Yu, S., Wang, Y., Li, J., Fang, X., Chen, J., Zheng, Z., and Fu, C.
IEEE Transactions on Computational Social Systems IEEE Trans. Comput. Soc. Syst. Computational Social Systems, IEEE Transactions on. 10(2):448-457 Apr, 2023
Kalita, Apurba Bikash, Rajbongshi, Subhash Chandra, and Trivedi, Gaurav
2023 10th International Conference on Signal Processing and Integrated Networks (SPIN) Signal Processing and Integrated Networks (SPIN), 2023 10th International Conference on. :7-13 Mar, 2023
2023 10th International Conference on Computing for Sustainable Global Development (INDIACom) Computing for Sustainable Global Development (INDIACom), 2023 10th International Conference on. :745-750 Mar, 2023
Prem, Ajay, Joshi, Anirudh, Madana, Haritha, J, Jaywanth, and Arya, Arti
2023 15th International Conference on Computer and Automation Engineering (ICCAE) Computer and Automation Engineering (ICCAE), 2023 15th International Conference on. :237-243 Mar, 2023
Rathod, V. P., Kadam, S. P., Yadav, O. P., and Rathore, A.P. S.
2023 2nd International Conference for Innovation in Technology (INOCON) Innovation in Technology (INOCON), 2023 2nd International Conference for. :1-4 Mar, 2023
2023 Second International Conference on Electronics and Renewable Systems (ICEARS) Electronics and Renewable Systems (ICEARS), 2023 Second International Conference on. :1527-1531 Mar, 2023
Ponnaganti, Krishna Priyanka, V.P. Chandra Sekhara Rao, M., Pinninti, Lekhana, Peddi, Anudeep, Chereddy, Sai Vignesh, and Lakshmikanth, Paleti
2023 Second International Conference on Electronics and Renewable Systems (ICEARS) Electronics and Renewable Systems (ICEARS), 2023 Second International Conference on. :1658-1663 Mar, 2023
Distributed evolutionary computation has been efficiently used, in last decades, to solve complex optimization problems. Island model (IM) is considered as a distributed population paradigm employed by evolutionary algorithms to preserve the diversification and, thus, to improve the local search. In this article, we study different island model techniques integrated in to particle swarm optimization (PSO) algorithm in order to overcome its drawbacks: premature convergence and lack of diversity. The first IMPSO approach consists in using the migration process in a static way to enhance the police migration strategy. On the other hand, the second approach, called dynamic-IMPSO, consists in integrating a learning strategy in case of migration. The last version called constrained-IMPSO utilizes a stochastic technique to ensure good communication between the sub-swarms. To evaluate and verify the effectiveness of the proposed algorithms, several standard constrained and unconstrained benchmark functions are used. The obtained results confirm that these algorithms are more efficient in solving low-dimensional problems (CEC'05), large-scale optimization problems (CEC'13) and constrained problems (CEC'06), compared to other well-known evolutionary algorithms. [ABSTRACT FROM AUTHOR]
IEEE Transactions on Intelligent Transportation Systems IEEE Trans. Intell. Transport. Syst. Intelligent Transportation Systems, IEEE Transactions on. 24(5):5282-5295 May, 2023
IEEE Transactions on Pattern Analysis and Machine Intelligence IEEE Trans. Pattern Anal. Mach. Intell. Pattern Analysis and Machine Intelligence, IEEE Transactions on. 45(5):6231-6246 May, 2023