Solving Optimization Problems by a Hybrid Algorithm Based on Sand Cat Swarm Optimization and Invasive Weed Optimization Algorithm
2
Issued Date
2025-01-01
Resource Type
Scopus ID
2-s2.0-105004553027
Journal Title
10th International Conference on Digital Arts, Media and Technology, DAMT 2025 and 8th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, NCON 2025
Start Page
792
End Page
796
Rights Holder(s)
SCOPUS
Bibliographic Citation
10th International Conference on Digital Arts, Media and Technology, DAMT 2025 and 8th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, NCON 2025 (2025) , 792-796
Suggested Citation
Pravesjit S., Kantawong K., Jitkongchuen D., Thammano A., Longpradit P. Solving Optimization Problems by a Hybrid Algorithm Based on Sand Cat Swarm Optimization and Invasive Weed Optimization Algorithm. 10th International Conference on Digital Arts, Media and Technology, DAMT 2025 and 8th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, NCON 2025 (2025) , 792-796. 796. doi:10.1109/ECTIDAMTNCON64748.2025.10962032 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/110126
Title
Solving Optimization Problems by a Hybrid Algorithm Based on Sand Cat Swarm Optimization and Invasive Weed Optimization Algorithm
Corresponding Author(s)
Other Contributor(s)
Abstract
This paper addresses an optimization problem using hybrid algorithms of the Sand Cat Swarm Optimization (SCSO) and Invasive Weed Optimization (IWO). In this study, the reproduction step in the IWO algorithm was incorporated after the initial population step of the SCSO. The proposed algorithm was compared against the following: Intersection Mutation Differential Evolution (IMDE), Differential Evolution (DE), SCSO, and Whale Optimization Algorithm (WOA), whereby the performance was tested on six benchmark functions using a 10-fold cross validation. The results indicate that the proposed algorithm yielded the optimal solution for two out of the six benchmark functions. Additionally, when compared with the other four chosen algorithms, it yielded the best overall results. The findings suggest that the proposed algorithm is able to generate solutions similar to those obtained from the previous methods, essentially for the continuous step function, the multimodal function, and the discontinuous step function.