Abstract
A novel stochastic search method (NSSM) is proposed for the polygonal approximation problem. NSSM incorporates the ranking selection scheme, which is initially developed for solving the premature convergence of genetic algorithms (GAs), into the traditional split-and-merge technique. For avoiding getting trapped in a local optimum, NSSM randomly selects the splitting points and the merging points and determines the selection probability using the ranking selection scheme. Three groups of digital curves, including the synthesized benchmark curves and the real image curves, are used to test the performance of NSSM. The experimental results show the higher performance over the other methods including the GA-based methods and the local search methods.
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