Research Article
Year 2023,
Volume: 23 Issue: 5, 1122 - 1141, 30.10.2023
Abstract
Metasezgisel algoritmalar, optimizasyon problemlerine makul bir sürede yaklaşık veya optimal
çözümler sunar. Bu özelliği ile metasezgisel algoritmalar zor optimizasyon problemlerini çözmek için
etkileyici bir araştırma alanı haline gelmiştir. Yılan Optimize Edici, yılanların çiftleşme davranışlarından
esinlenen popülasyon tabanlı bir metasezgisel algoritmadır. Bu çalışmada, Yılan Optimize Edicinin
performansını iyileştirmek için rastgele sayı dizileri yerine algoritmanın parametrelerine farklı kaotik
haritalar entegre edilmiş ve dört farklı kaotik haritalama kullanılarak Yılan Optimize Edici varyantları
önerilmiştir. Önerilen bu varyantların sekiz farklı kaotik harita için performansları klasik ve CEC2019 test
fonksiyonları üzerinde incelenmiştir. Sonuçlar, önerilen algoritmaların Yılan Optimize Edici
performansının iyileştirilmesine katkıda bulunduğunu ortaya koydu. Literatür ile karşılaştırıldığında
önerilen Kaotik Optimize Edici algoritması birçok fonksiyonda en iyi ortalama değerleri bulmuş ve
algoritmalar arasında ikinci sırada yer almıştır. Yapılan testler sonucunda, Kaotik Yılan Optimize Edicinin
gelecek vadeden, başarılı ve tercih edilebilir bir algoritma olduğu görülmüştür.
References
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- Arora, S., and Singh, S., 2019. Butterfly optimization algorithm: a novel approach for global optimization. Soft Computing, 23, 715-734.
- Bingol, H., and Alatas, B., 2020. Chaos based optics inspired optimization algorithms as global solution search approach. Chaos, Solitons & Fractals, 141, 110434.
- Cheng, K., Zhang, J., Tian, S., Liu, H., Gong, J., and Xie, Y., 2022. WiFi Localization Algorithm Based on Snake Optimization Algorithm to Optimize BP Neural Network. In 2022 International Conference on Image Processing, Computer Vision and Machine Learning , IEEE, 615-618.
- Dai, Y., Pang, J., Li, Z., Li, W., Wang, Q., and Li, S., 2022. Modeling of thermal error electric spindle based on KELM ameliorated by snake optimization. Case Studies in Thermal Engineering, 40, 102504.
- Dokeroglu, T., Sevinc, E., Kucukyilmaz, T., and Cosar, A., 2019. A survey on new generation metaheuristic algorithms. Computers & Industrial Engineering, 137, 106040.
- Daliri, A., Asghari, A., Azgomi, H., and Alimoradi, M., 2022. The water optimization algorithm: a novel metaheuristic for solving optimization problems. Applied Intelligence, 52, 17990-18029.
- El-Saleh, A. A., Thaher, T., Chantar, H., and Mafarja, M., 2023. Enhanced IoT Based IDS Driven by Binary Snake Optimizer for Feature Selection. In Advances in Model and Data Engineering in the Digitalization Era: MEDI 2022 Short Papers and DETECT 2022 Workshop Papers, Cairo, Egypt, November 21–24, 2022, Proceedings,Springer,29-43.
- Fu, H., Shi, H., Xu, Y., and Shao, J., 2022. Research on Gas Outburst Prediction Model Based on Multiple Strategy Fusion Improved Snake Optimization Algorithm With Temporal Convolutional Network. IEEE Access, 10, 117973-117984.
- García, S., Molina, D., Lozano, M., and Herrera, F. , 2009. A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 special session on real parameter optimization. Journal of Heuristics, 15, 617-644.
- Gong, Y., Li, C., Wang, F., and Fang, X., 2023. MHCF-CECSO: A Novel High-Performance Clustering Framework for Industrial IoT. IEEE Internet of Things Journal, 1-1.
- Hashim, F.A., and Hussien, A.G., 2022. Snake Optimizer: A novel meta-heuristic optimization algorithm. Knowledge-Based Systems, 242, 108320.
