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The proposed multi-objective optimization algorithm hybridizes random global search with a local refinement algorithm. The global search algorithm mimics the Bayesian multi-objective optimization algorithm. The site of current computation of the objective functions by the proposed algorithm is selected by randomized simulation of the bi-objective selection by the Bayesian-based algorithm. The advantage of the new algorithm is that it avoids the inner complexity of Bayesian algorithms. A version of the Hooke–Jeeves algorithm is adapted for the local refinement of the approximation of the Pareto front. The developed hybrid algorithm is tested under conditions previously applied to test other Bayesian algorithms so that performance could be compared. Other experiments were performed to assess the efficiency of the proposed algorithm under conditions where the previous versions of Bayesian algorithms were not appropriate because of the number of objectives and/or dimensionality of the decision space.

Biosensor response, in case of multi-substrate mixture, has nonlinear dependence on substrate concentrations. This work investigates the possibility to approximate this dependency with artificial neural network. Also the influence of external diffusion layer to results of multi-substrate determination was investigated. The numerically modelled biosensor response was used as experimental data. The principal components analysis was used to reduce the dimension of biosensor response. Prefered method gives acceptable acuratnes on multi-substrate determination and it can be improved by relatively large external diffusion layer.

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