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Simulated Annealing algorithm (SA) is a local search method invented to avoid local minima. The main shortcomings of this algorithm are its slowness and difficult tuning of its parameters. It is claimed that newer versions of fast SA (FSA) and very fast SA (VFSA) have resolved these problems, but these algorithms have some problems such as estimation of initial temperature and neighborhood generation. In this paper a modified form of VFSA is represented in which the standard deviation of the cost function is used for the estimation of initial temperature and also some modifications is done in neighborhood generation which made the algorithm more accurate. This algorithm is compared with some evolutionary-based algorithms and as simulation results show, it can optimize the complex problems with high accuracy in a feasible time.