Araştırma Makalesi
Yıl 2021,
Cilt: 5 Sayı: 2, 164 - 170, 31.12.2021
Öz
This article proposes an opposite based learning (OBL) enhanced crow search algorithm (CSA) version for solving optimization problems. The proposed method, named the opposite based CSA (ObCSA), starts searching with individuals with higher fitness in the initial phase of the evolutionary process. In this way, it is aimed to improve the convergence performance of the basic CSA. To validate the proposed method, a set of benchmark test suit of different of features is chosen. Its convergence characteristic and statistical results are compared with the basic CSA. The results obtained show that the proposed method improves the convergence performance of the basic CSA. And the statistical results indicate that it manages to reach the near optimal solution and increases the quality of the solution.
Anahtar Kelimeler
Kaynakça
- M. Dorigo and G. Di Caro, The Ant Colony Optimization Metaheuristic, New Ideas in Optimization. New York: McGraw-Hill, 1999.
- J. Kennedy and R. Eberhart, “Particle swarm optimization,” In Proc. IEEE International Conference on Neural Networks, vol. IV, 1995, pp. 1942-1948.
- D. Karaboğa and B. Baştürk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm,” Journal Global Optimization, vol. 39, no. 3, pp. 459-471, 2007.
- M. Yazdani and F. Jolai, “Lion optimization algorithm (LOA): A nature-inspired metaheuristic algorithm,” Journal of Computational Design and Engineering, vol. 3, no. 1, pp. 24-36, 2016.
- R. Rajabioun, “Cuckoo optimization algorithm,” Applied Soft Computing, vol. 11, pp. 5508–5518, 2011.
- M. A. Montes de Oca, T. Stützle, K. Van den Enden, and M. Dorigo, “Incremental social learning in particle swarms. IEEE Transactions on Systems Man and Cybernetics Part B Cybernetic, vol. 41, no. 2, pp. 368-384, 2011.
- C. Banerjee and R. Sawal, “PSO with dynamic acceleration coefficient based on multiple constraint satisfaction,” In Proc. International Conference on Advances in Electronics Computers and Communications ‘14, 2014, pp. 1-5.
- B. Durmuş, “Chaotic map based tree seed algorithm,” Süleyman Demirel University Journal of Natural and Applied Sciences, vol. 23, no. 2, pp. 601-610, 2019.
- H. R. Tizhoosh, “Opposition-based learning: A new scheme for machine intelligence,” In Proc. International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06), 2005, pp. 695-701.
- M. G. H. Omran and S. Sharhan. “Using opposition-based learning to improve the performance of particle swarm optimization,” In Proc. IEEE Swarm Intelligence Symposium, 2008, pp. 1-6.
- H. Wang, H. Ouyang, L. Gao, and W. Qin, “Opposition-based learning harmony search algorithm with mutation for solving global optimization problems,” In Proc. 26th Chinese Control and Decision Conference, 2014, pp. 1090-1094.
- S. K. Dinkar and K. Deep, “Accelerated opposition-based antlion optimizer with application to order reduction of linear time-invariant systems,” Arabian Journal for Science and Engineering, vol. 44, pp. 2213-2241, 2019.
- J. Zhao, L. Lv, and H. Sun, “Artificial bee colony using opposition-based learning,” in Genetic and Evolutionary Computing, H. Sun, C. Y. Yang, C. W. Lin, J. S. Pan, V. Snasel, A. Abraham, Eds. Nanchang: Springer Cham, 2015, pp. 3-10.
- J. Li, T. Chen, T. Zhang, and Y. X. Li, “A cuckoo optimization algorithm using elite opposition-based learning and chaotic disturbance,” Journal of Software Engineering, vol. 10, no. 1, pp. 16-28, 2016.
- H. Ming, W. Mingxu, and L. Xu, “An improved genetic algorithm using opposition-based learning for flexible job-shop scheduling problem,” In Proc. 2nd International Conference on Cloud Computing and Internet of Things, 2016, pp. 8-15.
- S. Rahnamayan, H. R. Tizhoosh, and M. M. A. Salama, “Opposition-based differential evolution algorithms,” In Proc. IEEE Congress on Evolutionary Computation, 2006, pp. 2010-2017.
- A. Askarzadeh, “A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm,” Computer & Structures, vol. 169, June, pp. 1-12, 2016.
