Acceso abierto

Research on Improved Particle Swarm Computational Intelligence Algorithm and Its Application to Multi-Objective Optimisation


Cite

Ammar, H. B., Yahia, W. B., Ayadi, O., & Masmoudi, F. (2021). Design of efficient multiobjective binary pso algorithms for solving multi-item capacitated lot-sizing problem. International Journal of Intelligent Systems. Search in Google Scholar

Gou, J., Guo, W. P., Wang, C., & Luo, W. (2017). A multi-strategy improved particle swarm optimization algorithm and its application to identifying uncorrelated multi-source load in the frequency domain. Neural Computing and Applications. Search in Google Scholar

Li, H., Wang, S., Chen, Q., Gong, M., & Chen, L. (2022). Ipsmt: multi-objective optimization of multipath transmission strategy based on improved immune particle swarm algorithm in wireless sensor networks. Applied Soft Computing(121-), 121. Search in Google Scholar

Gu, Q., Liu, Y., Chen, L., & Xiong, N. (2022). An improved competitive particle swarm optimization for many-objective optimization problems. Expert Systems with Applications, 189, 116118-. Search in Google Scholar

Ruochen, Liu, Jianxia, Li, Jing, & fan, et al. (2017). A coevolutionary technique based on multi-swarm particle swarm optimization for dynamic multi-objective optimization. European Journal of Operational Research. Search in Google Scholar

Yu, H., Gao, Y., & Wang, J. (2020). A multiobjective particle swarm optimization algorithm based on competition mechanism and gaussian variation. Complexity, 2020. Search in Google Scholar

Li, Y., Yao, X., & Liu, M. (2020). Multiobjective optimization of cloud manufacturing service composition with improved particle swarm optimization algorithm. Mathematical Problems in Engineering, 2020, 1-17. Search in Google Scholar

Xia, Y., Wu, P., Wu, T., & Chu, D. (2019). Application of improved multi-objective particle swarm optimization algorithm in discrete combinatorial optimization. Basic & clinical pharmacology & toxicology.(S2), 125. Search in Google Scholar

Xie, Y., Qiao, J., Wang, D., & Yin, B. (2020). A novel decomposition-based multiobjective evolutionary algorithm using improved multiple adaptive dynamic selection strategies. Information Sciences(5). Search in Google Scholar

Deng, W., Zhao, H., Yang, X., Xiong, J., Sun, M., & Li, B. (2017). Study on an improved adaptive pso algorithm for solving multi-objective gate assignment. Applied Soft Computing, S1568494617303472. Search in Google Scholar

Kunlun, L. I., & Jun, W. (2017). Multi-objective optimization for cloud task scheduling based on the anp model. Chinese Journal of Electronics(05), 889-898. Search in Google Scholar

Ying, Senliang, Wang, Zheng, Li, & Wanliang, et al. (2017). An improved decomposition-based multiobjective evolutionary algorithm with a better balance of convergence and diversity. Applied Soft Computing. Search in Google Scholar

Hinojosa, S., Oliva, D., Cuevas, E., Pajares, G., Avalos, O., & Gálvez, Jorge. (2017). Improving multi-criterion optimization with chaos: a novel multi-objective chaotic crow search algorithm. Neural Computing and Applications. Search in Google Scholar

Masoumi, J. N. A. S. (2021). An improved ant colony optimization-based algorithm for user-centric multi-objective path planning for ubiquitous environments. Geocarto international, 36(1a4). Search in Google Scholar

Chen, C. W. S. (2018). Multi-objective distribution routing optimization with time window based on improved genetic algorithm. Latin American Applied Research, 48(3). Search in Google Scholar

Sulaiman, M., Samiullah, I., Hamdi, A., & Hussain, Z. (2019). An improved whale optimization algorithm for solving multi-objective design optimization problem of pfhe. Journal of Intelligent & Fuzzy Systems, 37(3), 3815-3828. Search in Google Scholar

Zhang, C., Fu, W., Peng, T., Xia, X., Xue, X., & Li, C. (2019). Multiobjective optimization of a fractional-order pid controller for pumped turbine governing system using an improved nsga-iii algorithm under multiworking conditions. Complexity, 2019. Search in Google Scholar

Boufssasse, A., Hssayni, E. H., Joudar, N. E., & Ettaouil, M. (2023). A multi-objective optimization model for redundancy reduction in convolutional neural networks. Neural Processing Letters. Search in Google Scholar

Duan, X., Niu, T., & Huang, Q. (2018). Retracted: an improved spea2 algorithm with adaptive selection of evolutionary operators scheme for multiobjective optimization problems. Mathematical Problems in Engineering, 2018(PT.6), 1492768.1-1492768.1. Search in Google Scholar

Wang, R., Zhang, D., Kang, Z., Zhou, R., & Hui, G. (2023). Study on deep reinforcement learning-based multi-objective path planning algorithm for inter-well connected-channels. Applied Soft Computing, 147. Search in Google Scholar

Yi, X., Yu, H., & Xu, T. (2024). Solving multi-objective weapon-target assignment considering reliability by improved moea/d-am2m. Neurocomputing(Jan.1), 563. Search in Google Scholar

eISSN:
2444-8656
Idioma:
Inglés
Calendario de la edición:
Volume Open
Temas de la revista:
Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics