A Hybrid PSO-GCRA Framework for Optimizing Control Systems Performance

(1) Ahmad MohdAziz Hussein Mail (Middle East University, Jordan)
(2) Saleh Ali Alomari Mail (Jadara University, Jordan)
(3) Mohammad H. Almomani Mail (The Hashemite University, Jordan)
(4) Raed Abu Zitar Mail (Liwa College, United Arab Emirates)
(5) Hazem Migdady Mail (Oman College of Management and Technology, Oman)
(6) Aseel Smerat Mail (1) Faculty of Educational Sciences, Al-Ahliyya Amman University, Amman, 19328, Jordan. 2) Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, Punjab, India. 3) Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, India. 4) Computer Technologies Engineering, Mazaya University College, Nasiriyah, Iraq. 5) Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, Tabuk 71491, Saudi Arabia)
(7) Vaclav Snasel Mail (VŠB-Technical University of Ostrava, Czech Republic)
(8) * Laith Abualigah Mail (Al al-Bayt University, Jordan)
*corresponding author

Abstract


Optimization is essential for improving the performance of control systems, particularly in scenarios that involve complex, non-linear, and dynamic behaviors. This paper introduces a new hybrid optimization framework that merges Particle Swarm Optimization (PSO) with the Greater Cane Rat Algorithm (GCRA), which we call the PSO-GCRA framework. This hybrid approach takes advantage of PSO's global exploration capabilities and GCRA's local refinement strengths to overcome the shortcomings of each algorithm, such as premature convergence and ineffective local searches. We apply the proposed framework to a real-world load forecasting challenge using data from the Australian Energy Market Operator (AEMO). The PSO-GCRA framework functions in two sequential phases: first, PSO conducts a global search to explore the solution space, and then GCRA fine-tunes the solutions through mutation and crossover operations, ensuring convergence to high-quality optima. We evaluate the performance of this framework against benchmark methods, including EMD-SVR-PSO, FS-TSFE-CBSSO, VMD-FFT-IOSVR, and DCP-SVM-WO. Comprehensive experiments are carried out using metrics such as Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and convergence rate.  The proposed PSO-GCRA framework achieves a MAPE of 2.05% and an RMSE of 3.91, outperforming benchmark methods, such as EMD-SVR-PSO (MAPE: 2.85%, RMSE: 4.49) and FS-TSFE-CBSSO (MAPE: 2.98%, RMSE: 4.69), in terms of accuracy, stability, and convergence efficiency. Comprehensive experiments were conducted using Australian Energy Market Operator (AEMO) data, with specific attention to normalization, parameter tuning, and iterative evaluations to ensure reliability and reproducibility.

Keywords


Hybrid Optimization; Particle Swarm Optimization (PSO); Greater Cane Rat Algorithm (GCRA); Control Systems Optimization

   

DOI

https://doi.org/10.31763/ijrcs.v5i1.1738
      

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[1] A. I. Dounis, C. Caraiscos, "Advanced control systems engineering for energy and comfort management in a building environment—A review," Renewable and Sustainable Energy Reviews, vol. 13, no. 6-7, pp. 1246-1261, 2009, https://doi.org/10.1016/j.rser.2008.09.015.

[2] R. Baños, F. Manzano-Agugliaro, F.G. Montoya, C. Gil, A. Alcayde, J. Gómez, "Optimization methods applied to renewable and sustainable energy: A review," Renewable and Sustainable Energy Reviews, vol. 15, no. 4, pp. 1753-1766, 2011, https://doi.org/10.1016/j.rser.2010.12.008.

[3] J. J. Grefenstette, "Optimization of Control Parameters for Genetic Algorithms," IEEE Transactions on Systems, Man, and Cybernetics, vol. 16, no. 1, pp. 122-128, 1986, https://doi.org/10.1109/TSMC.1986.289288.

[4] D. Song, X. Fan, J. Yang, A. Liu, S. Chen, and Y. H. Joo, "Power extraction efficiency optimization of horizontal-axis wind turbines through optimizing control parameters of yaw control systems using an intelligent method," Applied Energy, vol. 224, pp. 267-279, 2018, https://doi.org/10.1016/j.apenergy.2018.04.114.