- Hussien, A. G., Amin, M., Wang, M., Liang, G., Alsanad, A., Gumaei, A., and Chen, H., 2020. Crow search algorithm: theory, recent advances, and applications. IEEE Access, 8, 173548-173565.
- Joshi, H., and Arora, S., 2017. Enhanced grey wolf optimization algorithm for global optimization. Fundamenta Informaticae, 153, 235-264.
- Klimov, P.V., Kelly, J., Martinis, J.M., and Neven, H. , 2020. The snake optimizer for learning quantum processor control parameters. arXiv preprint arXiv, 2006.04594.
- Li, Y., Xiao, L., Tang, B., Liang, L., Lou, Y., Guo, X., and Xue, X., 2022. A denoising method for ship-radiated noise based on optimized variational mode decomposition with snake optimization and dual-threshold criteria of correlation coefficient. Mathematical Problems in Engineering, 2022, 8024753.
- Liu, X., Tian, M., Zhou, J., and Liang, J., 2023. An efficient coverage method for SEMWSNs based on adaptive chaotic Gaussian variant snake optimization algorithm. Mathematical Biosciences and Engineering, 20, 3191-3215.
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- Mirjalili, S., 2016. SCA: a sine cosine algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120-133.
- Mirjalili, S., and Hashim, S.Z.M., 2010. A new hybrid PSOGSA algorithm for function optimization. In 2010 international conference on computer and information application,Tianjin, China, 374-377.
- Mirjalili, S., and Lewis, A., 2016. The whale optimization algorithm. Advances in engineering software, 95, 51-67.
- Omran, A. E.-F. A., Nafeh, A. E.-S. A., and Yousef, H. K., 2022. Optimal Sizing of a PV-Battery Stand-Alone Fast Charging Station for Electric Vehicles Using SO. International Journal of Renewable Energy Research, 12, 1769-1778.
- Price, K., Awad, N., Ali, M., and Suganthan, P., 2018. Problem definitions and evaluation criteria for the 100-digit challenge special session and competition on single objective numerical optimization. In Technical Report: Nanyang Technological University Singapore.
- Qais, M.H., Hasanien, H.M., and Alghuwainem, S., 2018. Augmented grey wolf optimizer for grid-connected PMSG-based wind energy conversion systems. Applied Soft Computing, 69, 504-515.
- Rawa, M., 2022. Towards Avoiding cascading failures in transmission expansion planning of modern active power systems using hybrid Snake-Sine Cosine optimization algorithm. Mathematics, 10, 1323.
- Varol Altay, E., and Alatas, B., 2020. Bird swarm algorithms with chaotic mapping. Artificial Intelligence Review, 53, 1373-1414.
- Wang, L., Cao, Q., Zhang, Z., Mirjalili, S., and Zhao, W., 2022. Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 114, 105082.
- Wei, X., Yuan, S., and Ye, Y., 2019. Optimizing facility layout planning for reconfigurable manufacturing system based on chaos genetic algorithm. Production & Manufacturing Research, 7, 109-124.
- Xu, W., Zhang, R., and Chen, L., 2022. An improved crow search algorithm based on oppositional forgetting learning. Applied Intelligence, 1-17.
- Vellingiri, M., Rawa, M., Alghamdi, S., Alhussainy, A.A., Althobiti, A. S., Calasan, M., Micev, M., Ali, Z.M., and Abdel Aleem, S.H.E., 2023. Non-Linear Analysis of Novel Equivalent Circuits of Single-Diode Solar Cell Models with Voltage-Dependent Resistance. Fractal and Fractional, 7, 95.
- Yang, X.-S., and He, X., 2013. Bat algorithm: literature review and applications. International Journal of Bio-Inspired Computation, 5, 141-149.
- Yao, L., Yuan, P., Tsai, C.-Y., Zhang, T., Lu, Y., and Ding, S., 2023. ESO: An enhanced snake optimizer for real-world engineering problems. Expert Systems with Applications, 230, 120594.
