Abstract The basic principle of quantum-inspired immune clonal algorithm is analyzed and an improved strategy with adaptive function is proposed.Quantum observing entropy is introduced to evaluate the population evolutionary level,and relevant parameters are adjusted according to the entropy value. In the experiments, the proposed algorithm is used in function optimization and the optimization result is compared with other algorithms, such as QICA, SICA, QEA. The convergence of the proposed algorithm is proved theoretically. The experimental results indicate that the calculation efficiency and search capability are much improved by the proposed algorithm.
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2008, Vol. 21 