[5] V. S. Tabar, M. A. Jirdehi, and R. Hemmati, "Sustainable planning of hybrid microgrid towards minimizing environmental pollution, operational cost and frequency fluctuations," Journal of Cleaner Production, vol. 203, pp. 1187-1200, 2018, https://doi.org/10.1016/j.jclepro.2018.05.059.

[6] A. Banks, J. Vincent, and C. Anyakoha, "A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications," Natural Computing, vol. 7, pp. 109-124, 2008, https://doi.org/10.1007/s11047-007-9050-z.

[7] M. H. Nadimi-Shahraki, H. Zamani, Z. Asghari Varzaneh, and S. Mirjalili, "A systematic review of the whale optimization algorithm: theoretical foundation, improvements, and hybridizations," Archives of Computational Methods in Engineering, vol. 30, no. 7, pp. 4113-4159, 2023, https://doi.org/10.1007/s11831-023-09928-7.

[8] Y. Zhang, S. Wang, and G. Ji, "A comprehensive survey on particle swarm optimization algorithm and its applications," Mathematical problems in engineering, vol. 2015, no. 1, p. 931256, 2015, https://doi.org/10.1155/2015/931256.

[9] F. Lamnabhi-Lagarrigue et al., "Systems & control for the future of humanity, research agenda: Current and future roles, impact and grand challenges," Annual Reviews in Control, vol. 43, pp. 1-64, 2017, https://doi.org/10.1016/j.arcontrol.2017.04.001.

[10] Ibraheem, P. Kumar and D. P. Kothari, "Recent philosophies of automatic generation control strategies in power systems," IEEE Transactions on Power Systems, vol. 20, no. 1, pp. 346-357, 2005, https://doi.org/10.1109/TPWRS.2004.840438.

[11] A. A. Almazroi and N. Ayub, "Deep learning hybridization for improved malware detection in smart Internet of Things," Scientific Reports, vol. 14, no. 1, p. 7838, 2024, https://doi.org/10.1038/s41598-024-57864-8.

[12] A. Chowdhury and D. De, "RGSO-UAV: Reverse Glowworm Swarm Optimization inspired UAV path-planning in a 3D dynamic environment," Ad Hoc Networks, vol. 140, p. 103068, 2023, https://doi.org/10.1016/j.adhoc.2022.103068.

[13] D. Sinoquet, G. Rousseau, and Y. Milhau, "Design optimization and optimal control for hybrid vehicles," Optimization and Engineering, vol. 12, pp. 199-213, 2011, https://doi.org/10.1007/s11081-009-9100-8.

[14] P. I. Barton, C. K. Lee, and M. Yunt, "Optimization of hybrid systems," Computers & Chemical Engineering, vol. 30, no. 10-12, pp. 1576-1589, 2006, https://doi.org/10.1016/j.compchemeng.2006.05.024.

[15] L. Abualigah, D. Izci, S. Ekinci, and R. A. Zitar, "Optimizing Aircraft Pitch Control Systems: A Novel Approach Integrating Artificial Rabbits Optimizer with PID-F Controller," International Journal of Robotics and Control Systems, vol. 4, no. 1, pp. 354-364, 2024, https://doi.org/10.31763/ijrcs.v4i1.1347.

[16] S. Ekinci, E. Eker, D. Izci, A. Smerat, and L. Abualigah, "Enhanced RSA Optimized TID Controller for Frequency Stabilization in a Two-Area Power System," International Journal of Robotics and Control Systems, vol. 4, no. 4, pp. 1886-1902, 2024, https://doi.org/10.31763/ijrcs.v4i4.1644.

[17] L. Abualigah, S. Ekinci, and D. Izci, "Aircraft Pitch Control via Filtered Proportional-Integral-Derivative Controller Design Using Sinh Cosh Optimizer," International Journal of Robotics and Control Systems, vol. 4, no. 2, pp. 746-757, 2024, https://doi.org/10.31763/ijrcs.v4i2.1433.

[18] Z. Afroz, G. Shafiullah, T. Urmee, and G. Higgins, "Modeling techniques used in building HVAC control systems: A review," Renewable and sustainable energy reviews, vol. 83, pp. 64-84, 2018, https://doi.org/10.1016/j.rser.2017.10.044.

