Araştırma Makalesi
BibTex RIS Kaynak Göster

Performance Evaluation of Meta-Heuristic Optimization Algorithms with The TOPSIS Approach

Yıl 2024, Cilt: 24 Sayı: 3, 726 - 748, 27.06.2024

Şehmus Fidan , Metin Zaloğlu , Emre Erkan

https://doi.org/10.35414/akufemubid.1387447

Öz

Meta-heuristic algorithms inspired by nature can be utilized to derive a mathematical model of a system based on input/output data. To achieve this, various meta-heuristic optimization algorithms such as artificial ecosystem optimization (AEO), flower pollination algorithm (FPA), ant lion optimizer (ALO), moth-flame optimization (MFO), tug of war optimization (TWO), atomic search optimization (ASO), brain storm optimization (BSO), water cycle algorithm (WCA), coral reefs optimization (CRO), and life choice-based optimization (LCO) have been considered and employed to optimize the parameters of the proposed transfer function. Additionally, their performances have been compared under constraints such as time, maximum function evaluations, early stopping, and maximum generations. However, in this context, alongside performance metrics such as MAE, MAPE, and R2, metrics specific to transfer functions like rise time, settling time, and overshoot also emerge. The multitude of metrics makes it challenging to determine which algorithm performs best. To overcome this difficulty, the use of a multi-criteria decision-making approach known as Topsis (Technique for Order Preference by Similarity) is proposed in this study. The algorithm's solution time, performance (R2), and rise time have been considered for multiple criteria. As a result of the study, determining the best algorithm ranking has been accomplished in a straightforward and practical manner.

