Abstract:

Based on the relational principles of quantum computing and idea of differential evolution, a hybird optimization algorithm based on quantum and differential evolution for solving optimization problems in continuous space is proposed. The core of this algorithm is that, a real coded chromosome, whose alleles are composed of a component of the decision vector and a pair of probability amplitudes of the corresponding states of a qubit is constructed, and a complementary mutation operator, which is designed based on the specific configuration of chromosome, is adopted to evolve some excellent individuals selected to improve the convergence speed of the algorithm, and a differential evolution operator is used to update some individuals selected randomly to keep the global search capability and rubstness of the algorithm. Simulation results on benchmark functions show that the algorithm has the characteristics of more powerful optimizing ability, higher searching precision and better stability. Finally, the algorithm is applied to estimate the paremeters of the prediction model of roadbed settlement, and results show that the prediction model of roadbed settlement based on the algorithm can improve the fitting precision of the observed data efficiently.