Year 2023,
Volume: 23 Issue: 5, 1122 - 1141, 30.10.2023
Abstract
Metaheuristic algorithms provide approximate or optimal solutions for optimization problems in a
reasonable time. With this feature, metaheuristic algorithms have become an impressive research area
for solving difficult optimization problems. Snake Optimizer is a population-based metaheuristic
algorithm inspired by the mating behavior of snakes. In this study, different chaotic maps were
integrated into the parameters of the algorithm instead of random number sequences to improve the
performance of Snake Optimizer, and Snake Optimizer variants using four different chaotic mappings
were proposed. The performances of these proposed variants for eight different chaotic maps were
examined on classical and CEC2019 test functions. The results revealed that the proposed algorithms
contribute to the improvement of Snake Optimizer performance. In the comparison with the literature,
the proposed Chaotic Snake Optimizer algorithm found the best mean values in many functions and
took second place among the algorithms. As a result of the tests, Chaotic Snake Optimizer has been
shown to be a promising, successful, and preferable algorithm.
References
- Abd Elaziz, M., Oliva, D., and Xiong, S., 2017. An improved opposition-based sine cosine algorithm for global optimization. Expert Systems with Applications, 90, 484-500.
- Alatas, B., Akin, E., and Ozer, A.B., 2009. Chaos embedded particle swarm optimization algorithms. Chaos, Solitons & Fractals, 40, 1715-1734.
- Alataş, B., Akın, E., and Özer, A.B., 2007. Kaotik Haritalı Parçacık Sürü Optimizasyon Algoritmaları. In ELECO 2007 5th International Conference on Electrical and Electronics Engineering, Bursa.
- Arora, S., and Singh, S., 2019. Butterfly optimization algorithm: a novel approach for global optimization. Soft Computing, 23, 715-734.
- Bingol, H., and Alatas, B., 2020. Chaos based optics inspired optimization algorithms as global solution search approach. Chaos, Solitons & Fractals, 141, 110434.
- Cheng, K., Zhang, J., Tian, S., Liu, H., Gong, J., and Xie, Y., 2022. WiFi Localization Algorithm Based on Snake Optimization Algorithm to Optimize BP Neural Network. In 2022 International Conference on Image Processing, Computer Vision and Machine Learning , IEEE, 615-618.
- Dai, Y., Pang, J., Li, Z., Li, W., Wang, Q., and Li, S., 2022. Modeling of thermal error electric spindle based on KELM ameliorated by snake optimization. Case Studies in Thermal Engineering, 40, 102504.
- Dokeroglu, T., Sevinc, E., Kucukyilmaz, T., and Cosar, A., 2019. A survey on new generation metaheuristic algorithms. Computers & Industrial Engineering, 137, 106040.
- Daliri, A., Asghari, A., Azgomi, H., and Alimoradi, M., 2022. The water optimization algorithm: a novel metaheuristic for solving optimization problems. Applied Intelligence, 52, 17990-18029.
- El-Saleh, A. A., Thaher, T., Chantar, H., and Mafarja, M., 2023. Enhanced IoT Based IDS Driven by Binary Snake Optimizer for Feature Selection. In Advances in Model and Data Engineering in the Digitalization Era: MEDI 2022 Short Papers and DETECT 2022 Workshop Papers, Cairo, Egypt, November 21–24, 2022, Proceedings,Springer,29-43.
- Fu, H., Shi, H., Xu, Y., and Shao, J., 2022. Research on Gas Outburst Prediction Model Based on Multiple Strategy Fusion Improved Snake Optimization Algorithm With Temporal Convolutional Network. IEEE Access, 10, 117973-117984.
- García, S., Molina, D., Lozano, M., and Herrera, F. , 2009. A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 special session on real parameter optimization. Journal of Heuristics, 15, 617-644.
- Gong, Y., Li, C., Wang, F., and Fang, X., 2023. MHCF-CECSO: A Novel High-Performance Clustering Framework for Industrial IoT. IEEE Internet of Things Journal, 1-1.
- Hashim, F.A., and Hussien, A.G., 2022. Snake Optimizer: A novel meta-heuristic optimization algorithm. Knowledge-Based Systems, 242, 108320.
- Hussien, A. G., Amin, M., Wang, M., Liang, G., Alsanad, A., Gumaei, A., and Chen, H., 2020. Crow search algorithm: theory, recent advances, and applications. IEEE Access, 8, 173548-173565.