Anahtar Kelimeler

Meta-heuristic System Identification Transfer Function Topsis

Kaynakça

  • Canayaz, M. (2019). Training Anfis System with Moth-Flame Optimization Algorithm. International Journal of Intelligent Systems and Applications in Engineering, 7(3), Article 3. https://doi.org/10.18201/ijisae.2019355375
  • Çelikel, R., & Gundogdu, A. (2020). System identification-based MPPT algorithm for PV systems under variable atmosphere conditions using current sensorless approach. International Transactions on Electrical Energy Systems, 30(8), e12433.
  • Chen, Y., Pi, D., & Wang, B. (2019). Enhanced global flower pollination algorithm for parameter identification of chaotic and hyper-chaotic system. Nonlinear Dynamics, 97(2), 1343-1358. https://doi.org/10.1007/s11071-019-05052-z
  • Crispim, J. A., & Pinho de Sousa, J. (2009). Partner selection in virtual enterprises: A multi-criteria decision support approach. International Journal of Production Research, 47(17), 4791-4812. https://doi.org/10.1080/00207540902847348
  • Ding, S., Shi, Z., Chen, K., & Azar, A. T. (2015). Mathematical Modelling and Analysis of Soft Computing. Mathematical Problems in Engineering, 2015, e578321. https://doi.org/10.1155/2015/578321
  • Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. IEEE Computational Intelligence Magazine, 1(4), 28-39. https://doi.org/10.1109/MCI.2006.329691
  • El‐Dabah, M. A., & El‐Sehiemy…, R. A. (2021). Parameter estimation of triple diode photovoltaic model using an artificial ecosystem based optimizer. Int Trans Electr Energ Syst. 31(11):e13043. https://doi.org/10.1002/2050-7038.13043
  • Eskandar, H., Sadollah, A., Bahreininejad, A., & Hamdi, M. (2012). Water cycle algorithm – A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures, 110-111, 151-166. https://doi.org/10.1016/j.compstruc.2012.07.010
  • Fadzli, A. A. M., Hadi, M. S., Eek, R. T. P., Talib, M. H. Ab., Yatim, H. M., & Darus, I. Z. M. (2022). PID Controller Based on Flower Pollination Algorithm of Flexible Beam System. Recent Trends in Mechatronics Towards Industry 4.0. Springer, 173-183 https://doi.org/10.1007/978-981-33-4597-3_17
  • Fan, S., Zhang, J., Blanco-Davis, E., Yang, Z., & Yan, X. (2020). Maritime accident prevention strategy formulation from a human factor perspective using Bayesian NeHÇArks and TOPSIS. Ocean Engineering, 210, 107544. https://doi.org/10.1016/j.oceaneng.2020.107544
  • Farag, M. A., El-Shorbagy, M. A., Mousa, A. A., & El-Desoky, I. M. (2020). A New Hybrid Metaheuristic Algorithm for Multi objective Optimization Problems. International Journal of Computational Intelligence Systems, 13(1), 920-940. https://doi.org/10.2991/ijcis.d.200618.001
  • Fidan, Ş., Sevim, D., & Erkan, E. (2022). System Identification and Control of High Voltage Boost Converter. 2022 Global Energy Conference (GEC), 25-31. https://doi.org/10.1109/GEC55014.2022.9986621
  • Guo, Y., Shi, Q., & Guo, C. (2022). A Performance-Oriented Optimization Framework Combining Meta-Heuristics and Entropy-Weighted TOPSIS for Multi-Objective Sustainable Supply Chain NeHÇArk Design. Electronics, 11(19), Article 19. https://doi.org/10.3390/electronics11193134
  • Izci, D. (2022). A novel modified arithmetic optimization algorithm for power system stabilizer design. Sigma Journal of Engineering and Natural Sciences, 40(3), 3.
  • Izci, D., Hekimoğlu, B., & Ekinci, S. (2022). A new artificial ecosystem-based optimization integrated with Nelder-Mead method for PID controller design of buck converter. Alexandria Engineering Journal, 61(3), 2030-2044. https://doi.org/10.1016/j.aej.2021.07.037
  • Janjanam, L., Saha, S. K., Kar, R., & Mandal, D. (2022). Wiener model-based system identification using moth flame optimised Kalman filter algorithm. Signal, Image and Video Processing, 16(5), 1425-1433. https://doi.org/10.1007/s11760-021-02096-w
  • Ji, Y., Jiang, X., & Wan, L. (2020). Hierarchical least squares parameter estimation algorithm for HÇA-input Hammerstein finite impulse response systems. Journal of the Franklin Institute, 357(8), 5019-5032. https://doi.org/10.1016/j.jfranklin.2020.03.027
  • Kalita, K., Pal, S., Haldar, S., & Chakraborty, S. (2022). A Hybrid TOPSIS-PR-GWO Approach for Multi-objective Process Parameter Optimization. Process Integration and Optimization for Sustainability, 6(4), 1011-1026. https://doi.org/10.1007/s41660-022-00256-0
  • 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. https://doi.org/10.1007/s10898-007-9149-x
  • Kaveh, A. and Bakhshpoori T. (2021). Tug of War Optimization, Advances in Metaheuristic Algorithms for Optimal Design of Structures. Springer International Publishing, 467-503. https://doi.org/10.1007/978-3-030-59392-6_15
  • Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN’95 - International Conference on Neural NeHÇArks, 4, 1942-1948 c.4. https://doi.org/10.1109/ICNN.1995.488968
  • Khatri, A., Gaba, A., Rana, K. P. S., & Kumar, V. (2020). A novel life choice-based optimizer. Soft Computing, 24(12), 9121-9141. https://doi.org/10.1007/s00500-019-04443-z
  • Khluabwannarat, P., Nawikavatan, A., & Puangdownreong, D. (2018). Fractional-Order Model Parameter Identification of BLDC Motor by Flower Pollination Algorithm. 13.
  • Kler, D., Sharma, P., Banerjee, A., Rana, K. P. S., & Kumar, V. (2017). PV cell and module efficient parameters estimation using Evaporation Rate based Water Cycle Algorithm. Swarm and Evolutionary Computation, 35, 93-110. https://doi.org/10.1016/j.swevo.2017.02.005
  • Kumbasar, T., Eksin, I., Guzelkaya, M., & Yesil, E. (2011). Adaptive fuzzy model based inverse controller design using BB-BC optimization algorithm. Expert Systems with Applications, 38(10), 12356-12364. https://doi.org/10.1016/j.eswa.2011.04.015
  • Long, B., Yang, W., Hu, Q., Guerrero, J. M., Garcia, C., Rodriguez, J., & Chong, K. T. (2022). Moth–Flame-Optimization-Based Parameter Estimation for FCS-MPC-Controlled Grid-Connected Converter With LCL Filter. IEEE Journal of Emerging and Selected Topics in Power Electronics, 10(4), 4102-4114. https://doi.org/10.1109/JESTPE.2022.3140228
  • Mirjalili, S. (2015a). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89, 228-249. https://doi.org/10.1016/j.knosys.2015.07.006
  • Mirjalili, S. (2015b). The Ant Lion Optimizer. Advances in Engineering Software, 83, 80-98. https://doi.org/10.1016/j.advengsoft.2015.01.010
  • Mohammadi, A., Sheikholeslam, F., & Mirjalili, S. (2022). Inclined planes system optimization: Theory, literature review, and state-of-the-art versions for IIR system identification. Expert Systems with Applications, 200, 117127. https://doi.org/10.1016/j.eswa.2022.117127
  • Mossa, M. A., Kamel, O. M., Sultan, H. M., & Diab, A. A. Z. (2021). Parameter estimation of PEMFC model based on Harris Hawks’ optimization and atom search optimization algorithms. Neural Computing and Applications, 33(11), 5555-5570. https://doi.org/10.1007/s00521-020-05333-4
