With the development of the times, science and technology, optimization theory and algorithms are increasingly being widely applied to our work and lives. In real life, many problems are characterized by discrete combinatorial optimization problems, such as the traveling salesman problem, vehicle routing problem, knapsack problem, minimal spanning tree problems, and graph coloring problem, all this kind of combinatorial optimization problems have been identified as NP-hard problem. Optimization is a classic and important branch of operational research, widely used in industry, agriculture, national defense, engineering, transport, finance, chemicals, energy, communications, management and many other areas. Optimization theory and algorithms can effectively provide the theoretical basis and solutions to urban planning, production planning arrangement, engineering and manufacturing design, resource allocation and other issues.Currently, the swarm intelligence optimization algorithms have become a hotspot research issues in optimization; it is an effective algorithm for solving optimization problems. Typical swarm intelligence optimization algorithm includes ant colony optimization algorithm optimization algorithms, bee colony optimization algorithm, and artificial fish swarm algorithm and so on. Swarm intelligence algorithm has the disadvantage of easily geting into local optimum, convergencing slowly and other defects. In this paper, quantum-inspired swarm intelligence optimization algorithm is presented by using quantum computing. It introduces quantum-inspired ant colony algorithm, quantum-inspired artificial bee colony algorithm, and quantum-inspired artificial fish swarm algorithm, etc. In this paper, both theoretical and experimental studies were carried out on them. The main research works of this dissertation are as follows:(1)The thesis reviewed the basic principles and development of swarm intelligence optimization algorithm. And it noted the current research in algorithm theory with the improvement of the algorithm and some shortcoming in applications, and gave some possible research directions.(2)With the quantum state, quantum bits, quantum logic gates combined, quantum-inspired ant colony algorithm, quantum-inspired artificial bee colony algorithm, and quantum-inspired artificial fish swarm algorithm are proposed base on ant colony algorithm, artificial bee colony algorithm, and artificial fish swarm algorithm. And the thesis describes the basic idea and step of all these algorithms.(3) Quantum-inspired ant colony algorithm, quantum-inspired artificial bee colony algorithm, and quantum-inspired artificial fish swarm algorithm are used to solve combinatorial optimization problems which including the traveling salesman problem, knapsack problem, vehicle routing problem, Steiner minimal spanning tree problem, figure coloring problem and the QoS multicast routing problem. The good performance of the algorithm has been verified from the experimental validation, and the convergence analysis of the algorithm is also been proved.(4)These entire three quantum-inspired algorithms are used to solve the matching problem in fingerprint identification problem of the indoor position system; and they have obtained satisfactory results through simulation experiments and field tests.Overall, this thesis proposed the quantum-inspired ant colony algorithm, quantum-inspired artificial bee colony algorithm, and quantum-inspired artificial fish swarm algorithm for solving combinatorial optimization problem, and prvoed the convergence of quantum-inspired ant colony algorithm in a combinatorial problem. The computational comparison of series of numerical examples shows that the quantum-inspired swarm intelligence can be a practical algorithm for solving the combinatorial optimization problem. Research work of thesis enriched the theoretical basis of quantum-inspired swarm intelligence optimization algorithms, and further strengthened the optimizational capabilities of swarm intelligence optimization algorithm on the basis of quantum computing.The research also expanded the range in applicatons of discrete issues and practical problems which sloved by quantum swarm intelligence optimization algorithm. It has some theoretical and practical significance for the development of quantum swarm intelligence optimization algorithm. |