Optimizing Generation Cost and Reducing Gas Emissions in Power Generation Using the Artificial Bee Rabbit Optimization Algorithm

Wei Wen  Lee; Mohd Ruzaini Hashim; Chin Kim Gan

doi:10.31436/iiumej.v26i3.3800

Authors

Wei Wen Lee Universiti Teknikal Malaysia Melaka https://orcid.org/0000-0002-7425-6098
Mohd Ruzaini Hashim Universiti Teknikal Malaysia Melaka https://orcid.org/0000-0001-8228-3865
Chin Kim Gan Universiti Teknikal Malaysia Melaka

DOI:

https://doi.org/10.31436/iiumej.v26i3.3800

Keywords:

optimization algorithm, artificial bee colony algorithm, hybrid algorithm, economic dispatched, emission dispatched

Abstract

This study develops a hybrid metaheuristic optimization algorithm named Artificial Bee Rabbit Optimization (ABRO) to improve generation cost efficiency and reduce gas emissions in power generation systems. By integrating the strengths of the Artificial Bee Colony (ABC) and Artificial Rabbits Optimization (ARO) algorithms, ABRO aims to overcome issues such as premature convergence and slow convergence speed commonly observed in ABC and ARO. This paper evaluates and compares the ABRO algorithm against a collection of optimization algorithms, such as ABC, ARO, the Crow Search (CSA) algorithm, and the Artificial Jellyfish Search (JS) algorithm. The evaluation covers four benchmark functions and extends to engineering applications, specifically in solving the economic dispatch, emission dispatch, and an integrated objective that considers financial and emission dispatch aspects for the IEEE 26-bus system. The simulation results show that ABRO generally outperforms the competing algorithms tested in solving various benchmark functions. ABRO consistently achieved the lowest mean, standard deviation, and minimum values, demonstrating superior convergence speed, robustness, and accuracy. Furthermore, the ABRO algorithm effectively enhances optimization regarding generation cost, generation emission, and an integrated objective that considers both economic and emission dispatch aspects for the IEEE 26-bus system

ABSTRAK: Kajian ini membangunkan satu algoritma pengoptimuman metaheuristik hibrid yang dinamakan Pengoptimuman Buatan Bee Rabbit (ABRO) bagi meningkatkan kecekapan kos penjanaan dan mengurangkan pelepasan gas dalam sistem penjanaan tenaga. Gabungan kekuatan antara Koloni Buatan Bee (ABC) dan Pengoptimuman Buatan Rabbit (ARO), menghasilkan ABRO yang bertujuan mengatasi isu penumpuan pramatang dan kelajuan penumpuan perlahan, ysng sering berlaku pada ABC dan ARO. Kajian ini menilai algoritma ABRO dan beberapa algoritma pengoptimuman lain seperti ABC, ARO, algoritma Pencarian Crow (CSA), dan pengoptimum Pencarian Buatan Jellyfish (JS). Penilaian meliputi empat fungsi penanda aras dan diperluas kepada aplikasi kejuruteraan, khususnya dalam penyelesaian masalah pengagihan ekonomi, pengagihan pelepasan, serta objektif bersepadu yang memgambil kira kedua-dua aspek ekonomi dan pelepasan bagi sistem IEEE 26-bas. Dapatan simulasi menunjukkan bahawa algoritma ABRO secara amnya mengatasi prestasi algoritma lain yang diuji dalam menyelesaikan pelbagai fungsi penanda aras. ABRO secara konsisten mencapai nilai min, sisihan piawai, dan nilai minimum terendah, sekali gus membuktikan kelajuan penumpuan, keteguhan, dan ketepatan terbaik. Tambahan pula, algoritma ABRO mampu meningkatkan pengoptimuman dari segi kos penjanaan, pelepasan penjanaan, serta objektif bersepadu yang mempertimbangkan kedua-dua aspek ekonomi dan pelepasan bagi sistem IEEE 26-bas.

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References

Mahmoud M. El-Halwagi, Ed., “Overview of optimization,” in process system engineering, vol. 7, Academic Press, 2006, pp. 285–314. doi: 10.1016/S1874-5970(06)80012-3. DOI: https://doi.org/10.1016/S1874-5970(06)80012-3

C. Peel and T. K. Moon, “Algorithms for Optimization [Bookshelf],” IEEE Control Syst., vol. 40, no. 2, pp. 92–94, Apr. 2020, doi: 10.1109/MCS.2019.2961589. DOI: https://doi.org/10.1109/MCS.2019.2961589

A. P. Engelbrecht, Computational Intelligence, Second. Wiley, 2007. doi: 10.1002/9780470512517. DOI: https://doi.org/10.1002/9780470512517

D. Karaboga, “An idea based on honey bee swarm for numerical optimization., Technical Report - TR06” in Erciyes University, 2005, pp. 1–10.