  • Nair, S. S., Rana, K. P. S., Kumar, V., & Chawla, A. (2017). Efficient Modelling of Linear Discrete Filters Using Ant Lion Optimizer. Circuits, Systems, and Signal Processing, 36(4), 1535-1568. https://doi.org/10.1007/s00034-016-0370-z
  • Nazir, M. I., Ahmad, A., & Hussain, I. (2022). Water Cycle Algorithm Based Parametric Tuning of Non-Negative LMMN Control of Grid Tied Renewable Energy Systems. IETE Journal of Research, 0(0), 1-17. https://doi.org/10.1080/03772063.2022.2089748
  • Nguyen, T. T. (2023) A novel metaheuristic method based on artificial ecosystem-based optimization for optimization of neHÇArk reconfiguration to reduce power loss. https://doi.org/10.1007/s00500-021-06346-4
  • Pal, P. S., Kar, R., Mandal, D., & Ghoshal, S. P. (2016). Identification of NARMAX Hammerstein models with performance assessment using brainstorm optimization algorithm. International Journal of Adaptive Control and Signal Processing, 30(7), 1043-1070. https://doi.org/10.1002/acs.2674
  • Prakash, V., Dwivedi, S., Gautam, K., Seth, M., & Anbumani, S. (2020). Occurrence and Ecotoxicological Effects of MiMRAplastics on Aquatic and Terrestrial Ecosystems. MiMRAplastics in Terrestrial Environments: Emerging Contaminants and Major Challenges. Springer International Publishing, (ss. 223-243). https://doi.org/10.1007/698_2020_456
  • Puangdownreong, D., Hlungnamtip, S., Thammarat, C., & Nawikavatan, A. (2017). Application of flower pollination algorithm to parameter identification of DC motor model. 2017 International Electrical Engineering Congress (iEECON), 1-4. https://doi.org/10.1109/IEECON.2017.8075889
  • Ravber, M., Liu, S.-H., Mernik, M., & Črepinšek, M. (2022). Maximum number of generations as a stopping criterion considered harmful. Applied Soft Computing, 128, 109478. https://doi.org/10.1016/j.AAAc.2022.109478
  • Salcedo-Sanz, S., Del Ser, J., Landa-Torres, I., Gil-López, S., & Portilla-Figueras, J. A. (2014). The Coral Reefs Optimization Algorithm: A Novel Metaheuristic for Efficiently Solving Optimization Problems. The Scientific World Journal, 2014, e739768. https://doi.org/10.1155/2014/739768
  • Shadkam, E., Safari, S., & Abdollahzadeh, S. S. (2021). Finally, which meta-heuristic algorithm is the best one? International Journal of Decision Sciences, Risk and Management, 10(1-2), 32-50. https://doi.org/10.1504/IJDSRM.2021.117555
  • Shaikh, M. S., Raj, S., Babu, R., Kumar, S., & Sagrolikar, K. (2023). A hybrid moth–flame algorithm with particle swarm optimization with application in power transmission and distribution. Decision Analytics Journal, 6, 100182. https://doi.org/10.1016/j.dajour.2023.100182
  • Shi, Y. (2011). Brain Storm Optimization Algorithm. Içinde Y. Tan, Y. Shi, Y. Chai, & G. Wang (Ed.), Advances in Swarm Intelligence (ss. 303-309). Springer. https://doi.org/10.1007/978-3-642-21515-5_36
  • Singh, P., Meena, N. K., Yang, J., Vega-Fuentes, E., & Bishnoi, S. K. (2020). Multi-criteria decision making monarch butterfly optimization for optimal distributed energy resources mix in distribution neHÇArks. Applied Energy, 278, 115723. https://doi.org/10.1016/j.apenergy.2020.115723
  • Singh, S., Ashok, A., Rawat, T. K., & Kumar, M. (2016). Optimal IIR system identification using flower pollination algorithm. 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), 1-6. https://doi.org/10.1109/ICPEICES.2016.7853666
  • Sompracha, C., & Rukkaphan, S. (2021). Fractional-Order System Identification of Temperature Process Rig Control System using Flower Pollination Algorithm. 2021 9th International Electrical Engineering Congress (iEECON), 309-312. https://doi.org/10.1109/iEECON51072.2021.944032
  • Omotoso O., H., Al-Shaalan, A. M., H Farh, H. M., & Al-Shamma, A. A. (2022). Citation: Omotoso, HEconomic Evaluation of Hybrid Energy Systems Using Artificial Ecosystem-Based Optimization with Demand Side Techno-Economic Evaluation of Hybrid Energy Systems Using Artificial Ecosystem-Based Optimization with Demand Side Management. https://doi.org/10.3390/electronics11020204
  • Tian, T., Liu, C., Guo, Q., Yuan, Y., Li, W., & Yan, Q. (2018). An Improved Ant Lion Optimization Algorithm and Its Application in Hydraulic Turbine Governing System Parameter Identification. Energies, 11(1), Article 1. https://doi.org/10.3390/en11010095
  • Wu, Z., Shen, D., Shang, M., & Qi, S. (2019). Parameter Identification of Single-Phase Inverter Based on Improved Moth Flame Optimization Algorithm. Electric Power Components and Systems, 47(4-5), 456-469. https://doi.org/10.1080/15325008.2019.1607922
  • Wu, Z., Yu, D., & Kang, X. (2017). Parameter identification of photovoltaic cell model based on improved ant lion optimizer. Energy Conversion and Management, 151, 107-115. https://doi.org/10.1016/j.enconman.2017.08.088
  • Yan, Z., Li, C., Song, Z., Xiong, L., & Luo, C. (2019). An Improved Brainstorming Optimization Algorithm for Estimating Parameters of Photovoltaic Models. IEEE Access, 7, 77629-77641. https://doi.org/10.1109/ACCESS.2019.2922327
  • Yang, X.-S. (2012). Flower Pollination Algorithm for Global Optimization. (Ed.), Unconventional Computation and Natural Computation (ss. 240-249). Springer. https://doi.org/10.1007/978-3-642-32894-7_27
  • Yang, X.-S. (2020). Nature-inspired optimization algorithms: Challenges and open problems. Journal of Computational Science, 46, 101104. https://doi.org/10.1016/j.jocs.2020.101104
  • Yang, Y., Yang, B., & Niu, M. (2017). Parameter identification of Jiles–Atherton model for magnetostrictive actuator using hybrid niching coral reefs optimization algorithm. Sensors and Actuators A: Physical, 261, 184-195. https://doi.org/10.1016/j.sna.2017.05.009
  • Yang, Y., Yang, B., & Niu, M. (2018). Adaptive infinite impulse response system identification using opposition based hybrid coral reefs optimization algorithm. Applied Intelligence, 48(7), 1689-1706. https://doi.org/10.1007/s10489-017-1034-9
  • Yin, M., Iannelli, A., Khosravi, M., Parsi, A., & Smith, R. S. (2020). Linear Time-Periodic System Identification with Grouped Atomic Norm Regularization. IFAC-Papers OnLine, 53(2), 1237-1242. https://doi.org/10.1016/j.ifacol.2020.12.1341
  • Yousri, D., Allam, D., Babu, T. S., AbdelAty, A. M., Radwan, A. G., Ramachandaramurthy, Vigna. K., & Eteiba, M. B. (2020). Fractional chaos maps with flower pollination algorithm for chaotic systems’ parameters identification. Neural Computing and Applications, 32(20), 16291-16327. https://doi.org/10.1007/s00521-020-04906-7
  • Zaloğlu, M., Fidan, Ş., & Erkan, E. (2023). Meta-Heuristik Optimizasyon Algoritmalarının Sistem Tanımlama Problemine Uygulanması ve Performans Karşılaştırması. International Conference on Engineering, Natural and Social Sciences, 1, 510-515.
  • Zhao, W., Wang, L., & Zhang, Z. (2019). Atom search optimization and its application to solve a hydrogeologic parameter estimation problem. Knowledge-Based Systems, 163, 283-304. https://doi.org/10.1016/j.knosys.2018.08.030