- Joshi, H., and Arora, S., 2017. Enhanced grey wolf optimization algorithm for global optimization. Fundamenta Informaticae, 153, 235-264.
- Klimov, P.V., Kelly, J., Martinis, J.M., and Neven, H. , 2020. The snake optimizer for learning quantum processor control parameters. arXiv preprint arXiv, 2006.04594.
- Li, Y., Xiao, L., Tang, B., Liang, L., Lou, Y., Guo, X., and Xue, X., 2022. A denoising method for ship-radiated noise based on optimized variational mode decomposition with snake optimization and dual-threshold criteria of correlation coefficient. Mathematical Problems in Engineering, 2022, 8024753.
- Liu, X., Tian, M., Zhou, J., and Liang, J., 2023. An efficient coverage method for SEMWSNs based on adaptive chaotic Gaussian variant snake optimization algorithm. Mathematical Biosciences and Engineering, 20, 3191-3215.
- Mirjalili, S., 2015. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89, 228-249.
- Mirjalili, S., 2016. SCA: a sine cosine algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120-133.
- Mirjalili, S., and Hashim, S.Z.M., 2010. A new hybrid PSOGSA algorithm for function optimization. In 2010 international conference on computer and information application,Tianjin, China, 374-377.
- Mirjalili, S., and Lewis, A., 2016. The whale optimization algorithm. Advances in engineering software, 95, 51-67.
- Omran, A. E.-F. A., Nafeh, A. E.-S. A., and Yousef, H. K., 2022. Optimal Sizing of a PV-Battery Stand-Alone Fast Charging Station for Electric Vehicles Using SO. International Journal of Renewable Energy Research, 12, 1769-1778.
- Price, K., Awad, N., Ali, M., and Suganthan, P., 2018. Problem definitions and evaluation criteria for the 100-digit challenge special session and competition on single objective numerical optimization. In Technical Report: Nanyang Technological University Singapore.
- Qais, M.H., Hasanien, H.M., and Alghuwainem, S., 2018. Augmented grey wolf optimizer for grid-connected PMSG-based wind energy conversion systems. Applied Soft Computing, 69, 504-515.
- Rawa, M., 2022. Towards Avoiding cascading failures in transmission expansion planning of modern active power systems using hybrid Snake-Sine Cosine optimization algorithm. Mathematics, 10, 1323.
- Varol Altay, E., and Alatas, B., 2020. Bird swarm algorithms with chaotic mapping. Artificial Intelligence Review, 53, 1373-1414.
- Wang, L., Cao, Q., Zhang, Z., Mirjalili, S., and Zhao, W., 2022. Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 114, 105082.
- Wei, X., Yuan, S., and Ye, Y., 2019. Optimizing facility layout planning for reconfigurable manufacturing system based on chaos genetic algorithm. Production & Manufacturing Research, 7, 109-124.
- Xu, W., Zhang, R., and Chen, L., 2022. An improved crow search algorithm based on oppositional forgetting learning. Applied Intelligence, 1-17.
- Vellingiri, M., Rawa, M., Alghamdi, S., Alhussainy, A.A., Althobiti, A. S., Calasan, M., Micev, M., Ali, Z.M., and Abdel Aleem, S.H.E., 2023. Non-Linear Analysis of Novel Equivalent Circuits of Single-Diode Solar Cell Models with Voltage-Dependent Resistance. Fractal and Fractional, 7, 95.
- Yang, X.-S., and He, X., 2013. Bat algorithm: literature review and applications. International Journal of Bio-Inspired Computation, 5, 141-149.
- Yao, L., Yuan, P., Tsai, C.-Y., Zhang, T., Lu, Y., and Ding, S., 2023. ESO: An enhanced snake optimizer for real-world engineering problems. Expert Systems with Applications, 230, 120594.
There are 34 citations in total.
Details
| Primary Language | English |
|---|---|
| Subjects | Artificial Intelligence |
| Journal Section | Articles |
| Authors | |
| Early Pub Date | October 27, 2023 |
| Publication Date | October 30, 2023 |
| Submission Date | March 11, 2023 |
| Published in Issue | Year 2023 Volume: 23 Issue: 5 |
