CAJ | 학술논문

菌群觅食优化算法具有算法简单、鲁棒性强和具备全局搜索能力的特点。但该算法收敛速度慢，对于多峰函数容易陷入局部最优。为提高菌群优化算法的搜索能力，避免其陷入早熟收敛，提出一种量子菌群算法，将二进制编码的量子进化算法融合到菌群算法中，用量子染色体表示细菌，用量子旋转门实现细菌状态更新。通过标准测试函数对其优化性能进行研究，实验结果表明，该算法无论是对于普通函数还是多峰函数，在收敛速度、收敛稳定性和寻找全局最优方面均优于菌群算法和量子遗传算法。
균군멱식우화산법구유산법간단、로봉성강화구비전국수색능력적특점。단해산법수렴속도만，대우다봉함수용역함입국부최우。위제고균군우화산법적수색능력，피면기함입조숙수렴，제출일충양자균군산법，장이진제편마적양자진화산법융합도균군산법중，용양자염색체표시세균，용양자선전문실현세균상태경신。통과표준측시함수대기우화성능진행연구，실험결과표명，해산법무론시대우보통함수환시다봉함수，재수렴속도、수렴은정성화심조전국최우방면균우우균군산법화양자유전산법。
Bacterial Foraging Optimization(BFO) algorithm is simple, robust and has global search capability. However, the speed of BFO is slow and it often seems to fall into local optimum. To improve the search capabilities of BFO and avoid its premature convergence, a new type of Quantum Bacterial Foraging Optimization(QBFO) algorithm is proposed by integrating binary code quantum evolutionary algorithm into BFO. Quantum triploid chromosome is used to represent bacteria, and Quantum Rotation Gate(QRG) is used to update bacteria’s state. To test the new algorithm’s optimization performance, a research based on benchmark functions is conducted. The results indicate that the new type of QBFO shows better results than BFO and quantum genetic algorithm in convergence rate, stability and looking for the global optimal solutions weather common function or multi-peak function.