Exploring Multifactorial Techniques in Rat Swarm Optimization: Preliminary Results
Received: 24 February 2025 | Revised: 7 April 2025 | Accepted: 12 April 2025 | Online: 4 June 2025
Corresponding author: Haw Yuan Kang
Abstract
Test Suite Reduction (TSR) is a critical optimization challenge in software testing that aims to reduce the number of test cases while maintaining maximum requirement coverage. Traditional algorithms, such as the Rat Swarm Optimizer (RSO), struggle with scalability, especially when dealing with large datasets. Additionally, RSO is unable to solve multiple tasks simultaneously, which leads to an increased time to complete the optimization process across multiple datasets. To resolve this constraint, this paper introduces the Multi-Factorial Rat Swarm Optimizer (MFRSO), which combines Multi-Factorial Optimization (MFO) principles to allow knowledge transfer between tasks, thus increasing optimization efficiency. The performance of MFRSO was compared to that of RSO on five datasets of varied sizes, with results averaging over ten runs. Experimental results show that MFRSO consistently delivered a higher Percentage of Test Suite Reduction (PTSR) while maintaining full requirement coverage, as opposed to RSO, which loses efficiency significantly with larger datasets. Furthermore, MFRSO reduced the optimization time compared to RSO, indicating its scalability and reliability. Future work will investigate adaptive knowledge transfer methods and apply MFRSO to dynamic test suite settings.
Keywords:
rat swarm optimization, multifactorial optimization, test suite reduction, transfer learning, multitaskingDownloads
References
L. Neves, O. Campos, R. Santos, C. Magalhaes, I. Santos, and R. D. S. Santos, "Elevating Software Quality in Agile Environments: The Role of Testing Professionals in Unit Testing," in 2024 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW), Toronto, Canada, May 2024, pp. 293–296. DOI: https://doi.org/10.1109/ICSTW60967.2024.00058
A. Alenzi, W. Alhumud, M. K. Khan, R. Michaels, and R. Bryce, "Events-Based Test Suite Reduction for Mobile App Test Suites Generated by Reinforcement Learning," in 2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE), Las Vegas, NV, USA, Jul. 2023, pp. 2650–2657.
A. S. Habib, S. U. R. Khan, and E. A. Felix, "A systematic review on search‐based test suite reduction: State‐of‐the‐art, taxonomy, and future directions," IET Software, vol. 17, no. 2, pp. 93–136, Apr. 2023. DOI: https://doi.org/10.1049/sfw2.12104
G. Dhiman, M. Garg, A. Nagar, V. Kumar, and M. Dehghani, "A novel algorithm for global optimization: Rat Swarm Optimizer," Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 8, pp. 8457–8482, Aug. 2021. DOI: https://doi.org/10.1007/s12652-020-02580-0
G. I. Sayed, "A Novel Multi-Objective Rat Swarm Optimizer-Based Convolutional Neural Networks for the Diagnosis of COVID-19 Disease," Automatic Control and Computer Sciences, vol. 56, no. 3, pp. 198–208, Jun. 2022. DOI: https://doi.org/10.3103/S0146411622030075
M. Ehteram, A. Seifi, and F. B. Banadkooki, "Rat Swarm Optimization Algorithm," in Application of Machine Learning Models in Agricultural and Meteorological Sciences, M. Ehteram, A. Seifi, and F. B. Banadkooki, Eds. Springer Nature, 2023, pp. 73–76. DOI: https://doi.org/10.1007/978-981-19-9733-4_9
K. K. Mohammed, S. Mekhilef, and S. Buyamin, "Improved Rat Swarm Optimizer Algorithm-Based MPPT Under Partially Shaded Conditions and Load Variation for PV Systems," IEEE Transactions on Sustainable Energy, vol. 14, no. 3, pp. 1385–1396, Jul. 2023. DOI: https://doi.org/10.1109/TSTE.2022.3233112
P. Manickam et al., "Empowering Cybersecurity Using Enhanced Rat Swarm Optimization With Deep Stack-Based Ensemble Learning Approach," IEEE Access, vol. 12, pp. 62492–62501, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3395328
M. Bhanumathi and B. Arthi, "Designing a Heuristic Based Hybrid CNN with Attention Mechanism for the Effective Classification of Fish Species," in 2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, Aug. 2023, pp. 981–988. DOI: https://doi.org/10.1109/ICIRCA57980.2023.10220648
