Memetic simulated annealing for data approximation with local-support curves
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This paper introduces a new memetic optimization algorithm called MeSA (Memetic Simulated Annealing) to address the data fitting problem with local-support free-form curves. The proposed method hybridizes simulated annealing with the COBYLA local search optimization method. This approach is further combined with the centripetal parameterization and the Bayesian information criterion to compute all free variables of the curve reconstruction problem with B-splines. The performance of our approach is evaluated by its application to four different shapes with local deformations and different degrees of noise and density of data points. The MeSA method has also been compared to the non-memetic version of SA. Our results show that MeSA is able to reconstruct the underlying shape of data even in the presence of noise and low density point clouds. It also outperforms SA for all the examples in this paper.