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Kaotik Denizatı Optimizasyon Algoritması

Year 2022, Issue: 44, 51 - 58, 31.12.2022
https://doi.org/10.31590/ejosat.1216396

Abstract

Global optimizasyon teknikleri olarak bilinen metasezgisel algoritmalar, çeşitli karmaşık ve gerçek optimizasyon problemlerini çözmek için başarıyla kullanılmaktadır. Metasezgisel yöntemler, fizik, sürü zekâsı ve biyolojinin farklı ilkelerinden ilham almaktadır. Denizatı Optimizasyon Algoritması (DOA), denizatlarının doğadaki hareket, avlanma ve üreme davranışlarından esinlenerek önerilmiş sürü zekasına tabanlı metasezgisel bir optimizasyon algoritmasıdır. Sürü zekasına dayalı metasezgisel optimizasyon algoritmalardan daha hızlı ve yüksek doğrulukta yakınsama elde etmek için farklı yöntemler önerilmiştir. Bu çalışmada, DOA’nın yakınsama hızını artırmak ve yerel çözümlerde takılıp kalmasını engellemek için rastgele değerler yerine Chebyshev, Circle, Gauss, Iterative, Logistic, Piecewise ve Sine olmak üzere yedi farklı kaotik harita uygulanmıştır. İlk kez bu çalışmada önerilen Kaotik Denizatı Optimizasyon Algoritması (KDOA), tek modlu, çok modlu ve sabit boyutlu çok modlu olmak üzere yedi farklı kıyaslama fonksiyonuna uygulanmıştır. Önerilen KDOA’nın performansını değerlendirmek için klasik DOA karşılaştırılmıştır. Deneysel sonuçlara göre, KDOA’nın yedi farklı kıyaslama fonksiyonunda klasik DOA’ya göre daha iyi sonuçlar elde ettiği gözlemlenmiştir.

References

  • Arora, S., & Anand, P. (2019). Chaotic grasshopper optimization algorithm for global optimization. Neural Computing and Applications, 31(8), 4385-4405.
  • Eberhart, R., & Kennedy, J. (1995, October). A new optimizer using particle swarm theory. In MHS'95. Proceedings of the sixth international symposium on micro machine and human science (pp. 39-43). Ieee.
  • Einstein, A. (1956). Investigations on the Theory of the Brownian Movement. Courier Corporation.
  • Hassan, B. A. (2021). CSCF: a chaotic sine cosine firefly algorithm for practical application problems. Neural Computing and Applications, 33(12), 7011-7030.
  • Holland, J. H. (1992). Genetic algorithms. Scientific american, 267(1), 66-73.
  • Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of global optimization, 39(3), 459-471.
  • Kaveh, A., & Mahdavi, V. R. (2014). Colliding bodies optimization: a novel meta-heuristic method. Computers & Structures, 139, 18-27.
  • Mantegna, R. N. (1994). Fast, accurate algorithm for numerical simulation of Levy stable stochastic processes. Physical Review E, 49(5), 4677.
  • Mirjalili, S. (2016). SCA: a sine cosine algorithm for solving optimization problems. Knowledge-based systems, 96, 120-133.
  • Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61.
  • Onay, F. K., & Aydemı̇r, S. B. (2022). Chaotic hunger games search optimization algorithm for global optimization and engineering problems. Mathematics and Computers in Simulation, 192, 514-536.
  • Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). GSA: a gravitational search algorithm. Information sciences, 179(13), 2232-2248.
  • Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4), 341-359.
  • Yang, X. S. (2010). A new metaheuristic bat-inspired algorithm. In Nature inspired cooperative strategies for optimization (NICSO 2010) (pp. 65-74). Springer, Berlin, Heidelberg.
  • Zhao, S., Zhang, T., Ma, S., & Wang, M. (2022). Sea-horse optimizer: a novel nature-inspired meta-heuristic for global optimization problems. Applied Intelligence, 1-28.
  • Zhao, W., Wang, L., & Zhang, Z. (2019). A novel atom search optimization for dispersion coefficient estimation in groundwater. Future Generation Computer Systems, 91, 601-610.

