American, vol. 267, no. 1, pp. 66–72, 1992.
  • J.-T. Tsai, T.-K. Liu, and J.-H. Chou, “Hybrid Taguchi-Genetic Algorithm for Global Numerical Optimization,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 4, pp. 365–377, 2004.
  • C.-F. Juang, “A Hybrid of Genetic Algorithm and Particle Swarm Optimization for Recurrent Network Design,” IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol. 34, no. 2, pp. 997–1006, 2004.
  • E. Falkenauer, “A hybrid grouping genetic algorithm for bin packing,” Journal of Heuristics, vol. 2, no. 1, pp. 5–30, 1996.
  • D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm,” Journal of Global Optimization, vol. 39, no. 3, pp. 459–471, 2007.
  • C. Ozturk and D. Karaboga, “Hybrid Artificial Bee Colony algorithm for neural network training,” 2011 IEEE Congress of Evolutionary Computation (CEC), 2011.
  • X. Yan, Y. Zhu, W. Zou, and L. Wang, “A new approach for data clustering using hybrid artificial bee colony algorithm,” Neurocomputing, vol. 97, pp. 241–250, 2012.
  • F. Kang, J. Li, and Q. Xu, “Structural inverse analysis by hybrid simplex artificial bee colony algorithms,” Computers & Structures, vol. 87, no. 13-14, pp. 861–870, 2009.
  • Z. W. Geem, J. H. Kim, and G. Loganathan, “A New Heuristic Optimization Algorithm: Harmony Search,” Simulation, vol. 76, no. 2, pp. 60–68, 2001.
  • G. Wang and L. Guo, “A Novel Hybrid Bat Algorithm with Harmony Search for Global Numerical Optimization,” Journal of Applied Mathematics, vol. 2013, pp. 1–21, 2013.
  • A. R. Yıldız, «Hybrid Taguchi‐Harmony Search Algorithm for Solving,» International Journal of Industrial Engineering Theory, Applications and Practice, cilt 15, no. 3, pp. 286-93, 2008.
  • B. Wu, C. Qian, W. Ni, and S. Fan, “Hybrid harmony search and artificial bee colony algorithm for global optimization problems,” Computers & Mathematics with Applications, vol. 64, no. 8, pp. 2621–2634, 2012.
  • S. Mirjalili, “SCA: A Sine Cosine Algorithm for solving optimization problems,” Knowledge-Based Systems, vol. 96, pp. 120–133, 2016.
  • S. H. R. Pasandideh və S. Khalilpourazari, “SINE COSINE CROW SEARCH ALGORITHM: A POWERFUL HYBRID META HEURISTIC FOR GLOBAL OPTIMIZATION,” arXiv preprint arXiv:1801.08485, 2018.
  • S. Khalilpourazari and S. Khalilpourazary, “SCWOA: an efficient hybrid algorithm for parameter optimization of multi-pass milling process,” Journal of Industrial and Production Engineering, vol. 35, no. 3, pp. 135–147, 2018.
  • N. Singh and S. Singh, “A novel hybrid GWO-SCA approach for optimization problems,” Engineering Science and Technology, an International Journal, vol. 20, no. 6, pp. 1586–1601, 2017.
  • S. Mirjalili, S. M. Mirjalili, and A. Hatamlou, “Multi-Verse Optimizer: a nature-inspired algorithm for global optimization,” Neural Computing and Applications, vol. 27, no. 2, pp. 495–513, 2015.
  • P. Jangir, S. A. Parmar, I. N. Trivedi, and R. Bhesdadiya, “A novel hybrid Particle Swarm Optimizer with multi verse optimizer for global numerical optimization and Optimal Reactive Power Dispatch problem,” Engineering Science and Technology, an International Journal, vol. 20, no. 2, pp. 570–586, 2017.
  • M. Sulaiman, S. Ahmad, J. Iqbal, A. Khan, and R. Khan, “Optimal Operation of the Hybrid Electricity Generation System Using Multiverse Optimization Algorithm,” Computational Intelligence and Neuroscience, vol. 2019, pp. 1–12, 2019.
  • X. Wang, D. Luo, X. Zhao, and Z. Sun, “Estimates of energy consumption in China using a self-adaptive multi-verse optimizer-based support vector machine with rolling cross-validation,” Energy, vol. 152, pp. 539–548, 2018.
  • M. Jamil and X. S. Yang, “A literature survey of benchmark functions for global optimisation problems,” International Journal of Mathematical Modelling and Numerical Optimisation, vol. 4, no. 2, p. 150, 2013.
  • B. Alizada, «Improved Whale Optimization Algorithm Based On π Number,» INTERNATIONAL SCIENTIFIC AND VOCATIONAL JOURNAL (ISVOS JOURNAL), cilt 4, no. 1, pp. 21-30, 2020.
  • S. Mirjalili ve A. Lewis, «The Whale Optimization Algorithm,» Advances in Engineering Software, cilt 95, pp. 51-67, 2016.
  • P. Chakraborty, G. G. Roy, S. Das, D. Jain, and A. Abraham, “An Improved Harmony Search Algorithm with Differential Mutation Operator,” Fundamenta Informaticae, vol. 95, no. 4, pp. 401–426, 2009.
  • Q.-K. Pan, P. Suganthan, M. F. Tasgetiren, and J. Liang, “A self-adaptive global best harmony search algorithm for continuous optimization problems,” Applied Mathematics and Computation, vol. 216, no. 3, pp. 830–848, 2010.
  • X. Gan, E. Jiang, Y. Peng, S. Geng, and M. Kustudic, “Research Optimization on Logistic Distribution Center Location Based on Improved Harmony Search Algorithm,” Lecture Notes in Computer Science Advances in Swarm Intelligence, pp. 410–420, 2018.
  • S. Mirjalili, “The Ant Lion Optimizer,” Advances in Engineering Software, vol. 83, pp. 80–98, 2015.

