Abstract: Based on the theory of quantum evolution and ant colony optimization strategy, combined with a binary quantum ant colony algorithm, this paper proposes a novel quantum ant colony algorithm based on adaptive phase rotation（BQACO）. The algorithm uses the probability amplitude of quantum bits to represent the ant colony pheromone, uses a pseudo-random selection policy to achieve the moving of the position, based on the adaptive phase rotation strategy and the mutating operation, the pheromone is dynamically updated and the probability of premature convergence is reduced. To test the new algorithm’s optimization performance, a research based on benchmark functions is conducted. The result indicates that the BQACO has a stronger ability of global optimization and higher convergence speed than binary coded quantum ant colony algorithm and continuous quantum ant colony algorithm.

Key words: quantum evolution algorithm, quantum ant colony algorithm, quantum rotation gate, adaptive phase, binary coded

摘要： 基于量子进化理论以及蚂蚁群体的寻优策略，结合一种二进制量子蚁群算法，提出了一种自适应相位旋转的二进制量子蚁群算法（Binary Quantum Ant Colony Optimization Algorithm，BQACO）。该算法采用量子比特概率幅表示蚁群信息素，利用伪随机选择策略实现蚂蚁的位置移动，通过自适应相位旋转以及变异操作，实现蚂蚁信息素的动态更新，并有效降低算法早熟收敛概率。通过标准测试函数对其优化性能进行研究，该算法在函数优化的全局寻优能力和快速搜索能力上，均优于二进制量子蚁群算法和连续量子蚁群算法。

关键词: 量子进化计算, 量子蚁群算法, 量子旋转门, 自适应相位, 二进制编码