一种自适应相位旋转的二进制量子蚁群算法

Binary Quantum Ant Colony Algorithm based on adaptive phase rotation

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。

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 al- gorithm 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 phero- mone 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.