Improved Quantum Ant Colony Algorithm And Its Application

Quantum Computation (QC) has many excellent features, such as properties of parallelism, exponential storage and speed up. Today many countries to stydy it, it has become the forefront of countries around the world to explore subjects in depth. The features of superposition of quantum states, entanglement and interference in Quantum theory, is possible to solve many difficult problems in traditional computing, It caused widespread concern as its unique computing performance and powerful computing technology.Discrete optimization plays a great role both in theory and application. It’s almost impossible to retain the optimal solution with deterministic algorithm especially when the scale of the problem is very large. Ant colony optimization (ACO) belongs to meta-heuristic algorithms and it can help us to obtain a reasonable optimal solution. ACO has such features as positive feedback, parallel computing, robustness and so on.Combined with quantum computing and intelligent approach opens up a new subject. It can improve the computational efficiency and overcome fall into local minimum to a certain extent. Therefore, the study of intelligent technology and quantum correlation, the introduction of the classic calculations associated with some of the principles of quantum computing, improved performance computing, but also has important application value and theoretical value. The Quantum-inspired ant colony algorithm is a new algorithm which is based on the combination of ant colony optimization and quantum computing, It has better population dispersion, better parallelism, faster convergence speed and much strong global search capability. This paper concentrates on the principle of Quantum-inspired ant colony algorithm, improved versions of ant system, the theory, as well as strategies to improve the performance of the algorithm and apply to discrete optimization.In this paper, taking advantage of the natural parallelism of the quantum evolutionary algorithm and the cloud platform, realized Quantum-inspired ant colony algorithm on cloud platform, and the results show that there will be a better parallel efficiency in a cloud platform. To further study the performance of it, combined Quantum-inspired ant colony algorithm with neighborhood exchange strategy, a Quantum-inspired ant colony algorithm based on exchange strategy is presented for TSP. In this algorithm, the pheromone on each path is encoded by a group of quantum bits, the quantum rotation gate and ant’s tour are used to update the pheromone so as to accelerate its convergence speed; Some cases from the TSP library(TSPLIB) are used to experiment, the results show that the algorithm has rapider convergence speed and higher accuracy than the classical ant colony algorithm.