[19] H. Ma et al., "Multi-objective production scheduling optimization and management control system of complex aerospace components: a review," The International Journal of Advanced Manufacturing Technology, vol. 127, no. 11-12, pp. 4973-4993, 2023, https://doi.org/10.1007/s00170-023-11707-4.

[20] M. N. A. Hamid et al., "Adaptive Frequency Control of an Isolated Microgrids Implementing Different Recent Optimization Techniques," International Journal of Robotics & Control Systems, vol. 4, no. 3, pp. 1000-1012, 2024, http://dx.doi.org/10.31763/ijrcs.v4i3.1432.

[21] N. T. Pham, "Design of Novel STASOSM Controller for FOC Control of Dual Star Induction Motor Drives," International Journal of Robotics and Control Systems, vol. 4, no. 3, pp. 1059-1074, 2024, http://dx.doi.org/10.31763/ijrcs.v4i3.1443.

[22] H. Sarimveis and G. Bafas, "Fuzzy model predictive control of non-linear processes using genetic algorithms," Fuzzy sets and systems, vol. 139, no. 1, pp. 59-80, 2003, https://doi.org/10.1016/S0165-0114(02)00506-7.

[23] U. Riaz, M. Tayyeb, and A. A. Amin, "A review of sliding mode control with the perspective of utilization in fault tolerant control," Recent Advances in Electrical & Electronic Engineering, vol. 14, no. 3, pp. 312-324, 2021, http://dx.doi.org/10.2174/2352096513999201120091512.

[24] L. Abualigah et al., "Particle swarm optimization algorithm: review and applications," Metaheuristic Optimization Algorithms, pp. 1-14, 2024, https://doi.org/10.1016/B978-0-443-13925-3.00019-4.

[25] A. G. Gad, "Particle swarm optimization algorithm and its applications: a systematic review," Archives of computational methods in engineering, vol. 29, no. 5, pp. 2531-2561, 2022, https://doi.org/10.1007/s11831-021-09694-4.

[26] B. Zhao, C. Guo, B. Bai, and Y. Cao, "An improved particle swarm optimization algorithm for unit commitment," International Journal of Electrical Power & Energy Systems, vol. 28, no. 7, pp. 482-490, 2006, https://doi.org/10.1016/j.ijepes.2006.02.011.

[27] Z. Xin-gang, L. Ji, M. Jin, and Z. Ying, "An improved quantum particle swarm optimization algorithm for environmental economic dispatch," Expert Systems with Applications, vol. 152, p. 113370, 2020, https://doi.org/10.1016/j.eswa.2020.113370.

[28] J. O. Agushaka, A. E. Ezugwu, A. K. Saha, J. Pal, L. Abualigah, and S. Mirjalili, "Greater cane rat algorithm (GCRA): A nature-inspired metaheuristic for optimization problems," Heliyon, vol. 10, no. 11, p. e31629, 2024, https://doi.org/10.1016/j.heliyon.2024.e31629.

[29] S. Biswas et al., "Integrating Differential Evolution into Gazelle Optimization for advanced global optimization and engineering applications," Computer Methods in Applied Mechanics and Engineering, vol. 434, p. 117588, 2025, https://doi.org/10.1016/j.cma.2024.117588.

[30] P. Jangir et al., "A cooperative strategy-based differential evolution algorithm for robust PEM fuel cell parameter estimation," Ionics, pp. 1-39, 2024, https://doi.org/10.1007/s11581-024-05963-x.

[31] M. Abdel-Salam, L. Abualigah, A. I. Alzahrani, F. Alblehai, H. J. C. M. i. A. M. Jia, and Engineering, "Boosting crayfish algorithm based on halton adaptive quadratic interpolation and piecewise neighborhood for complex optimization problems," Computer Methods in Applied Mechanics and Engineering, vol. 432, p. 117429, 2024, https://doi.org/10.1016/j.cma.2024.117429.

[32] S. Ekinci, C. Turkeri, D. Izci, L. Abualigah, M. Bajaj, and V. Blazek, "Optimizing Steam Condenser Efficiency: Integrating Logarithmic Spiral Search and Greedy Selection Mechanisms in Gazelle Optimizer for PI Controller Tuning," Results in Engineering, vol. 24, p. 103501, 2024, https://doi.org/10.1016/j.rineng.2024.103501.

