a mixed algorithm for smooth global optimization

This paper presents a covering algorithm for solving bound-constrained global minimization problems with a differentiable cost function. in the proposed algorithm, we suggest to explore the feasible domain using a one-dimensional global search algorithm through a number of parametric curves that are relatively spread and simultaneously scan the search space. to accelerate the corresponding algorithm, we incorporate a multivariate quasi-newton local search algorithm to spot the lowest regions.  the proposed algorithm converges in a finite number of iterations to an $varepsilon$-approximation of the global minimum. the performance of the algorithm is demonstrated through numerical experiments on some typical test functions.