Yıl 2021,
Cilt: 5 Sayı: 2, 164 - 170, 31.12.2021
Öz
This article proposes an opposite based learning (OBL) enhanced crow search algorithm (CSA) version for solving optimization problems. The proposed method, named the opposite based CSA (ObCSA), starts searching with individuals with higher fitness in the initial phase of the evolutionary process. In this way, it is aimed to improve the convergence performance of the basic CSA. To validate the proposed method, a set of benchmark test suit of different of features is chosen. Its convergence characteristic and statistical results are compared with the basic CSA. The results obtained show that the proposed method improves the convergence performance of the basic CSA. And the statistical results indicate
that it manages to reach the near optimal solution and increases the quality of the solution.
Anahtar Kelimeler
Kaynakça
- M. Dorigo and G. Di Caro, The Ant Colony Optimization Metaheuristic, New Ideas in Optimization. New York: McGraw-Hill, 1999.
- J. Kennedy and R. Eberhart, “Particle swarm optimization,” In Proc. IEEE International Conference on Neural Networks, vol. IV, 1995, pp. 1942-1948.
- D. Karaboğa and B. Baştürk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm,” Journal Global Optimization, vol. 39, no. 3, pp. 459-471, 2007.
- M. Yazdani and F. Jolai, “Lion optimization algorithm (LOA): A nature-inspired metaheuristic algorithm,” Journal of Computational Design and Engineering, vol. 3, no. 1, pp. 24-36, 2016.
- R. Rajabioun, “Cuckoo optimization algorithm,” Applied Soft Computing, vol. 11, pp. 5508–5518, 2011.
- M. A. Montes de Oca, T. Stützle, K. Van den Enden, and M. Dorigo, “Incremental social learning in particle swarms. IEEE Transactions on Systems Man and Cybernetics Part B Cybernetic, vol. 41, no. 2, pp. 368-384, 2011.
- C. Banerjee and R. Sawal, “PSO with dynamic acceleration coefficient based on multiple constraint satisfaction,” In Proc. International Conference on Advances in Electronics Computers and Communications ‘14, 2014, pp. 1-5.
- B. Durmuş, “Chaotic map based tree seed algorithm,” Süleyman Demirel University Journal of Natural and Applied Sciences, vol. 23, no. 2, pp. 601-610, 2019.
- H. R. Tizhoosh, “Opposition-based learning: A new scheme for machine intelligence,” In Proc. International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06), 2005, pp. 695-701.
- M. G. H. Omran and S. Sharhan. “Using opposition-based learning to improve the performance of particle swarm optimization,” In Proc. IEEE Swarm Intelligence Symposium, 2008, pp. 1-6.
- H. Wang, H. Ouyang, L. Gao, and W. Qin, “Opposition-based learning harmony search algorithm with mutation for solving global optimization problems,” In Proc. 26th Chinese Control and Decision Conference, 2014, pp. 1090-1094.
- S. K. Dinkar and K. Deep, “Accelerated opposition-based antlion optimizer with application to order reduction of linear time-invariant systems,” Arabian Journal for Science and Engineering, vol. 44, pp. 2213-2241, 2019.
- J. Zhao, L. Lv, and H. Sun, “Artificial bee colony using opposition-based learning,” in Genetic and Evolutionary Computing, H. Sun, C. Y. Yang, C. W. Lin, J. S. Pan, V. Snasel, A. Abraham, Eds. Nanchang: Springer Cham, 2015, pp. 3-10.
- J. Li, T. Chen, T. Zhang, and Y. X. Li, “A cuckoo optimization algorithm using elite opposition-based learning and chaotic disturbance,” Journal of Software Engineering, vol. 10, no. 1, pp. 16-28, 2016.
- H. Ming, W. Mingxu, and L. Xu, “An improved genetic algorithm using opposition-based learning for flexible job-shop scheduling problem,” In Proc. 2nd International Conference on Cloud Computing and Internet of Things, 2016, pp. 8-15.
- S. Rahnamayan, H. R. Tizhoosh, and M. M. A. Salama, “Opposition-based differential evolution algorithms,” In Proc. IEEE Congress on Evolutionary Computation, 2006, pp. 2010-2017.
- A. Askarzadeh, “A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm,” Computer & Structures, vol. 169, June, pp. 1-12, 2016.
Toplam 17 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Yapay Zeka, Elektrik Mühendisliği |
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 31 Aralık 2021 |
| Kabul Tarihi | 13 Aralık 2021 |
| Yayımlandığı Sayı | Yıl 2021 Cilt: 5 Sayı: 2 |
Kaynak Göster
Creative Commons License
Creative Commons Atıf 4.0 It is licensed under an International License