A. Gupta, Y. S. Ong, and L. Feng, "Multifactorial Evolution: Toward Evolutionary Multitasking," IEEE Transactions on Evolutionary Computation, vol. 20, no. 3, pp. 343–357, Jun. 2016. DOI: https://doi.org/10.1109/TEVC.2015.2458037
A. Gupta, Y. S. Ong, L. Feng, and K. C. Tan, "Multiobjective Multifactorial Optimization in Evolutionary Multitasking," IEEE Transactions on Cybernetics, vol. 47, no. 7, pp. 1652–1665, Jul. 2017. DOI: https://doi.org/10.1109/TCYB.2016.2554622
Y. Li, W. Gong, and S. Li, "Multitasking optimization via an adaptive solver multitasking evolutionary framework," Information Sciences, vol. 630, pp. 688–712, Jun. 2023. DOI: https://doi.org/10.1016/j.ins.2022.10.099
L. Li, M. Xuan, Q. Lin, M. Jiang, Z. Ming, and K. C. Tan, "An Evolutionary Multitasking Algorithm With Multiple Filtering for High-Dimensional Feature Selection," IEEE Transactions on Evolutionary Computation, vol. 27, no. 4, pp. 802–816, Aug. 2023. DOI: https://doi.org/10.1109/TEVC.2023.3254155
C. Lyu, Y. Shi, and L. Sun, "A Novel Multi-Task Optimization Algorithm Based on the Brainstorming Process," IEEE Access, vol. 8, pp. 217134–217149, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.3042004
K. Z. Zamli, Md. A. Kader, and A. R. Mekeng, "A Population Division based Multi-Task Sine Cosine Algorithm for Test Redundancy Reduction Optimization," in Proceedings of the 2024 10th International Conference on Computer Technology Applications, Vienna Austria, May 2024, pp. 94–102. DOI: https://doi.org/10.1145/3674558.3674571
P. K. Mishra and A. K. Chaturvedi, "An Improved Laxity based Cost Efficient Task Scheduling Approach for Cloud-Fog Environment," Engineering, Technology & Applied Science Research, vol. 15, no. 1, pp. 19037–19044, Feb. 2025. DOI: https://doi.org/10.48084/etasr.8595
H. Jafarzadeh, N. Moradinasab, and M. Elyasi, "An Enhanced Genetic Algorithm for the Generalized Traveling Salesman Problem," Engineering, Technology & Applied Science Research, vol. 7, no. 6, pp. 2260–2265, Dec. 2017. DOI: https://doi.org/10.48084/etasr.1570
M. Shukla, Y. P. Kosta, and M. Jayswal, "A Modified Approach of OPTICS Algorithm for Data Streams," Engineering, Technology & Applied Science Research, vol. 7, no. 2, pp. 1478–1481, Apr. 2017. DOI: https://doi.org/10.48084/etasr.963
Y. Xu, D. Pi, S. Yang, and E. Zio, "Knowledge Transfer-Based Multifactorial Evolutionary Algorithm for Selective Maintenance Optimization of Multistate Complex Systems," IEEE Transactions on Reliability, vol. 73, no. 2, pp. 1341–1352, Jun. 2024. DOI: https://doi.org/10.1109/TR.2023.3324701
M. V. Merino and T. van der Storm, "cwi-swat/kogi: Kogi 0.1.0." Zenodo, Jun. 16, 2020.
B. Mayhew, "Guide to synthetic test data | TechTarget," TechTarget - Search Software Quality. https://www.techtarget.com/
searchsoftwarequality/tip/Guide-to-synthetic-test-data.
X. Y. Ma, Z. F. He, B. K. Sheng, and C. Q. Ye, "A Genetic Algorithm for Test-Suite Reduction," in 2005 IEEE International Conference on Systems, Man and Cybernetics, Waikoloa, HI, USA, 2005, vol. 1, pp. 133–139. DOI: https://doi.org/10.1109/ICSMC.2005.1571134
A. Deneke, B. Gizachew Assefa, and S. Kumar Mohapatra, "Test suite minimization using particle swarm optimization," Materials Today: Proceedings, vol. 60, pp. 229–233, 2022. DOI: https://doi.org/10.1016/j.matpr.2021.12.472
T. Y. Chen and M. F. Lau, "A new heuristic for test suite reduction," Information and Software Technology, vol. 40, no. 5, pp. 347–354, Jul. 1998. DOI: https://doi.org/10.1016/S0950-5849(98)00050-0
M. Bharathi and V. Sangeetha, "Weighted Rank Ant Colony Metaheuristics Optimization Based Test Suite Reduction in Combinatorial Testing for Improving Software Quality," in 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, Jun. 2018, pp. 525–534. DOI: https://doi.org/10.1109/ICCONS.2018.8663102
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Copyright (c) 2025 Haw Yuan Kang, Raja Rina Raja Ikram, Kamal Zuhairi Zamli, Nurul Akmar Binti Emran
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