A Novel Hybrid Algorithm: Sine Cosine Harmony Search Algorithm for Global Optimization

Yıl 2021, , 513 - 529, 15.04.2021
https://doi.org/10.16984/saufenbilder.770367

Öz

The study is about the new hybrid optimization algorithm called Sine Cosine Harmony Search (SCHS). SCHS was created by combining the features of Sine Cosine Algorithm (SCA) and Harmony Search (HS) meta-heuristic algorithms. In the research, the stage of creating the model was confirmed by experiments. For this, it has been tested using global optimization techniques. Benchmark Functions (BF) with different parametric properties were used for testing time. For performance measurement, comparisons were made with various optimization algorithms. The improvement of SCHS on the basis of exploitation and exploration shows that the algorithm is competitive.

Teşekkür

I would like to thank Dr. Seyedali Mirjalili and Seyed Mostapha Kalami Heris for their work in the field and for making them open to us.

Kaynakça

  • Wolpert, D. and Macready, W., 1997. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), pp.67-82.
  • J. H. Holland, “Genetic Algorithms,” Scientific American, vol. 267, no. 1, pp. 66–72, 1992.
  • J.-T. Tsai, T.-K. Liu, and J.-H. Chou, “Hybrid Taguchi-Genetic Algorithm for Global Numerical Optimization,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 4, pp. 365–377, 2004.
  • C.-F. Juang, “A Hybrid of Genetic Algorithm and Particle Swarm Optimization for Recurrent Network Design,” IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol. 34, no. 2, pp. 997–1006, 2004.
  • E. Falkenauer, “A hybrid grouping genetic algorithm for bin packing,” Journal of Heuristics, vol. 2, no. 1, pp. 5–30, 1996.
  • D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm,” Journal of Global Optimization, vol. 39, no. 3, pp. 459–471, 2007.
  • C. Ozturk and D. Karaboga, “Hybrid Artificial Bee Colony algorithm for neural network training,” 2011 IEEE Congress of Evolutionary Computation (CEC), 2011.
  • X. Yan, Y. Zhu, W. Zou, and L. Wang, “A new approach for data clustering using hybrid artificial bee colony algorithm,” Neurocomputing, vol. 97, pp. 241–250, 2012.
  • F. Kang, J. Li, and Q. Xu, “Structural inverse analysis by hybrid simplex artificial bee colony algorithms,” Computers & Structures, vol. 87, no. 13-14, pp. 861–870, 2009.
  • Z. W. Geem, J. H. Kim, and G. Loganathan, “A New Heuristic Optimization Algorithm: Harmony Search,” Simulation, vol. 76, no. 2, pp. 60–68, 2001.
  • G. Wang and L. Guo, “A Novel Hybrid Bat Algorithm with Harmony Search for Global Numerical Optimization,” Journal of Applied Mathematics, vol. 2013, pp. 1–21, 2013.
  • A. R. Yıldız, «Hybrid Taguchi‐Harmony Search Algorithm for Solving,» International Journal of Industrial Engineering Theory, Applications and Practice, cilt 15, no. 3, pp. 286-93, 2008.
  • B. Wu, C. Qian, W. Ni, and S. Fan, “Hybrid harmony search and artificial bee colony algorithm for global optimization problems,” Computers & Mathematics with Applications, vol. 64, no. 8, pp. 2621–2634, 2012.