TOPSIS Yaklaşımı ile Metasezgisel Optimizasyon Algoritmalarının Performans Değerlendirmesi

Yıl 2024, Cilt: 24 Sayı: 3, 726 - 748, 27.06.2024

Şehmus Fidan , Metin Zaloğlu , Emre Erkan

https://doi.org/10.35414/akufemubid.1387447

Öz

Bir sistemin sadece giriş/çıkış verilerinin kullanılarak matematiksel bir model elde etmek için doğadan ilham alan metasezgisel algoritmalar kullanılabilir. Bunu gerçekleştirmek için yapay ekosistem (YEA), çiçek tozlaşma (ÇTA), güve-alev (GAA), karınca aslanı algoritması (KAA), halat çekme (HÇA), atom arama (AAA), beyin fırtınası (BFA), su döngüsü (SDA), mercan resifleri (MRA) ve yaşam seçimi tabanlı algoritma (YSTA) gibi çeşitli metasezgisel optimizasyon algoritmaları ele alınmış ve önerilen transfer fonksiyonunun parametrelerini optimize etmek için kullanılmıştır. Ayrıca zaman, maksimum fonksiyon, erken durdurma ve maksimum generasyon sınırlılıkları altında performanslar karşılaştırılmıştır. Ancak bu durumda MAE, MAPE, R2 gibi performans metriklerinin yanında transfer fonksiyonlarına özgü yükselme zamanı, oturma zamanı, aşım miktarı gibi metrikler de ortaya çıkmaktadır. Çok sayıdaki metrik hangi algoritmanın en iyi olduğunu belirlemeyi zorlaştırmaktadır. Bu zorluğun üzerinden gelmek için bu çalışmada Topsis (Technique for Order Preference by Similarity) olarak anılan çok kriterli bir karar verme yaklaşımının kullanımını önerilmiştir. Çoklu kriter için algoritmanın çözüm zamanı, performans (R2) ve yükselme zamanı dikkate alınmıştır. Yapılan çalışma neticesinde en iyi algoritma sıralamasını belirlemek oldukça kolay ve pratik bir şekilde gerçekleştirilmiştir.

