Abstract
In this paper, we propose a modification to particle swarm optimization in order to speed up the optimization process. The modification is applied to the constriction coefficient, an important parameter that controls the convergence rate. To validate the proposed strategy, we carried out a number of experiments on a wide range of 25 standard test problems. The obtained results show that the proposed strategy significantly improves the performance of the selected PSO algorithm.
Keywords
- Particle Swarm Optimization
- Particle Swarm
- Test Problem
- Particle Swarm Optimization Algorithm
- Inertia Weight
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Bui, L.T., Soliman, O., Abbass, H.A. (2007). A Modified Strategy for the Constriction Factor in Particle Swarm Optimization. In: Randall, M., Abbass, H.A., Wiles, J. (eds) Progress in Artificial Life. ACAL 2007. Lecture Notes in Computer Science(), vol 4828. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76931-6_29
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DOI: https://doi.org/10.1007/978-3-540-76931-6_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76930-9
Online ISBN: 978-3-540-76931-6
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