  • S. Mirjalili, “SCA: A Sine Cosine Algorithm for solving optimization problems,” Knowledge-Based Systems, vol. 96, pp. 120–133, 2016.
  • S. H. R. Pasandideh və S. Khalilpourazari, “SINE COSINE CROW SEARCH ALGORITHM: A POWERFUL HYBRID META HEURISTIC FOR GLOBAL OPTIMIZATION,” arXiv preprint arXiv:1801.08485, 2018.
  • S. Khalilpourazari and S. Khalilpourazary, “SCWOA: an efficient hybrid algorithm for parameter optimization of multi-pass milling process,” Journal of Industrial and Production Engineering, vol. 35, no. 3, pp. 135–147, 2018.
  • N. Singh and S. Singh, “A novel hybrid GWO-SCA approach for optimization problems,” Engineering Science and Technology, an International Journal, vol. 20, no. 6, pp. 1586–1601, 2017.
  • S. Mirjalili, S. M. Mirjalili, and A. Hatamlou, “Multi-Verse Optimizer: a nature-inspired algorithm for global optimization,” Neural Computing and Applications, vol. 27, no. 2, pp. 495–513, 2015.
  • P. Jangir, S. A. Parmar, I. N. Trivedi, and R. Bhesdadiya, “A novel hybrid Particle Swarm Optimizer with multi verse optimizer for global numerical optimization and Optimal Reactive Power Dispatch problem,” Engineering Science and Technology, an International Journal, vol. 20, no. 2, pp. 570–586, 2017.
  • M. Sulaiman, S. Ahmad, J. Iqbal, A. Khan, and R. Khan, “Optimal Operation of the Hybrid Electricity Generation System Using Multiverse Optimization Algorithm,” Computational Intelligence and Neuroscience, vol. 2019, pp. 1–12, 2019.
  • X. Wang, D. Luo, X. Zhao, and Z. Sun, “Estimates of energy consumption in China using a self-adaptive multi-verse optimizer-based support vector machine with rolling cross-validation,” Energy, vol. 152, pp. 539–548, 2018.
  • M. Jamil and X. S. Yang, “A literature survey of benchmark functions for global optimisation problems,” International Journal of Mathematical Modelling and Numerical Optimisation, vol. 4, no. 2, p. 150, 2013.
  • B. Alizada, «Improved Whale Optimization Algorithm Based On π Number,» INTERNATIONAL SCIENTIFIC AND VOCATIONAL JOURNAL (ISVOS JOURNAL), cilt 4, no. 1, pp. 21-30, 2020.
  • S. Mirjalili ve A. Lewis, «The Whale Optimization Algorithm,» Advances in Engineering Software, cilt 95, pp. 51-67, 2016.
  • P. Chakraborty, G. G. Roy, S. Das, D. Jain, and A. Abraham, “An Improved Harmony Search Algorithm with Differential Mutation Operator,” Fundamenta Informaticae, vol. 95, no. 4, pp. 401–426, 2009.
  • Q.-K. Pan, P. Suganthan, M. F. Tasgetiren, and J. Liang, “A self-adaptive global best harmony search algorithm for continuous optimization problems,” Applied Mathematics and Computation, vol. 216, no. 3, pp. 830–848, 2010.
  • X. Gan, E. Jiang, Y. Peng, S. Geng, and M. Kustudic, “Research Optimization on Logistic Distribution Center Location Based on Improved Harmony Search Algorithm,” Lecture Notes in Computer Science Advances in Swarm Intelligence, pp. 410–420, 2018.
  • S. Mirjalili, “The Ant Lion Optimizer,” Advances in Engineering Software, vol. 83, pp. 80–98, 2015.
Toplam 28 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Zeka, Yazılım Testi, Doğrulama ve Validasyon
Bölüm Araştırma Makalesi
Yazarlar

