Abstract
Particle swarm optimization (PSO) is a new robust swarm intelligence technique, which has exhibited good performance on well-known numerical test problems. Though many improvements published aims to increase the computational efficiency, there are still many works need to do. Inspired by evolution programming theory, this paper proposes a new adaptive particle swarm optimization in which the velocity threshold dynamically changes during the course of a simulation. Seven benchmark functions are used to testify the new algorithm, and the results showed clearly the new adaptive PSO leads to a significantly better performance, although the performance improvements were found to be dependent on problems.
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Cui, Z., Zeng, J., Sun, G. (2006). Adaptive Velocity Threshold Particle Swarm Optimization. In: Wang, GY., Peters, J.F., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2006. Lecture Notes in Computer Science(), vol 4062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11795131_47
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DOI: https://doi.org/10.1007/11795131_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36297-5
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