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Solving large-scale problems using multi-swarm particle swarm approach by Salih, Sinan Q., Alsewari, Abdulrahman A.

“…The proposed approach strived to scale up the application of the (PSO) algorithm towards solving large-scale optimization tasks of up to 1000 real-valued variables. …”
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Prediction of rainfall trends using Mahalanobis-Taguchi system by Muhammad Arieffuddin, Mohd Jamil, Mohd Yazid, Abu, Sri Nur Areena, Mohd Zaini, Nurul Haziyani, Aris, Nur Syafikah, Pinueh, Nur Najmiyah, Jaafar, Wan Zuki Azman, Wan Muhammad, Faizir, Ramlie, Nolia, Harudin, Emelia Sari, ., Nadiatul Adilah, Ahmad Abdul Ghani

“…The results showed that the Mahalanobis-Taguchi Bee Algorithm (MTBA) is more effective than the Mahalanobis-Taguchi System (MTS) in finding the significant parameters, but the parameters were a subset of MTS Teshima. …”
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Prediction of Rainfall Trends using Mahalanobis-Taguchi System by Jamil M.A.M., Abu M.Y., Zaini S.N.A.M., Aris N.H., Pinueh N.S., Jaafar N.N., Muhammad W.Z.A.W., Ramlie F., Harudin N., Sari E., Ghani N.A.A.A.

“…The results showed that the Mahalanobis-Taguchi Bee Algorithm (MTBA) is more effective than the Mahalanobis-Taguchi System (MTS) in finding the significant parameters, but the parameters were a subset of MTS Teshima. …”