Abstract
In order to apply grey relational analysis to the evolutionary process, a modified grey relational analysis is introduced in this study. Then, with the help of such a grey relational analysis, this study also proposed a grey-based particle swarm optimization algorithm in which both inertia weight and acceleration coefficients are varying over the generations. In each generation, every particle has its own algorithm parameters and those parameters may differ for different particles. The proposed PSO algorithm is applied to solve the optimization problems of twelve test functions for illustration. Simulation results are compared with the other three variants of PSO to demonstrate the search performance of the proposed algorithm.
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Yeh, MF., Wen, C., Leu, MS. (2012). Grey-Based Particle Swarm Optimization Algorithm. In: Tan, Y., Shi, Y., Ji, Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30976-2_7
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DOI: https://doi.org/10.1007/978-3-642-30976-2_7
Publisher Name: Springer, Berlin, Heidelberg
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