Optimization of filter parameters based on quantum genetic algorithm

The traditional design scheme of analog filters is imprecise and inefficient for complicated functional requirement. An optimization scheme of the analog filter design based on the quantum genetic algorithm (QGA) is proposed. Combined quantum theory with evolutionary theory, the QGA has better diversity than the classical genetic algorithm (CGA). Rapid convergence and good global search capacity characterize the performance of QGA. The optimization result can be obtained by the introducing of the QGA algorithm. With adopting an adaptive search grid adjustment strategy and quantum crossover, mutation, catastrophe operator, the efficiency of the proposed algorithm is enhanced and the possibility of prematurity is dropped effectively. The practical example shows the algorithm is effective and feasible.