[33] H. Jia, J. Zhang, H. Rao, and L. J. A. I. R. Abualigah, "Improved sandcat swarm optimization algorithm for solving global optimum problems," Artificial Intelligence Review, vol. 58, no. 1, p. 5, 2024, https://doi.org/10.1007/s10462-024-10986-x.

[34] P. Civicioglu and E. Besdok, "A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms," Artificial intelligence review, vol. 39, pp. 315-346, 2013, https://doi.org/10.1007/s10462-011-9276-0.

[35] K. Langfield-Smith, "Management control systems and strategy: a critical review," Accounting, organizations and society, vol. 22, no. 2, pp. 207-232, 1997, https://doi.org/10.1016/S0361-3682(95)00040-2.

[36] K. H. Ang, G. Chong and Yun Li, "PID control system analysis, design, and technology," IEEE Transactions on Control Systems Technology, vol. 13, no. 4, pp. 559-576, 2005, https://doi.org/10.1109/TCST.2005.847331.

[37] N. G. Leveson, M. P. E. Heimdahl, H. Hildreth and J. D. Reese, "Requirements specification for process-control systems," IEEE Transactions on Software Engineering, vol. 20, no. 9, pp. 684-707, 1994, https://doi.org/10.1109/32.317428.

[38] P. Rani, V. Parkash, and N. K. Sharma, "Technological aspects, utilization and impact on power system for distributed generation: A comprehensive survey," Renewable and Sustainable Energy Reviews, vol. 192, p. 114257, 2024, https://doi.org/10.1016/j.rser.2023.114257.

[39] H. Yue, H. He, M. Han, and S. Gong, "Active disturbance rejection control strategy for PEMFC oxygen excess ratio based on adaptive internal state estimation using unscented Kalman filter," Fuel, vol. 356, p. 129619, 2024, https://doi.org/10.1016/j.fuel.2023.129619.

[40] Y. Zhou, P. Bhowmick, L. Zhang, L. Chen, R. Nagamune, and Y. Li, "A model reference adaptive control framework for floating offshore wind turbines with collective and individual blade pitch strategy," Ocean Engineering, vol. 291, p. 116054, 2024, https://doi.org/10.1016/j.oceaneng.2023.116054.

[41] O. Mercan and J. M. Ricles, "Stability and accuracy analysis of outer loop dynamics in real‐time pseudodynamic testing of SDOF systems," Earthquake engineering & structural dynamics, vol. 36, no. 11, pp. 1523-1543, 2007, https://doi.org/10.1002/eqe.701.

[42] K. Ohnishi, N. Matsui and Y. Hori, "Estimation, identification, and sensorless control in motion control system," Proceedings of the IEEE, vol. 82, no. 8, pp. 1253-1265, 1994, https://doi.org/10.1109/5.301687.

[43] H. Sarimveis, P. Patrinos, C. D. Tarantilis, and C. T. Kiranoudis, "Dynamic modeling and control of supply chain systems: A review," Computers & operations research, vol. 35, no. 11, pp. 3530-3561, 2008, https://doi.org/10.1016/j.cor.2007.01.017.

[44] X. Zhao, Y. Sun, Y. Li, N. Jia, and J. Xu, "Applications of Machine Learning in Real-Time Control Systems: A Review," Measurement Science and Technology, vol. 36, no. 1, 2024, https://doi.org/10.1088/1361-6501/ad8947.

[45] V. Mariani, F. Kiefer, T. Schmidt, J. Haas, and T. Schwede, "Assessment of template based protein structure predictions in CASP9," Proteins: Structure, Function, and Bioinformatics, vol. 79, no. S10, pp. 37-58, 2011, https://doi.org/10.1002/prot.23177.

[46] A. Tolk, "Terms and application domains," Engineering principles of combat modeling and distributed simulation, pp. 55-78, 2012, https://doi.org/10.1002/9781118180310.ch4.

[47] D. Pinto-Fernandez et al., "Performance Evaluation of Lower Limb Exoskeletons: A Systematic Review," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 7, pp. 1573-1583, 2020, https://doi.org/10.1109/TNSRE.2020.2989481.

