Automatic termination of parallel optimization runs of global stochastic optimization methods by upper limit criterion

Abstract

Many combinations of adjustable parameters should be tested in optimization experiments of biochemical networks to find the smallest subset of parameters enabling the best improvements of objective function both in case of design task and parameter estimation task. In case of optimization with global stochastic optimization methods one of the problems is the termination of the optimization run looking for a good compromise between spent computational resources and probability that the best found value of objective function will be the global optimum. Longer runs increase the possibility to each the global optimum. Automatic termination criteria in case of consensus or stagnation of parallel optimization runs have been proposed as criteria for automatic termination. Varying the consensus and delay time settings different probability of reaching global optimum and duration of optimization can be reached. It is proposed to modify automatic optimization termination criteria of parallel optimization runs applying upper limit agreement of a number of parallel optimization runs. Automatic application of upper limit agreement would reduce the duration of scanning of the whole space of combination of adjustable parameters. This approach is tested on the yeast glycolysis model with six adjustable parameters using COPASI, CoRunner and ConvAn software for five parallel optimization runs per combination of adjustable parameters.