Quantum adaptive immune clone algorithm based on cloud model

Based on a cloud model, which makes good balance between randomness and fuzziness, a quantum adaptive immune clone algorithm is proposed. The quantum rotate gate, a key operator of quantum evolutionary algorithm for optimization and mutation operation, is insteaded by cloud operators. Dynamic adjustment of searching range in evolutionary process is realized by controlling collaboration of cloud operators. It makes the algorithm has strong ability in global search. Programs are modified for targeted optimization. They make algorithm avoid falling into local optimum effectively. Simulation experiments for standard numerical optimization problems show that the proposed algorithm has better search capability, higher search precision, and better stability. Simulation experiment in parameters estimation of nonlinear system shows that the algorithm gets effectively estimation for system parameters with high accuracy and good performance.