Crow Search Algorithm with Incremental Population

Yıl 2021, Sayı: 25, 615 - 622, 31.08.2021

Burhanettin Durmuş , Ayhan Gün , Hasan Temurtaş

https://doi.org/10.31590/ejosat.932389

Öz

The crow search algorithm (CSA) is one of the new metaheuristics based on the intelligence behavior of crows. Although its simple structure and the need for few parametric adjustments give it an advantage, the problem of early convergence and easily falling to the local optimum decreases its performance in multimodal problems. In this paper, incremental population (IPOP) based CSA (IPOP-CSA) algorithm has been developed to strengthen this weakness of CSA and increase its efficiency. Using the IPOP strategy based on an expanding population, it is aimed to maintain the diversity of solutions throughout evolution. The four CSA versions developed are applied to 100-dimensional test functions to monitor their performance. The results obtained show that the proposed methods improve the performance of the basic CSA.

Anahtar Kelimeler

Metaheuristic, Optimization, Crow search algorithm

Artımsal Popülasyonlu Karga Arama Algoritması

Öz

Karga arama algoritması (CSA) kargaların zekâ davranışlarına temellendirilen yeni meta-sezgisellerden biridir. Basit yapısı ve az sayıda parametreye ihtiyaç duyuşu ona avantaj sağlamasına rağmen, erken yakınsama problemi ve yerel optimuma kolayca düşmesi özellikle çokmodlu (MM) problem çözümlerinde performansını düşürmektedir. Bu çalışmada, CSA ‘nın bu zayıflığını güçlendirmek ve etkinliğini arttırmak için artımsal popülasyon (IPOP) temelli CSA (ICSA) algoritmaları geliştirilmektedir. Genişleyen bir popülasyonu temel alan IPOP stratejisi ile hesaplama boyunca çözüm çeşitliliğin sağlanması hedeflenmektedir. Geliştirilen dört adet CSA versiyonu 100-boyutlu test fonksiyonlarına uygulanarak performansları gözlemlenmiştir. Elde edilen sonuçlar, önerilen metotların temel CSA ’nın performansını iyileştirdiğini göstermektedir.

Anahtar Kelimeler

Metazesgisel, Optimizasyon, Karga arama algoritması

Kaynakça

• Karaboğa, D. & Baştürk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal Global Optimization, 39(3), 459-471.
• Dorigo, M. & Di Caro, G. (1999). The ant colony optimization metaheuristic, new ideas in optimization. McGraw-Hill, New York, pp 11-32.
• Rajabioun, R. (2011). Cuckoo optimization algorithm. Applied Soft Computing, 11, 5508-5518.
• Kennedy, J. & Eberhart, R. (1995, November). Particle swarm optimization. In 1995 IEEE International Conference on Neural Networks, 4, 1942-1948.
• Mirjalili, S., Mirjalili, S. M. & Lewis, A. (2014). Grey wolf optimizer. Advance Engineering Software, 69, 46-61.
• Mirjalili, S. & Lewis, A. (2016). The whale optimization algorithm. Advance Engineering Software, 95, 51-67.
• Xu, X., Rong, H. & Trovati, M. (2018). CS-PSO: chaotic particle swarm optimization algorithm for solving combinatorial optimization problems. Soft Computing, 22(3), 783-795.
• Yu, W., Li, X., Cai, H., Zeng, Z. & Li, X. (2018). An improved artificial bee colony algorithm based on factor library and dynamic search balance. Mathematical Problems in Engineering, 3102628, 1-16.
• Gao, W. & Liu, S. (2011). Improved artificial bee colony algorithm for global optimization. Information Processing Letters, 111(17), 871-882.
• Aydın, D., Yavuz, G. & Stützle, T. (2017). ABC-X: a generalized, automatically configurable artificial bee colony framework. Swarm Intelligence, 11(1), 1-38.
• Yavuz, G. (2021). 100 Basamak probleminin JADE algoritması ile çözümü. Avrupa Bilim ve Teknoloji Dergisi, 21, 493-500.
• Özyön, S. (2020). Yenilenebilir enerji üretim birimleri içeren çevresel-ekonomik güç dağıtım probleminin yüklü sistem arama algoritması ile çözümü. Avrupa Bilim ve Teknoloji Dergisi, 18, 81-90.
• Yang, J. & Zhuang, Y. (2010). An improved ant colony optimization algorithm for solving a complex combinatorial optimization problem. Applied Soft Computing, 10(2), 653-660.
• Zhang, Q. & Zhang, C. (2018). An improved ant colony optimization algorithm with strengthened pheromone updating mechanizm for constraint satisfaction problem. Neural Computing and Applications, 30, 3209-3220.
• Abdollahi, M., Bouyer, A. & Abdollahi, D. (2016). Improved cuckoo optimization algorithm for solving systems of nonlinear equations. The Journal of Supercomputing, 72, 1246-1269.
• Li, S. Y., Wang, S. M., Wang, P. F., Su, X. L., Zhang, X. S. & Dong, Z. H. (2018). An improved grey wolf optimizer algorithm for the inversion of geolectrical data. Acta Geophysica, 66, 607-621.
• Sahoo, A. & Chandra, S. (2017). Multi-objective grey wolf optimizer for improved cervix lesion classification. Applied Soft Computing, 52, 64-80.
• Saidala, R. K. & Devarakonda, N. (2018). Improved whale optimization algorithm case study: clinical data of anaemic pregnant woman. Data Engineering and Intelligent Computing, 542, 271-281.
• Kaur, G. & Arora, S. (2018). Chaotic whale optimization algorithm. Journal of Computational Design and Engineering, 5(3), 275-284.
• Askarzadeh, A (2016). A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Computer & Structures, 169, 1-12.
• Aydın, D. & Özyön, S. (2013). Incremental artificial bee colony with local search to economic dispatch problem with ramp rate limits and prohibited operating zones. Energy Conversion and Management, 65, 397-407.
• Özyön, S., Yaşar, C. & Temurtaş, H. (2019). Incremental gravitational search algorithm for high-dimensional benchmark functions. Neural Computing and Applications, 31, 3779-3803.
• Montes de Oca, M. A. & Stützle, T. (2008, July). Towards incremental social learning in optimization and multiagent systems. In 10th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO’08), New York, 1939-1944.
• Yao, X., Liu, Y. & Lin, G. (1999). Evolutionary programming made faster. IEEE Transaction on Evolutionary Computation, 3(2), 82-102. Derrac, J., García, S., Molina, D., & Herrera, F. (2011). A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 1(1), 3-18.