Anahtar Kelimeler

Metasezgisel Algoritma Sistem Tanımlama Transfer Fonksiyonu Topsis.

Kaynakça

  • Canayaz, M. (2019). Training Anfis System with Moth-Flame Optimization Algorithm. International Journal of Intelligent Systems and Applications in Engineering, 7(3), Article 3. https://doi.org/10.18201/ijisae.2019355375
  • Çelikel, R., & Gundogdu, A. (2020). System identification-based MPPT algorithm for PV systems under variable atmosphere conditions using current sensorless approach. International Transactions on Electrical Energy Systems, 30(8), e12433.
  • Chen, Y., Pi, D., & Wang, B. (2019). Enhanced global flower pollination algorithm for parameter identification of chaotic and hyper-chaotic system. Nonlinear Dynamics, 97(2), 1343-1358. https://doi.org/10.1007/s11071-019-05052-z
  • Crispim, J. A., & Pinho de Sousa, J. (2009). Partner selection in virtual enterprises: A multi-criteria decision support approach. International Journal of Production Research, 47(17), 4791-4812. https://doi.org/10.1080/00207540902847348
  • Ding, S., Shi, Z., Chen, K., & Azar, A. T. (2015). Mathematical Modelling and Analysis of Soft Computing. Mathematical Problems in Engineering, 2015, e578321. https://doi.org/10.1155/2015/578321
  • Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. IEEE Computational Intelligence Magazine, 1(4), 28-39. https://doi.org/10.1109/MCI.2006.329691
  • El‐Dabah, M. A., & El‐Sehiemy…, R. A. (2021). Parameter estimation of triple diode photovoltaic model using an artificial ecosystem based optimizer. Int Trans Electr Energ Syst. 31(11):e13043. https://doi.org/10.1002/2050-7038.13043
  • Eskandar, H., Sadollah, A., Bahreininejad, A., & Hamdi, M. (2012). Water cycle algorithm – A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures, 110-111, 151-166. https://doi.org/10.1016/j.compstruc.2012.07.010
  • Fadzli, A. A. M., Hadi, M. S., Eek, R. T. P., Talib, M. H. Ab., Yatim, H. M., & Darus, I. Z. M. (2022). PID Controller Based on Flower Pollination Algorithm of Flexible Beam System. Recent Trends in Mechatronics Towards Industry 4.0. Springer, 173-183 https://doi.org/10.1007/978-981-33-4597-3_17
  • Fan, S., Zhang, J., Blanco-Davis, E., Yang, Z., & Yan, X. (2020). Maritime accident prevention strategy formulation from a human factor perspective using Bayesian NeHÇArks and TOPSIS. Ocean Engineering, 210, 107544. https://doi.org/10.1016/j.oceaneng.2020.107544
  • Farag, M. A., El-Shorbagy, M. A., Mousa, A. A., & El-Desoky, I. M. (2020). A New Hybrid Metaheuristic Algorithm for Multi objective Optimization Problems. International Journal of Computational Intelligence Systems, 13(1), 920-940. https://doi.org/10.2991/ijcis.d.200618.001
  • Fidan, Ş., Sevim, D., & Erkan, E. (2022). System Identification and Control of High Voltage Boost Converter. 2022 Global Energy Conference (GEC), 25-31. https://doi.org/10.1109/GEC55014.2022.9986621
  • Guo, Y., Shi, Q., & Guo, C. (2022). A Performance-Oriented Optimization Framework Combining Meta-Heuristics and Entropy-Weighted TOPSIS for Multi-Objective Sustainable Supply Chain NeHÇArk Design. Electronics, 11(19), Article 19. https://doi.org/10.3390/electronics11193134
  • Izci, D. (2022). A novel modified arithmetic optimization algorithm for power system stabilizer design. Sigma Journal of Engineering and Natural Sciences, 40(3), 3.
  • Izci, D., Hekimoğlu, B., & Ekinci, S. (2022). A new artificial ecosystem-based optimization integrated with Nelder-Mead method for PID controller design of buck converter. Alexandria Engineering Journal, 61(3), 2030-2044. https://doi.org/10.1016/j.aej.2021.07.037
  • Janjanam, L., Saha, S. K., Kar, R., & Mandal, D. (2022). Wiener model-based system identification using moth flame optimised Kalman filter algorithm. Signal, Image and Video Processing, 16(5), 1425-1433. https://doi.org/10.1007/s11760-021-02096-w
  • Ji, Y., Jiang, X., & Wan, L. (2020). Hierarchical least squares parameter estimation algorithm for HÇA-input Hammerstein finite impulse response systems. Journal of the Franklin Institute, 357(8), 5019-5032. https://doi.org/10.1016/j.jfranklin.2020.03.027
  • Kalita, K., Pal, S., Haldar, S., & Chakraborty, S. (2022). A Hybrid TOPSIS-PR-GWO Approach for Multi-objective Process Parameter Optimization. Process Integration and Optimization for Sustainability, 6(4), 1011-1026. https://doi.org/10.1007/s41660-022-00256-0
  • 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. https://doi.org/10.1007/s10898-007-9149-x
  • Kaveh, A. and Bakhshpoori T. (2021). Tug of War Optimization, Advances in Metaheuristic Algorithms for Optimal Design of Structures. Springer International Publishing, 467-503. https://doi.org/10.1007/978-3-030-59392-6_15
  • Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN’95 - International Conference on Neural NeHÇArks, 4, 1942-1948 c.4. https://doi.org/10.1109/ICNN.1995.488968
  • Khatri, A., Gaba, A., Rana, K. P. S., & Kumar, V. (2020). A novel life choice-based optimizer. Soft Computing, 24(12), 9121-9141. https://doi.org/10.1007/s00500-019-04443-z
  • Khluabwannarat, P., Nawikavatan, A., & Puangdownreong, D. (2018). Fractional-Order Model Parameter Identification of BLDC Motor by Flower Pollination Algorithm. 13.
  • Kler, D., Sharma, P., Banerjee, A., Rana, K. P. S., & Kumar, V. (2017). PV cell and module efficient parameters estimation using Evaporation Rate based Water Cycle Algorithm. Swarm and Evolutionary Computation, 35, 93-110. https://doi.org/10.1016/j.swevo.2017.02.005
  • Kumbasar, T., Eksin, I., Guzelkaya, M., & Yesil, E. (2011). Adaptive fuzzy model based inverse controller design using BB-BC optimization algorithm. Expert Systems with Applications, 38(10), 12356-12364. https://doi.org/10.1016/j.eswa.2011.04.015
  • Long, B., Yang, W., Hu, Q., Guerrero, J. M., Garcia, C., Rodriguez, J., & Chong, K. T. (2022). Moth–Flame-Optimization-Based Parameter Estimation for FCS-MPC-Controlled Grid-Connected Converter With LCL Filter. IEEE Journal of Emerging and Selected Topics in Power Electronics, 10(4), 4102-4114. https://doi.org/10.1109/JESTPE.2022.3140228
  • Mirjalili, S. (2015a). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89, 228-249. https://doi.org/10.1016/j.knosys.2015.07.006
  • Mirjalili, S. (2015b). The Ant Lion Optimizer. Advances in Engineering Software, 83, 80-98. https://doi.org/10.1016/j.advengsoft.2015.01.010
  • Mohammadi, A., Sheikholeslam, F., & Mirjalili, S. (2022). Inclined planes system optimization: Theory, literature review, and state-of-the-art versions for IIR system identification. Expert Systems with Applications, 200, 117127. https://doi.org/10.1016/j.eswa.2022.117127
  • Mossa, M. A., Kamel, O. M., Sultan, H. M., & Diab, A. A. Z. (2021). Parameter estimation of PEMFC model based on Harris Hawks’ optimization and atom search optimization algorithms. Neural Computing and Applications, 33(11), 5555-5570. https://doi.org/10.1007/s00521-020-05333-4
  • Nair, S. S., Rana, K. P. S., Kumar, V., & Chawla, A. (2017). Efficient Modelling of Linear Discrete Filters Using Ant Lion Optimizer. Circuits, Systems, and Signal Processing, 36(4), 1535-1568. https://doi.org/10.1007/s00034-016-0370-z
  • Nazir, M. I., Ahmad, A., & Hussain, I. (2022). Water Cycle Algorithm Based Parametric Tuning of Non-Negative LMMN Control of Grid Tied Renewable Energy Systems. IETE Journal of Research, 0(0), 1-17. https://doi.org/10.1080/03772063.2022.2089748
  • Nguyen, T. T. (2023) A novel metaheuristic method based on artificial ecosystem-based optimization for optimization of neHÇArk reconfiguration to reduce power loss. https://doi.org/10.1007/s00500-021-06346-4
  • Pal, P. S., Kar, R., Mandal, D., & Ghoshal, S. P. (2016). Identification of NARMAX Hammerstein models with performance assessment using brainstorm optimization algorithm. International Journal of Adaptive Control and Signal Processing, 30(7), 1043-1070. https://doi.org/10.1002/acs.2674
  • Prakash, V., Dwivedi, S., Gautam, K., Seth, M., & Anbumani, S. (2020). Occurrence and Ecotoxicological Effects of MiMRAplastics on Aquatic and Terrestrial Ecosystems. MiMRAplastics in Terrestrial Environments: Emerging Contaminants and Major Challenges. Springer International Publishing, (ss. 223-243). https://doi.org/10.1007/698_2020_456
  • Puangdownreong, D., Hlungnamtip, S., Thammarat, C., & Nawikavatan, A. (2017). Application of flower pollination algorithm to parameter identification of DC motor model. 2017 International Electrical Engineering Congress (iEECON), 1-4. https://doi.org/10.1109/IEECON.2017.8075889
  • Ravber, M., Liu, S.-H., Mernik, M., & Črepinšek, M. (2022). Maximum number of generations as a stopping criterion considered harmful. Applied Soft Computing, 128, 109478. https://doi.org/10.1016/j.AAAc.2022.109478
  • Salcedo-Sanz, S., Del Ser, J., Landa-Torres, I., Gil-López, S., & Portilla-Figueras, J. A. (2014). The Coral Reefs Optimization Algorithm: A Novel Metaheuristic for Efficiently Solving Optimization Problems. The Scientific World Journal, 2014, e739768. https://doi.org/10.1155/2014/739768
  • Shadkam, E., Safari, S., & Abdollahzadeh, S. S. (2021). Finally, which meta-heuristic algorithm is the best one? International Journal of Decision Sciences, Risk and Management, 10(1-2), 32-50. https://doi.org/10.1504/IJDSRM.2021.117555