J.-S. Chou and D.-N. Truong, “A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean,” Appl. Math. Comput., vol. 389, p. 125535, Jan. 2021, doi: 10.1016/j.amc.2020.125535. DOI: https://doi.org/10.1016/j.amc.2020.125535

L. Wang, Q. Cao, Z. Zhang, S. Mirjalili, and W. Zhao, “Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems,” Eng. Appl. Artif. Intell., vol. 114, p. 105082, Sep. 2022, doi: 10.1016/j.engappai.2022.105082. DOI: https://doi.org/10.1016/j.engappai.2022.105082

D. H. Wolpert and W. G. Macready, “No free lunch theorems for optimization,” IEEE Trans. Evol. Comput., vol. 1, no. 1, pp. 67–82, Apr. 1997, doi: 10.1109/4235.585893. DOI: https://doi.org/10.1109/4235.585893

?. Öztürk, R. Ahmad, and N. Akhtar, “Variants of Artificial Bee Colony algorithm and its applications in medical image processing,” Appl. Soft Comput., vol. 97, p. 106799, Dec. 2020, doi: 10.1016/j.asoc.2020.106799. DOI: https://doi.org/10.1016/j.asoc.2020.106799

M. Ettappan, V. Vimala, S. Ramesh, and V. T. Kesavan, “Optimal reactive power dispatch for real power loss minimization and voltage stability enhancement using Artificial Bee Colony Algorithm,” Microprocess. Microsyst., vol. 76, p. 103085, Jul. 2020, doi: DOI: https://doi.org/10.1016/j.micpro.2020.103085

Z. Wang, H. Ding, B. Li, L. Bao, and Z. Yang, “An Energy Efficient Routing Protocol Based on Improved Artificial Bee Colony Algorithm for Wireless Sensor Networks,” IEEE Access, vol. 8, pp. 133577–133596, 2020, doi: 10.1109/ACCESS.2020.3010313. DOI: https://doi.org/10.1109/ACCESS.2020.3010313

A. Sharma, A. Sharma, S. Choudhary, R. K. Pachauri, A. Shrivastava, and D. Kumar, “A review on artificial bee colony and its engineering applications,” J. Crit. Rev., vol. 7, no. 11, pp. 4097–4107, 2020, doi: 10.31838/jcr.07.11.558.

M. Zhang, Y. Pan, J. Zhu, and G. Chen, “ABC-TLBO: A Hybrid Algorithm Based on Artificial Bee Colony and Teaching-Learning-Based Optimization,” in 2018 37th Chinese Control Conference (CCC), IEEE, Jul. 2018, pp. 2410–2417. doi: 10.23919/ChiCC.2018.8483829. DOI: https://doi.org/10.23919/ChiCC.2018.8483829

A. Yurtkuran and E. Emel, “An adaptive artificial bee colony algorithm for global optimization,” Appl. Math. Comput., vol. 271, pp. 1004–1023, Nov. 2015, doi: 10.1016/j.amc.2015.09.064. DOI: https://doi.org/10.1016/j.amc.2015.09.064

J. Luo, Q. Liu, Y. Yang, X. Li, M. Chen, and W. Cao, “An artificial bee colony algorithm for multi-objective optimisation,” Appl. Soft Comput., vol. 50, pp. 235–251, Jan. 2017, doi: 10.1016/j.asoc.2016.11.014. DOI: https://doi.org/10.1016/j.asoc.2016.11.014

W. W. Lee and M. R. Bin Hashim, “A Hybrid Algorithm Based on Artificial Bee Colony and Artificial Rabbits Optimization for Solving Economic Dispatch Problem,” in 2023 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS), IEEE, Jun. 2023, pp. 298–303. doi: 10.1109/I2CACIS57635.2023.10193351. DOI: https://doi.org/10.1109/I2CACIS57635.2023.10193351

A. Askarzadeh, “A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm,” Comput. Struct., vol. 169, pp. 1–12, Jun. 2016, doi: 10.1016/j.compstruc.2016.03.001. DOI: https://doi.org/10.1016/j.compstruc.2016.03.001

M. A. Bagherian and K. Mehranzamir, “A comprehensive review on renewable energy integration for combined heat and power production,” Energy Convers. Manag., vol. 224, p. 113454, Nov. 2020, doi: 10.1016/j.enconman.2020.113454. DOI: https://doi.org/10.1016/j.enconman.2020.113454

