Abstract
To increase the convergence speed and prevent the prematurity of the particle swarm optimizer (PSO), a novel strategy for inertia weight was proposed, which was different from the traditional linearly decreasing weight (LDW). The inertia weight was dynamically updated by two factors (the dispersion degree and advance degree factors) which have significant impact on the evolutionary state of the PSO. Comparison studies were done for three PSOs (the proposed algorithm and other two improved methods). The experimental results for eight test functions demonstrated good performance of the proposed method in both the optimization speed and computational accuracy.
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Miao, Am., Shi, Xl., Zhang, Jh., Wang, Ey., Peng, Sq. (2009). A Modified Particle Swarm Optimizer with Dynamical Inertia Weight. In: Cao, B., Li, TF., Zhang, CY. (eds) Fuzzy Information and Engineering Volume 2. Advances in Intelligent and Soft Computing, vol 62. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03664-4_84
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DOI: https://doi.org/10.1007/978-3-642-03664-4_84
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