It is well-known that the biogeography-based optimization (BBO) algorithm lacks searching power in some circumstances. In order to address this issue, an adaptive opposition-based biogeography-based optimization algorithm (AO-BBO) is proposed. Based on the BBO algorithm and opposite learning strategy, this algorithm chooses different opposite learning probabilities for each individual according to the habitat suitability index (HSI), so as to avoid elite individuals from returning to local optimal solution. Meanwhile, the proposed method is tested in 9 benchmark functions respectively. The results show that the improved AO-BBO algorithm can improve the population diversity better and enhance the search ability of the global optimal solution. The global exploration capability, convergence rate and convergence accuracy have been significantly improved. Eventually, the algorithm is applied to the parameter optimization of soft-sensing model in plant medicine extraction rate. The simulation results show that the model obtained by this method has higher prediction accuracy and generalization ability.
Keywords: AO-BBO algorithm, support vector machine (SVM), parameter optimization, soft-sensing model, plant medicine extraction, extraction rate
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