Chaotic Seahorse Optimization Algorithm

Year 2022, Issue: 44, 51 - 58, 31.12.2022
https://doi.org/10.31590/ejosat.1216396

Abstract

Metaheuristic algorithms, known as global optimization techniques, have been successfully used to solve a variety of complex and real optimization problems. Metaheuristic methods are inspired by different principles of physics, swarm intelligence, and biology. The Seahorse Optimization Algorithm (SOA) is a suggested swarm intelligence-based metaheuristic optimization algorithm inspired by the movement, hunting, and breeding behavior of seahorses in nature. Different methods have been proposed to achieve faster and higher accuracy convergence than metaheuristic optimization algorithms based on swarm intelligence. In this study, seven different chaotic maps, namely Chebyshev, Circle, Gauss, Iterative, Logistic, Piecewise, and Sine, were applied instead of random values in order to increase the convergence speed of SOA and to prevent it from getting stuck in local solutions. The Chaotic Seahorse Optimization Algorithm (CSOA), proposed for the first time in this study, has been applied to seven different benchmarking functions. Classic SOA was compared to evaluate the performance of the proposed CSOA. According to the experimental results, it was observed that CSOA achieved better results than classical SOA in seven different comparison functions.

References

  • Arora, S., & Anand, P. (2019). Chaotic grasshopper optimization algorithm for global optimization. Neural Computing and Applications, 31(8), 4385-4405.
  • Eberhart, R., & Kennedy, J. (1995, October). A new optimizer using particle swarm theory. In MHS'95. Proceedings of the sixth international symposium on micro machine and human science (pp. 39-43). Ieee.
  • Einstein, A. (1956). Investigations on the Theory of the Brownian Movement. Courier Corporation.
  • Hassan, B. A. (2021). CSCF: a chaotic sine cosine firefly algorithm for practical application problems. Neural Computing and Applications, 33(12), 7011-7030.
  • Holland, J. H. (1992). Genetic algorithms. Scientific american, 267(1), 66-73.
  • Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of global optimization, 39(3), 459-471.
  • Kaveh, A., & Mahdavi, V. R. (2014). Colliding bodies optimization: a novel meta-heuristic method. Computers & Structures, 139, 18-27.
  • Mantegna, R. N. (1994). Fast, accurate algorithm for numerical simulation of Levy stable stochastic processes. Physical Review E, 49(5), 4677.
  • Mirjalili, S. (2016). SCA: a sine cosine algorithm for solving optimization problems. Knowledge-based systems, 96, 120-133.
  • Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61.
  • Onay, F. K., & Aydemı̇r, S. B. (2022). Chaotic hunger games search optimization algorithm for global optimization and engineering problems. Mathematics and Computers in Simulation, 192, 514-536.
  • Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). GSA: a gravitational search algorithm. Information sciences, 179(13), 2232-2248.
  • Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4), 341-359.
  • Yang, X. S. (2010). A new metaheuristic bat-inspired algorithm. In Nature inspired cooperative strategies for optimization (NICSO 2010) (pp. 65-74). Springer, Berlin, Heidelberg.
  • Zhao, S., Zhang, T., Ma, S., & Wang, M. (2022). Sea-horse optimizer: a novel nature-inspired meta-heuristic for global optimization problems. Applied Intelligence, 1-28.
  • Zhao, W., Wang, L., & Zhang, Z. (2019). A novel atom search optimization for dispersion coefficient estimation in groundwater. Future Generation Computer Systems, 91, 601-610.
There are 16 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Feyza Altunbey Özbay 0000-0003-0629-6888

Erdal Özbay 0000-0002-9004-4802

Early Pub Date December 31, 2022
Publication Date December 31, 2022
Published in Issue Year 2022 Issue: 44

Cite

APA Altunbey Özbay, F., & Özbay, E. (2022). Kaotik Denizatı Optimizasyon Algoritması. Avrupa Bilim Ve Teknoloji Dergisi(44), 51-58. https://doi.org/10.31590/ejosat.1216396