This paper is devoted to construct an algorithm based on morphological filters, for global optimization of functions in big dimensions. A panel of various benchmark problems with different properties were used to assess the performance of the proposed Optimization morphological filters (OMF) algorithm. The obtained results has shown the scalability of the algorithm in contrast to optimization algorithms encountered in the literature. Moreover, in comparison with some metaheuristics (PSO, CuCkoo, Bat, QPSO...), the computational results revealed that the proposed algorithm is an effective and efficient optimization algorithm.

Today, insurers may use the yield curve as an indicator evaluation of the profit or the performance of their portfolios; therefore, they modeled it by one class of model that has the ability to fit and forecast the future term structure of interest rates. This class of model is the Nelson-Siegel-Svensson model. Unfortunately, many authors have reported a lot of difficulties when they want to calibrate the model because the optimization problem is not convex and has multiple local optima. In this context, we implement a hybrid Particle Swarm optimization and Nelder Mead algorithm in order to minimize by least squares method, the difference between the zero-coupon curve and the NSS curve. Keywords—Optimization, zero-coupon curve, Nelson-SiegelSvensson, Particle Swarm Optimization, Nelder-Mead Algorithm.