The Research Of Quantum Genetic Algorithm Base On Predatory Search Strategy

Quantum Genetic algorithm is an optimization algorithm which is developed in recent years and based on the theory of quantum computation. It mainly includes Quantum-bit encoding, solution space transform, determination of direction and size of the quantum rotation gate and selection of mutation probability. Among those, the size of quantum rotation gate and the mutation probability are two important factors affecting the computational efficiency of Quantum Genetic Algorithm. It’s hard to choose the two factors especially the mutation probability for it’s generally determined based on the experience, which has the blindness and easily leads to the early-maturing behavior once you have selected wrong. In this regard, this paper introduced predator search strategy on the basis of Quantum Genetic Algorithm. By limiting the threshold value to dynamically resize corner and mutation probability, balancing local search and global search.Firstly, this paper simply introduce the status of study on Quantum Genetic Algorithm at home and abroad in recent years, such as General Quantum Genetic Algorithm, Quantum multi-objective genetic algorithm, Novel Quantum Genetic Algorithm, Multi-universe parallel Quantum Genetic Algorithm, Chaotic Quantum Genetic Algorithm, Immune Quantum Genetic Algorithm and Double-chain Quantum Genetic Algorithm based on real coding, of which General Quantum Genetic Algorithm and Double-chain Quantum Genetic Algorithm based on real coding are emphatically introduced.Secondly, in order to improve the computational efficiency of Quantum Genetic Algorithm, the predator search strategy is added to this article on the basis of the Double-chain Quantum Genetic Algorithm, meanwhile, the angle step function and mutation probability are improvement, furthermore, limiting the threshold value to dynamically resize corner and mutation probability to balance the local search and global search and improve the computational efficiency. Besides, this paper takes Schaffer’sF6function for example and demonstrates the feasibility and effectiveness of the algorithm through simulation.Finally, this paper gives a simple description of the application conditions of Quantum Genetic Algorithm based on predatory strategy as well as the algorithm improvements that may exist, providing a preference for future research directions.