Multi-guide particle swarm optimisation archive management strategies for dynamic optimisation problems
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s11721-022-00210-3.pdf
Reference32 articles.
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3. Cámara, M., Lopera, J. O., & de Toro, F. (2010). Approaching dynamic multi-objective optimization problems by using parallel evolutionary algorithms. In Advances in multi-objective nature inspired computing, studies in computational intelligence (Vol. 272, pp. 63–86). Springer. https://doi.org/10.1007/978-3-642-11218-8_4
4. Cámara, M., Ortega, J., & de Toro, F. (2007). Parallel processing for multi-objective optimization in dynamic environments. In Proceedings of the IEEE international parallel and distributed processing symposium (pp. 1–8). https://doi.org/10.1109/IPDPS.2007.370433
5. Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197. https://doi.org/10.1109/4235.996017.
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