  • Shaikh, M. S., Raj, S., Babu, R., Kumar, S., & Sagrolikar, K. (2023). A hybrid moth–flame algorithm with particle swarm optimization with application in power transmission and distribution. Decision Analytics Journal, 6, 100182. https://doi.org/10.1016/j.dajour.2023.100182
  • Shi, Y. (2011). Brain Storm Optimization Algorithm. Içinde Y. Tan, Y. Shi, Y. Chai, & G. Wang (Ed.), Advances in Swarm Intelligence (ss. 303-309). Springer. https://doi.org/10.1007/978-3-642-21515-5_36
  • Singh, P., Meena, N. K., Yang, J., Vega-Fuentes, E., & Bishnoi, S. K. (2020). Multi-criteria decision making monarch butterfly optimization for optimal distributed energy resources mix in distribution neHÇArks. Applied Energy, 278, 115723. https://doi.org/10.1016/j.apenergy.2020.115723
  • Singh, S., Ashok, A., Rawat, T. K., & Kumar, M. (2016). Optimal IIR system identification using flower pollination algorithm. 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), 1-6. https://doi.org/10.1109/ICPEICES.2016.7853666
  • Sompracha, C., & Rukkaphan, S. (2021). Fractional-Order System Identification of Temperature Process Rig Control System using Flower Pollination Algorithm. 2021 9th International Electrical Engineering Congress (iEECON), 309-312. https://doi.org/10.1109/iEECON51072.2021.944032
  • Omotoso O., H., Al-Shaalan, A. M., H Farh, H. M., & Al-Shamma, A. A. (2022). Citation: Omotoso, HEconomic Evaluation of Hybrid Energy Systems Using Artificial Ecosystem-Based Optimization with Demand Side Techno-Economic Evaluation of Hybrid Energy Systems Using Artificial Ecosystem-Based Optimization with Demand Side Management. https://doi.org/10.3390/electronics11020204
  • Tian, T., Liu, C., Guo, Q., Yuan, Y., Li, W., & Yan, Q. (2018). An Improved Ant Lion Optimization Algorithm and Its Application in Hydraulic Turbine Governing System Parameter Identification. Energies, 11(1), Article 1. https://doi.org/10.3390/en11010095
  • Wu, Z., Shen, D., Shang, M., & Qi, S. (2019). Parameter Identification of Single-Phase Inverter Based on Improved Moth Flame Optimization Algorithm. Electric Power Components and Systems, 47(4-5), 456-469. https://doi.org/10.1080/15325008.2019.1607922
  • Wu, Z., Yu, D., & Kang, X. (2017). Parameter identification of photovoltaic cell model based on improved ant lion optimizer. Energy Conversion and Management, 151, 107-115. https://doi.org/10.1016/j.enconman.2017.08.088
  • Yan, Z., Li, C., Song, Z., Xiong, L., & Luo, C. (2019). An Improved Brainstorming Optimization Algorithm for Estimating Parameters of Photovoltaic Models. IEEE Access, 7, 77629-77641. https://doi.org/10.1109/ACCESS.2019.2922327
  • Yang, X.-S. (2012). Flower Pollination Algorithm for Global Optimization. (Ed.), Unconventional Computation and Natural Computation (ss. 240-249). Springer. https://doi.org/10.1007/978-3-642-32894-7_27
  • Yang, X.-S. (2020). Nature-inspired optimization algorithms: Challenges and open problems. Journal of Computational Science, 46, 101104. https://doi.org/10.1016/j.jocs.2020.101104
  • Yang, Y., Yang, B., & Niu, M. (2017). Parameter identification of Jiles–Atherton model for magnetostrictive actuator using hybrid niching coral reefs optimization algorithm. Sensors and Actuators A: Physical, 261, 184-195. https://doi.org/10.1016/j.sna.2017.05.009
  • Yang, Y., Yang, B., & Niu, M. (2018). Adaptive infinite impulse response system identification using opposition based hybrid coral reefs optimization algorithm. Applied Intelligence, 48(7), 1689-1706. https://doi.org/10.1007/s10489-017-1034-9
  • Yin, M., Iannelli, A., Khosravi, M., Parsi, A., & Smith, R. S. (2020). Linear Time-Periodic System Identification with Grouped Atomic Norm Regularization. IFAC-Papers OnLine, 53(2), 1237-1242. https://doi.org/10.1016/j.ifacol.2020.12.1341
  • Yousri, D., Allam, D., Babu, T. S., AbdelAty, A. M., Radwan, A. G., Ramachandaramurthy, Vigna. K., & Eteiba, M. B. (2020). Fractional chaos maps with flower pollination algorithm for chaotic systems’ parameters identification. Neural Computing and Applications, 32(20), 16291-16327. https://doi.org/10.1007/s00521-020-04906-7
  • Zaloğlu, M., Fidan, Ş., & Erkan, E. (2023). Meta-Heuristik Optimizasyon Algoritmalarının Sistem Tanımlama Problemine Uygulanması ve Performans Karşılaştırması. International Conference on Engineering, Natural and Social Sciences, 1, 510-515.
  • Zhao, W., Wang, L., & Zhang, Z. (2019). Atom search optimization and its application to solve a hydrogeologic parameter estimation problem. Knowledge-Based Systems, 163, 283-304. https://doi.org/10.1016/j.knosys.2018.08.030
Toplam 57 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Otomasyon Mühendisliği
Bölüm Makaleler
Yazarlar