M. Gent and J. Lamont, “Minimum-Emission Dispatch,” IEEE Trans. Power Appar. Syst., vol. PAS-90, no. 6, pp. 2650–2660, Nov. 1971, doi: 10.1109/TPAS.1971.292918. DOI: https://doi.org/10.1109/TPAS.1971.292918

B. Dey, S. K. Roy, and B. Bhattacharyya, “Solving multi-objective economic emission dispatch of a renewable integrated microgrid using latest bio-inspired algorithms,” Eng. Sci. Technol. an Int. J., vol. 22, no. 1, pp. 55–66, Feb. 2019, doi: 10.1016/j.jestch.2018.10.001. DOI: https://doi.org/10.1016/j.jestch.2018.10.001

H. Jia et al., “Improved artificial rabbits algorithm for global optimization and multi-level thresholding color image segmentation,” Artif. Intell. Rev., vol. 58, no. 2, 2025, doi: 10.1007/s10462-024-11035-3. DOI: https://doi.org/10.1007/s10462-024-11035-3

M. Abdel-Basset, L. Abdel-Fatah, and A. K. Sangaiah, “Metaheuristic Algorithms: A Comprehensive Review,” in Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications, Elsevier, 2018, pp. 185–231. doi: 10.1016/B978-0-12-813314-9.00010-4. DOI: https://doi.org/10.1016/B978-0-12-813314-9.00010-4

J. Yao Lee, M. Ruzaini Hashim, and M. O. Tokhi, “Artificial Bee Colony Optimization Algorithm with Flexible Manipulator System,” vol. 3, no. 2, pp. 75–82, 2020.

N. B and J. J, “Hybrid ABC-BAT Optimization Algorithm for Localization in HWSN,” Microprocess. Microsyst., p. 104024, Jan. 2021, doi: 10.1016/j.micpro.2021.104024. DOI: https://doi.org/10.1016/j.micpro.2021.104024

Y. Yan, J. Chen, H. Huang, and S. Yang, “A hybrid algorithm for three-dimensional loading capacitated vehicle routing problems with time windows,” in Proceedings - 2020 7th International Conference on Information Science and Control Engineering, ICISCE 2020, L. S., D. Y., M. J., and C. Y., Eds., Xiamen University, School of Informatics, Xiamen City, China: Institute of Electrical and Electronics Engineers Inc., 2020, pp. 1534–1540. doi: 10.1109/ICISCE50968.2020.00304. DOI: https://doi.org/10.1109/ICISCE50968.2020.00304

C. Mala, V. Deepak, S. Prakash, and S. Lashmi Srinivasan, “A Hybrid Artificial Bee Colony Algorithmic Approach for Classification Using Neural Networks,” in EAI/Springer Innovations in Communication and Computing, H. A., R. A., M. S., and C. M.-Y., Eds., National Institute of Technology, Tiruchirappalli, Tamil Nadu, India: Springer Science and Business Media Deutschland GmbH, 2021, pp. 339–359. doi: 10.1007/978-3-030-47560-4_28. DOI: https://doi.org/10.1007/978-3-030-47560-4_28

P. Kumar, B. J. Sowmya, A. Kanavalli, and A. Vincent, “ABC-DE-WOA: A New Hybrid Algorithm for Optimization Problems,” in 2022 4th International Conference on Circuits, Control, Communication and Computing (I4C), 2022, pp. 501–506. DOI: https://doi.org/10.1109/I4C57141.2022.10057946

M. Ramachandran, S. Mirjalili, M. Malli Ramalingam, C. A. R. Charles Gnanakkan, D. S. Parvathysankar, and A. Sundaram, “A ranking-based fuzzy adaptive hybrid crow search algorithm for combined heat and power economic dispatch,” Expert Syst. Appl., vol. 197, 2022, doi: 10.1016/j.eswa.2022.116625. DOI: https://doi.org/10.1016/j.eswa.2022.116625

D. Ustun, A. Toktas, U. Erkan, and A. Akdagli, “Modified artificial bee colony algorithm with differential evolution to enhance precision and convergence performance,” Expert Syst. Appl., vol. 198, 2022, doi: 10.1016/j.eswa.2022.116930. DOI: https://doi.org/10.1016/j.eswa.2022.116930

