摘要：
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。
关键词:
菌群觅食优化算法,
二进制编码,
量子进化算法,
量子旋转门,
量子菌群觅食优化算法
Abstract:
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.
Key words:
Bacterial Foraging Optimization(BFO) algorithm,
binary code,
quantum evolutionary algorithm,
quantum rotation gate,
Quantum Bacterial Foraging Optimization(QBFO) algorithm
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