Taguchi-Grey Relational Analysis Method for Parameter Tuning of Multi-objective Pareto Ant Colony System Algorithm

Authors

  • Shatha Abdulhadi Muthana General Company of Electricity Production South Region, Ministry of Electricity, Iraq
  • Ku Ruhana Ku-Mahamud School of Computing, Universiti Utara Malaysia, Malaysia and Shibaura Institute of Technology, Tokyo, Malaysia

DOI:

https://doi.org/10.32890/jict2023.22.2.1

Keywords:

Optimization, Scheduling, Taguchi method, Grey Relational Analysis, Generator maintenance

Abstract

In any metaheuristic, the parameter values strongly affect the efficiency of an algorithm’s search. This research aims to find the optimal parameter values for the Pareto Ant Colony System (PACS) algorithm, which is used to obtain solutions for the generator maintenance scheduling problem. For optimal maintenance scheduling with low cost, high reliability, and low violation, the parameter values of the PACS algorithm were tuned using the Taguchi and Grey Relational Analysis (Taguchi-GRA) method through search-based approach. The new parameter values were tested on two systems. i.e., 26- and 36-unit systems for window with operational hours [3000-5000]. The grey relational grade (GRG) performance metric and the Friedman test were used to evaluate the algorithm’s performance. The Taguchi-GRA method that produced the new values for the algorithm’s parameters was shown to be able to provide a better multi-objective generator maintenance scheduling (GMS) solution. These values can be benchmarked in solving multi-objective GMS problems using the multi-objective PACS algorithm and its variants.

References

Ankita & Sahana, S. K. (2019). An automated parameter tuning method for ant colony optimization for scheduling jobs in grid environment. International Journal of Intelligent Systems and Applications, 11(3), 11–21. https://doi.org/10.5815/ ijisa.2019.03.02

Ariyaratne, M. K. A., & Fernando, T. G. I. (2018). A self-tuning firefly algorithm to tune the parameters of ant colony system. International Journal of Swarm Intelligence, 3(4), 309–331. https://doi.org/10.1080/09692290.2013.878741

Berrichi, A., Yalaoui, F., Amodeo, L., & Mezghiche, M. (2010). Bi-objective ant colony optimization approach to optimize production and maintenance scheduling. Computers and Operations Research, 37(9), 1584–1596. https://doi.org/10.1016/j.cor.2009.11.017

Chagas, J. B. C., & Wagner, M. (2020). Ants can orienteer a thief in their robbery. Operations Research Letters, 48(6), 708–714. https://doi.org/10.1016/j.orl.2020.08.011

Chagas, J. B. C., & Wagner, M. (2022). Efficiently solving the thief orienteering problem with a max–min ant colony optimization approach. Optimization Letters, 16(8), 2313–2331. https://doi.org/10.1007/s11590-021-01824-y

Drozdik, M., Aguirre, H., Akimoto, Y., & Tanaka, K. (2015, January). Comparison of parameter control mechanisms in multi-objective differential evolution. In International Conference on Learning and Intelligent Optimization (LION 2015) (pp. 89–103). https://doi.org/10.1007/978-3-319-19084-6_8

Fattahi, M., Mahootchi, M., Mosadegh, H., & Fallahi, F. (2014). A new approach for maintenance scheduling of generating units in electrical power systems based on their operational hours. Computers and Operations Research, 50(Feb 1), 61–79. https://doi.org/10.1016/j.cor.2014.04.004

Förster, M., Bickel, B., Hardung, B., & Gabriella, K. (2007, July). Self-adaptive ant colony optimisation applied to function allocation in vehicle networks. In Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation (pp. 1991–1998). https://dl.acm.org/doi/pdf/10.1145/127695 8.1277352?casa_token=SORrdgdp36IAAAAA:IsoPtCQUP A865C4pnTx5GI3UoXTnJNzwVxQIn0afFcR8Uv-mFV9_ SEwa5PLUyDEopBUF6sQdKXCcww Journal of ICT, 22, No. 2 (April) 2023, pp: 149–

Han, Z., Wang, Y., & Tian, D. (2021). Ant colony optimization for assembly sequence planning based on parameters optimization. Frontiers of Mechanical Engineering, 16, 393–409. https://doi.org/10.1016/j.asoc.2021.107439

Jozić, S., Bajić, D., & Celent, L. (2015). Application of compressed cold air cooling: Achieving multiple performance characteristics in end milling process. Journal of Cleaner Production, 100(Aug 1), 325–332. https://doi.org/10.1016/j.jclepro.2015.03.095

Kolahan, F., & Azadi Moghaddam, M. (2015). The use of Taguchi method with grey relational analysis to optimize the EDM process parameters with multiple quality characteristics. Scientia Iranica, 22(2), 530–538. http://scientiairanica.sharif.edu/article_3665_0e7b4362f86839 d882e60aa681d5ce56.pdf

Lezama, F., Faia, R., Soares, J., Faria, P., & Vale, Z. (2020, July). Learning bidding strategies in local electricity markets using ant colony optimization. In IEEE Congress on Evolutionary Computation (CEC) (pp. 1–8).

López-Ibáñez, M., Stützle, T., & Dorigo, M. (2015). Ant colony optimization: A component-wise overview. In Handbook of heuristics (pp. 371-407). Springer, Cham

Lyu, Z., Wang, Z., Duan, D., Lin, L., Li, J., Yang, Y., Chen, Y., & Li, Y. (2020). Tilting path optimization of tilt quad rotor in conversion process based on ant colony optimization algorithm. IEEE Access, 8(July), 140777–140791. https://doi.org/10.1109/ ACCESS.2020.3012672

Malisia, A. R. (2008). Improving the exploration ability of ant-based algorithms. In Oppositional Concepts in Computational Intelligence (pp. 121–142). https://link.springer.com/content/ pdf/10.1007/978-3-540-70829-2.pdf

Manikandan, N., Kumanan, S., & Sathiyanarayanan, C. (2015). Multi response optimization of electrochemical drilling of titanium Ti6Al4V alloy using Taguchi based grey relational analysis. Indian Journal of Engineering and Materials Sciences, 22(April), 153–160.

Mavrovouniotis, M., Ioannou, A., & Yang, S. (2017, March). Pre-scheduled colony size variation in dynamic environments. In European Conference on the Applications of Evolutionary Computation (pp. 128–139). https://doi.org/10.1007/978-3-319-55792-2_9 Journal of ICT, 22, No. 2 (April) 2023, pp: 149–

Moncayo-Martínez, L. A., & Zhang, D. Z. (2011). Multi-objective ant colony optimisation: A meta-heuristic approach to supply chain design. International Journal of Production Economics, 131(1), 407–420. https://doi.org/10.1016/j.ijpe.2010.11.026

Muthana, S. A., & Ku-Mahamud, K. R. (2022). Tri-objective generator maintenance scheduling model based on sequential strategy. PLOS ONE, 17(10), 1–29. https://doi.org/10.1371/ journal.pone.0276225

Muthana, S. A., & Ku-Mahamud, K. R. (2021). Generator Maintenance scheduling models for electrical power systems: A review. International Journal of Electrical and Electronic Engineering & Telecommunications, 10(5), 307–318. http://www.ijeetc. com/uploadfile/2021/0408/20210408102935567.pdf

Muthana, S. A., & Ku-Mahamud, K. R. (2022). Comparison of multi-objective optimization methods for generator maintenance scheduling. International Journal of Intelligent Engineering and Systems, 15(4), 599–614. https://doi.org/10.22266/ ijies2022.0831.54

Negulescu, A. E. (2017). Normalization of ACO algorithm parameters. University Politehnica Bucharest, 79(2), 71–82.

Ragmani, A., Elomri, A., Abghour, N., Moussaid, K., & Rida, M. (2019). An improved hybrid fuzzy-ant colony algorithm applied to load balancing in cloud computing environment. Procedia Computer Science, 151(2019), 519–526. https://doi.org/10.1016/j.procs.2019.04.070

Ragmani, A., Elomri, A., Abghour, N., Moussaid, K., & Rida, M. (2020). FACO: A hybrid fuzzy ant colony optimization algorithm for virtual machine scheduling in high-performance cloud computing. Journal of Ambient Intelligence and Humanized Computing, 11(10), 3975–3987. https://doi.org/10.1007/s12652-019-01631-5

Sagban, R. (2016). Reactive approach for automating exploration and exploitation in ant colony optimization [Doctoral dissertation, Universiti Utara Malaysia]. https://www.researchgate. net/publication/303446764_Reactive_Approach_for_ Automating_Exploration_and_Exploitation_in_Ant_Colony_ Optimization