M. Elshahed, M. A. Tolba, A. M. El-Rifaie, A. Ginidi, A. Shaheen, and S. A. Mohamed, “An Artificial Rabbits’ Optimization to Allocate PVSTATCOM for Ancillary Service Provision in Distribution Systems,” Mathematics, vol. 11, no. 2, p. 339, Jan. 2023, doi: 10.3390/math11020339. DOI: https://doi.org/10.3390/math11020339

B. Gülmez, “Stock price prediction with optimized deep LSTM network with artificial rabbits optimization algorithm,” Expert Syst. Appl., vol. 227, p. 120346, Oct. 2023, doi: 10.1016/j.eswa.2023.120346. DOI: https://doi.org/10.1016/j.eswa.2023.120346

A. O. Alsaiari, E. B. Moustafa, H. Alhumade, H. Abulkhair, and A. Elsheikh, “A coupled artificial neural network with artificial rabbits optimizer for predicting water productivity of different designs of solar stills,” Adv. Eng. Softw., vol. 175, p. 103315, Jan. 2023, doi: 10.1016/j.advengsoft.2022.103315. DOI: https://doi.org/10.1016/j.advengsoft.2022.103315

A. G. Hussien, H. A. El-Sattar, F. A. Hashim, and S. Kamel, “Enhancing optimal sizing of stand-alone hybrid systems with energy storage considering techno-economic criteria based on a modified artificial rabbits optimizer,” J. Energy Storage, vol. 78, no. October 2023, p. 109974, Feb. 2024, doi: 10.1016/j.est.2023.109974. DOI: https://doi.org/10.1016/j.est.2023.109974

F. Li, H. Xu, and F. Qiu, “Modified artificial rabbits optimization combined with bottlenose dolphin optimizer in feature selection of network intrusion detection,” Electron. Res. Arch., vol. 32, no. 3, pp. 1770–1800, 2024, doi: 10.3934/ERA.2024081. DOI: https://doi.org/10.3934/era.2024081

X. Zhou, J. Lu, J. Huang, M. Zhong, and M. Wang, “Enhancing artificial bee colony algorithm with multi-elite guidance,” Inf. Sci. (Ny)., vol. 543, pp. 242–258, Jan. 2021, doi: 10.1016/j.ins.2020.07.037. DOI: https://doi.org/10.1016/j.ins.2020.07.037

D. Bajer and B. Zori?, “An effective refined artificial bee colony algorithm for numerical optimisation,” Inf. Sci. (Ny)., vol. 504, pp. 221–275, Dec. 2019, doi: 10.1016/j.ins.2019.07.022. DOI: https://doi.org/10.1016/j.ins.2019.07.022

D. Kumar and K. K. Mishra, “Co-variance guided Artificial Bee Colony,” Appl. Soft Comput., vol. 70, pp. 86–107, Sep. 2018, doi: 10.1016/j.asoc.2018.04.050. DOI: https://doi.org/10.1016/j.asoc.2018.04.050

S. W. Scheff, “Nonparametric Statistics,” in Fundamental Statistical Principles for the Neurobiologist, Academic Press, 2016, pp. 157–182. doi: 10.1016/B978-0-12-804753-8.00008-7. DOI: https://doi.org/10.1016/B978-0-12-804753-8.00008-7

D. V. Tien, P. Hawliczek, R. Gono, and Z. Leonowicz, “Analysis and modeling of STATCOM for regulate the voltage in power systems,” in 2017 18th International Scientific Conference on Electric Power Engineering (EPE), IEEE, May 2017, pp. 1–4. doi: 10.1109/EPE.2017.7967358. DOI: https://doi.org/10.1109/EPE.2017.7967358

P. P. S. Saputra, F. D. Murdianto, R. Firmansyah, and K. Widarsono, “Economic Dispatch in IEEE 26 Bus System using Quantum Behaved Particle Swarm Optimization,” in 2020 International Conference on Applied Science and Technology (ICAST), IEEE, 2020, pp. 54–58. doi: 10.1109/iCAST51016.2020.9557625. DOI: https://doi.org/10.1109/iCAST51016.2020.9557625

M. R. M. Ridzuan, E. E. Hassan, A. R. Abdullah, and A. F. A. Kadir, “Sustainable environmental economic dispatch optimization with hybrid metaheuristic modification,” Indones. J. Electr. Eng. Comput. Sci., vol. 11, no. 1, pp. 161–168, 2018, doi: 10.11591/ijeecs.v11.i1.pp161-168. DOI: https://doi.org/10.11591/ijeecs.v11.i1.pp161-168

M. R. Hashim, “Improved spiral dynamics and artificial bee colony algorithms with application to engineering problems,” University of Sheffield, 2018.