Şehmus Fidan 0000-0002-5249-7245

Metin Zaloğlu 0009-0003-0589-8868

Emre Erkan 0000-0003-0187-4079

Erken Görünüm Tarihi 8 Haziran 2024
Yayımlanma Tarihi 27 Haziran 2024
Gönderilme Tarihi 7 Kasım 2023
Kabul Tarihi 7 Mayıs 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 24 Sayı: 3

Kaynak Göster

APA Fidan, Ş., Zaloğlu, M., & Erkan, E. (2024). TOPSIS Yaklaşımı ile Metasezgisel Optimizasyon Algoritmalarının Performans Değerlendirmesi. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 24(3), 726-748. https://doi.org/10.35414/akufemubid.1387447
AMA Fidan Ş, Zaloğlu M, Erkan E. TOPSIS Yaklaşımı ile Metasezgisel Optimizasyon Algoritmalarının Performans Değerlendirmesi. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. Haziran 2024;24(3):726-748. doi:10.35414/akufemubid.1387447
Chicago Fidan, Şehmus, Metin Zaloğlu, ve Emre Erkan. “TOPSIS Yaklaşımı Ile Metasezgisel Optimizasyon Algoritmalarının Performans Değerlendirmesi”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 24, sy. 3 (Haziran 2024): 726-48. https://doi.org/10.35414/akufemubid.1387447.
EndNote Fidan Ş, Zaloğlu M, Erkan E (01 Haziran 2024) TOPSIS Yaklaşımı ile Metasezgisel Optimizasyon Algoritmalarının Performans Değerlendirmesi. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 24 3 726–748.
IEEE Ş. Fidan, M. Zaloğlu, ve E. Erkan, “TOPSIS Yaklaşımı ile Metasezgisel Optimizasyon Algoritmalarının Performans Değerlendirmesi”, Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, c. 24, sy. 3, ss. 726–748, 2024, doi: 10.35414/akufemubid.1387447.
ISNAD Fidan, Şehmus vd. “TOPSIS Yaklaşımı Ile Metasezgisel Optimizasyon Algoritmalarının Performans Değerlendirmesi”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 24/3 (Haziran 2024), 726-748. https://doi.org/10.35414/akufemubid.1387447.
JAMA Fidan Ş, Zaloğlu M, Erkan E. TOPSIS Yaklaşımı ile Metasezgisel Optimizasyon Algoritmalarının Performans Değerlendirmesi. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. 2024;24:726–748.
MLA Fidan, Şehmus vd. “TOPSIS Yaklaşımı Ile Metasezgisel Optimizasyon Algoritmalarının Performans Değerlendirmesi”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, c. 24, sy. 3, 2024, ss. 726-48, doi:10.35414/akufemubid.1387447.
Vancouver Fidan Ş, Zaloğlu M, Erkan E. TOPSIS Yaklaşımı ile Metasezgisel Optimizasyon Algoritmalarının Performans Değerlendirmesi. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. 2024;24(3):726-48.
 Kapak Resmi İndir