Sihem, B., & Benmansour, A. (2018). Ant colony parameters of electric power system reliability by Taguchi method. EEA - Electrotehnica, Electronica, Automatica, 66(2), 57–60.https://www.researchgate.net/profile/Sihem-Bouri/ Journal of ICT, 22, No. 2 (April) 2023, pp: 149– publication/340383187_Ant_colony_parameters_of_ electric_power_system_reliability_by_Taguchi_method/ links/5e85db33a6fdcca789e965c4/Ant-colony-parameters-o f - e l e c t r i c - p o w e r - s y s t e m - r e l i a b i l i t y - b y - Ta g u c h i - method.pdf?_sg%5B0%5D=started_experiment_ milestone&origin=journalDetail

Talbi, E.-G. (2009). Metaheuristics: From design to implementation. John Wiley & Sons. https://scholar.google.com.my/scholar?q =Metaheuristics:+From+design+to+implementation&hl=en& as_sdt=0&as_vis=1&oi=scholart

Tirkolaee, E. B., Alinaghian, M., Hosseinabadi, A. A. R., Sasi, M. B., & Sangaiah, A. K. (2019). An improved ant colony optimization for the multi-trip capacitated Arc routing problem. Computers and Electrical Engineering, 77(Jul 1), 457–470. https://doi.org/10.1016/j.compeleceng.2018.01.040

Vinay, V. P., & Sridharan, R. (2013). Taguchi method for parameter design in ACO algorithm for distribution-allocation in a two-stage supply chain. International Journal of Advanced Manufacturing Technology, 64(Feb 1), 1333–1343. https://doi.org/10.1007/s00170-012-4104-5

Wang, G., Chu, H. C. E., Zhang, Y., Chen, H., Hu, W., Li, Y., & Peng, X. J. (2015). Multiple parameter control for ant colony optimization applied to feature selection problem. Neural Computing and Applications, 26(7), 1693–1708. https://doi.org/10.1007/s00521-015-1829-8

Wang, Y., & Han, Z. (2021). Ant colony optimization for traveling salesman problem based on parameters optimization. Applied Soft Computing, 107(Aug), 107439. https://doi.org/10.1016/j. asoc.2021.107439

Yasear, S. A., & Ku-Mahamud, K. R. (2021). Review of multi-objective swarm intelligence optimization algorithms. Journal of Information and Communication Technology, 20(2), 171–211. https://doi.org/10.32890/jict2021.20.2.3

Yuan-Kang, W., Chih-Cheng, H., & Chun-Liang, L. (2013). Resolution of the unit commitment problems by using the hybrid Taguchi-ant colony system algorithm. International Journal of Electrical Power and Energy Systems, 49(Jul 1), 188–198. https://doi.org/10.1016/j.ijepes.2013.01.007

Zheng, F., Zecchin, A. C., Newman, J. P., Maier, H. R., & Dandy, G. C. (2017). An adaptive convergence-trajectory controlled ant colony optimization algorithm with application to water Journal of ICT, 22, No. 2 (April) 2023, pp: 149– distribution system design problems. IEEE Transactions on Evolutionary Computation, 21(5), 773–791. https://doi.org/10.1109/TEVC.2017.2682899

Zhou, R., Lee, H. P., & Nee, A. Y. C. (2008). Applying ant colony optimisation (ACO) algorithm to dynamic job shop scheduling problems. International Journal of Manufacturing Research, 3(3), 301. https://doi.org/10.1504/IJMR.2008.019212

Downloads

Published

03-04-2023

Issue

Vol. 22 No. 2 (2023): Journal of Information and Communication Technology (JICT) Vol.22, No.2, April 2023

Section

Articles

License

Copyright (c) 2023 Journal of Information and Communication Technology

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Muthana, S. A., & Ku-Mahamud, K. R. (2023). Taguchi-Grey Relational Analysis Method for Parameter Tuning of Multi-objective Pareto Ant Colony System Algorithm. Journal of Information and Communication Technology, 22(2), 149-181. https://doi.org/10.32890/jict2023.22.2.1

Research impact

Harvested 2026-09-05
8 citations, from Scopus — the highest of the sources checked

Counts differ between services because each indexes a different body of literature. None of them is the whole picture.

Identifiers DOI 10.32890/jict2023.22.2.1 OpenAlex W4362575475 Semantic Scholar CorpusID 257974602 Scopus 85152397005

Most read articles by the same author(s)

1 2 > >>