Abstract
As an efficient and simple optimization algorithm, particle swarm optimization (PSO) has been widely applied to solve various real optimization problems in expert systems. However, avoiding premature convergence and balancing the global exploration and local exploitation capabilities of the PSO remains an open issue. To overcome these drawbacks and strengthen the ability of PSO in solving complex optimization problems, a modified PSO using adaptive strategy called MPSO is proposed, although MPSO has achieved excellent performance, and its convergence and stability are still some defects. In this paper, we presented a new variant of MPSO algorithm which can explore the search space deeper than the previous method, and better performance can be achieved under CEC2013 test suite.
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Zhong, Y., Chen, Y., Yang, C., Meng, Z. (2022). An Operation with Crossover and Mutation of MPSO Algorithm. In: Wu, TY., Ni, S., Chu, SC., Chen, CH., Favorskaya, M. (eds) Advances in Smart Vehicular Technology, Transportation, Communication and Applications. Smart Innovation, Systems and Technologies, vol 250. Springer, Singapore. https://doi.org/10.1007/978-981-16-4039-1_26
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DOI: https://doi.org/10.1007/978-981-16-4039-1_26
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