In this work, we solve parameter estimation problems from Biosciences and Chemical Engineering. These problems are highly multimodal, requiring optimization techniques that overcome local optima. In order to do so, we use a clustering optimization technique based on the topographical information on the objective function called Topographical Algorithm (TA). This algorithm consists of three steps: a uniform random sampling of solutions in the search space, the construction of the topograph, and the application of a local optimization algorithm using the topograph minima as starting points. In this work, we use the Sobol quasi-random sequence to perform the first step and the Hooke–Jeeves and Rosenbrock direct search methods for the third step. Both variants of TA are compared against other methods in the literature, and the results obtained are very effective.
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