Abstract
In this paper, a new mechanism of velocity update in multi-objective particle swarm optimization (VM-MOPSO) is proposed. The main goal of the method is to balance local exploration and global exploration of multi-objective particle swarm optimization. In VM-MOPSO, as the number of iterations increases, the learning strength of global optimal solutions is adjusted by a linear decreasing search mechanism, which can make the swarm hold a stronger global search ability in the initial stage of the iteration and better local search ability in the later stage of the iteration. Experimental results in benchmark functions present that our method is better in convergence and diversity by comparison with MOPSO.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (Grants nos. 71461027, 71471158); Qian KeHE (NY Zi [2016]3013, LH Zi [2015]7033, J Zi LKZS[2014]06); Guizhou province natural science foundation in China (Qian Jiao He KY [2014]295); Zhunyi innovative talent team (Zunyi KH(2015)38); Science and technology talent training object of Guizhou province outstanding youth (Qian ke he ren zi [2015]06); Guizhou science and technology cooperation plan (Qian Ke He LH zi [2016]7028); Project of teaching quality and teaching reform of higher education in Guizhou province (Qian Jiao gaofa[2015]337) and 2016; 2013, 2014 and 2015 Zunyi 15851 talents elite project funding; Innovative talent team in Guizhou Province (Qian Ke HE Pingtai Rencai[2016]5619); College students’ innovative entrepreneurial training plan (201510664016).
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Liu, Y., Yuan, L., Ouyang, A., Ye, H., Leng, R., Huang, T. (2019). A New Multi-objective Particle Swarm Optimization Based on Linear Decreasing Velocity Update Mechanism. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11643. Springer, Cham. https://doi.org/10.1007/978-3-030-26763-6_60
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DOI: https://doi.org/10.1007/978-3-030-26763-6_60
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