Bahadur Alızada 0000-0001-6587-4057

Yayımlanma Tarihi 15 Nisan 2021
Gönderilme Tarihi 16 Temmuz 2020
Kabul Tarihi 17 Mart 2021
Yayımlandığı Sayı Yıl 2021

Kaynak Göster

APA Alızada, B. (2021). A Novel Hybrid Algorithm: Sine Cosine Harmony Search Algorithm for Global Optimization. Sakarya University Journal of Science, 25(2), 513-529. https://doi.org/10.16984/saufenbilder.770367
AMA Alızada B. A Novel Hybrid Algorithm: Sine Cosine Harmony Search Algorithm for Global Optimization. SAUJS. Nisan 2021;25(2):513-529. doi:10.16984/saufenbilder.770367
Chicago Alızada, Bahadur. “A Novel Hybrid Algorithm: Sine Cosine Harmony Search Algorithm for Global Optimization”. Sakarya University Journal of Science 25, sy. 2 (Nisan 2021): 513-29. https://doi.org/10.16984/saufenbilder.770367.
EndNote Alızada B (01 Nisan 2021) A Novel Hybrid Algorithm: Sine Cosine Harmony Search Algorithm for Global Optimization. Sakarya University Journal of Science 25 2 513–529.
IEEE B. Alızada, “A Novel Hybrid Algorithm: Sine Cosine Harmony Search Algorithm for Global Optimization”, SAUJS, c. 25, sy. 2, ss. 513–529, 2021, doi: 10.16984/saufenbilder.770367.
ISNAD Alızada, Bahadur. “A Novel Hybrid Algorithm: Sine Cosine Harmony Search Algorithm for Global Optimization”. Sakarya University Journal of Science 25/2 (Nisan 2021), 513-529. https://doi.org/10.16984/saufenbilder.770367.
JAMA Alızada B. A Novel Hybrid Algorithm: Sine Cosine Harmony Search Algorithm for Global Optimization. SAUJS. 2021;25:513–529.
MLA Alızada, Bahadur. “A Novel Hybrid Algorithm: Sine Cosine Harmony Search Algorithm for Global Optimization”. Sakarya University Journal of Science, c. 25, sy. 2, 2021, ss. 513-29, doi:10.16984/saufenbilder.770367.
Vancouver Alızada B. A Novel Hybrid Algorithm: Sine Cosine Harmony Search Algorithm for Global Optimization. SAUJS. 2021;25(2):513-29.

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