Quantum ant colony algorithm for continuous space optimization
求解连续空间优化问题的量子蚁群算法

To tackle the shortcoming of ant colony optimization which can only be applied to discrete problems and hold a slow convergence rate,a novel method for solving optimization problems in continuous space is presented.In this algorithm,each ant carries a group of quantum bits which represent the position of the ant.Firstly,the target where the ant is going to move is selected according to the selection probability based on pheromone information and heuristic information.Secondly,quantum bits of the ant are updated by quantum rotation gates so as to enable the ant to move.Some quantum bits are mutated by quantum non-gate so as to increase the variety of ant positions.Finally,pheromone information and the heuristic information are updated according to the new position of each ant arrived at.In this algorithm,both probability amplitudes of a quantum bit are regarded as position information belonging to an ant,a double searching space is acquired for ant colony which hold the same number of ants.At last,the availability of the algorithm is illustrated by two application examples of function optimization and weight optimization of neural networks.