To improve search ability and optimization efficiency and avoid premature convergence for artificial fish school algorithm (AFSA), a novel quantum AFSA for continuous space optimization was proposed. Artificial fishes were encoded by the phase of quantum bit,evolved by quantum rotation gates, and mutated by quantum Pauli-Z gate. The simulation results showed that the approach is superior to either common AFSA or simple genetic algorithm in both search capability and optimization efficiency.%为提高人工鱼群算法的搜索能力和优化效率并避免早熟收敛,将量子进化算法融合到人工鱼群算法中,提出一种求解连续空间的新的量子人工鱼群优化算法.该方法直接采用量子位的相位对人工鱼进行编码,采用人工鱼的进化方程实现人工鱼群上相位的更新,采用Pauli-Z门实现人工鱼的变异.仿真结果表明,该方法的搜索能力和优化效率明显优于基本人工鱼群算法.
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