[48] M. Ali, H. Kotb, K. M. Aboras and N. H. Abbasy, "Design of Cascaded PI-Fractional Order PID Controller for Improving the Frequency Response of Hybrid Microgrid System Using Gorilla Troops Optimizer," IEEE Access, vol. 9, pp. 150715-150732, 2021, https://doi.org/10.1109/ACCESS.2021.3125317.

[49] S. B. Joseph, E. G. Dada, A. Abidemi, D. O. Oyewola, and B. M. Khammas, "Metaheuristic algorithms for PID controller parameters tuning: Review, approaches and open problems," Heliyon, vol. 8, no. 5, p. e09399, 2022, https://doi.org/10.1016/j.heliyon.2022.e09399.

[50] M. Rokonuzzaman, N. Mohajer, S. Nahavandi, and S. Mohamed, "Review and performance evaluation of path tracking controllers of autonomous vehicles," IET Intelligent Transport Systems, vol. 15, no. 5, pp. 646-670, 2021, https://doi.org/10.1049/itr2.12051.

[51] L. Zhu et al., "SDN controllers: A comprehensive analysis and performance evaluation study," ACM Computing Surveys (CSUR), vol. 53, no. 6, pp. 1-40, 2020, https://doi.org/10.1145/3421764.

[52] X. Xu, Y. Du and C. Qin, "Lie Group-Based Optimization of the Greater Cane Rat Algorithm," 2024 International Symposium on Parallel Computing and Distributed Systems (PCDS), pp. 1-10, 2024, https://doi.org/10.1109/PCDS61776.2024.10743786.

[53] H. Fan, "A modification to particle swarm optimization algorithm," Engineering Computations, vol. 19, no. 8, pp. 970-989, 2002, https://doi.org/10.1108/02644400210450378.

[54] Y. Jiang, T. Hu, C. Huang, and X. Wu, "An improved particle swarm optimization algorithm," Applied Mathematics and Computation, vol. 193, no. 1, pp. 231-239, 2007, https://doi.org/10.1016/j.amc.2007.03.047.

[55] F. Marini and B. Walczak, "Particle swarm optimization (PSO). A tutorial," Chemometrics and Intelligent Laboratory Systems, vol. 149, pp. 153-165, 2015, https://doi.org/10.1016/j.chemolab.2015.08.020.

[56] J. O. Agushaka, O. Akinola, A. E. Ezugwu, and O. N. Oyelade, "A novel binary greater cane rat algorithm for feature selection," Results in Control and Optimization, vol. 11, p. 100225, 2023, https://doi.org/10.1016/j.rico.2023.100225.

[57] T. S. Brinsmead, J. Hayward, and P. Graham, "Australian electricity market analysis report to 2020 and 2030," CSIRO Technical Report No. EP141067, 2014, https://arena.gov.au/assets/2017/02/CSIRO-Electricity-market-analysis-for-IGEG.pdf.

[58] S. Boroczky, B. Connell, and A. Radi, "Experiences in State Estimation at the Australian Energy Market Operator," Experiences on Use of State Estimator in Power System Operations, pp. 373-393, 2024, https://doi.org/10.1007/978-3-031-62867-2_16.

[59] D. Chicco, M. J. Warrens, and G. Jurman, "The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation," Peerj computer science, vol. 7, p. e623, 2021, https://doi.org/10.7717/peerj-cs.623.

[60] S. Kim and H. Kim, "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, vol. 32, no. 3, pp. 669-679, 2016, https://doi.org/10.1016/j.ijforecast.2015.12.003.

[61] F. X. Diebold, "Comparing predictive accuracy, twenty years later: A personal perspective on the use and abuse of Diebold–Mariano tests," Journal of Business & Economic Statistics, vol. 33, no. 1, p. 1, 2015, https://doi.org/10.1080/07350015.2014.983236.

[62] J. Zhou, H. Li, and W. Zhong, "A modified Diebold–Mariano test for equal forecast accuracy with clustered dependence," Economics Letters, vol. 207, p. 110029, 2021, https://doi.org/10.1016/j.econlet.2021.110029.


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