Abstract
This paper presents a particle swarm optimization algorithm (PSO) to solve error-bounded polygonal approximation of digital curves. Different from the existing PSO-based methods for polygonal approximation problem, the mutation operators borrowed from genetic algorithms, are incorporated into the PSO, so we call it MPSO. This scheme can increase the diversity of the population and help the particles effectively escape from the local optimum. Experiments were performed on three commonly used benchmark curves to test the effectiveness of the proposed MPSO. The results manifest that the proposed MPSO has the higher performance than the existing GA-based methods and PSO methods.
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Wang, B., Shu, HZ., Li, BS., Niu, ZM. (2008). A Mutation-Particle Swarm Algorithm for Error-Bounded Polygonal Approximation of Digital Curves. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2008. Lecture Notes in Computer Science, vol 5226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87442-3_142
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DOI: https://doi.org/10.1007/978-3-540-87442-3_142
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