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

Abstract

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.

Keywords

• Sine Cosine Algorithm.
• Harmony Search.
• Benchmark Functions
• Meta-heuristic algorithms

Thanks

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.

References

1. Wolpert, D. and Macready, W., 1997. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), pp.67-82.
2. J. H. Holland, “Genetic Algorithms,” Scientific American, vol. 267, no. 1, pp. 66–72, 1992.
3. 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.
4. 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.
5. E. Falkenauer, “A hybrid grouping genetic algorithm for bin packing,” Journal of Heuristics, vol. 2, no. 1, pp. 5–30, 1996.
6. 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.
7. C. Ozturk and D. Karaboga, “Hybrid Artificial Bee Colony algorithm for neural network training,” 2011 IEEE Congress of Evolutionary Computation (CEC), 2011.
8. 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.

9. 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.
10. 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.
11. 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.
12. 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.
13. 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.
14. S. Mirjalili, “SCA: A Sine Cosine Algorithm for solving optimization problems,” Knowledge-Based Systems, vol. 96, pp. 120–133, 2016.
15. 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.
16. 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.
17. 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.
18. 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.
19. 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.
20. 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.
21. 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.
22. 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.
23. 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.
24. S. Mirjalili ve A. Lewis, «The Whale Optimization Algorithm,» Advances in Engineering Software, cilt 95, pp. 51-67, 2016.
25. 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.
26. 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.
27. 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.
28. S. Mirjalili, “The Ant Lion Optimizer,” Advances in Engineering Software, vol. 83, pp. 80–98, 2015.