Optimization based on adaptive hinging hyperplanes and genetic algorithm

This paper describes an optimization strategy based on the model of adaptive hinging hyperplanes (AHH) and genetic algorithm (GA). The sample points of physical model are approximated by the AHH model, and the resulting model is minimized using a modified GA. In the modified GA, each chromosome corresponds to a local optimum. A criterion based on γ-valid cut is used to judge whether the global optimum is reached. Simulation results show that if the parameters are carefully chosen, the global optimum of AHH minimization is close to the optimum of the original function.