Quantum genetic algorithm is an intelligent optimization algorithm that has been favored in recent years.Compared with the traditional genetic algorithm,the related theory in quantum computing is integrated into the genetic algorithm,and the quantum properties such as quantum coherence and quantum superposition are used to effectively improve the performance of the algorithm.However,in the face of a type of problem such as multi-peak,non-differentiation or discontinuity,the quantum genetic algorithm under the native framework may still fall into the dilemma of local optimization or premature convergence.Therefore,in view of the above problems,this paper introduces several local search strategies into quantum genetic algorithm.By means of operators such as dynamic inverse probability amplitude and bidirectional decoding,an improved quantum genetic algorithm is proposed to better adapt and solve the problem.This paper first expounds the evolutionary process and evolutionary mechanism of quantum genetic algorithm and genetic algorithm,and analyzes the common points and differences between the two.Secondly,it gives some explanations for the related research and development of quantum genetic algorithms in recent years.Thirdly,combined with the limitations of the current quantum genetic algorithm and the bottleneck encountered in dealing with the problem,the algorithm's operation flow is improved,including the coding mode,the setting of various parameters,and the design of each operator function.and many more.The operators involved in this paper mainly include: dynamic inverse probability amplitude operator,bidirectional decoding operator,quantum revolving gate operator and quantum non-gate operator.Then,the improved quantum genetic algorithm is programmed using Microsoft's development tools.In the process of implementation,it not only ensures the correct operation of the relevant operator,but also ensures its effectiveness in the process of participating in the operation.Finally,the performance of the improved quantum genetic algorithm based on local search strategy is tested.The improved algorithm is verified by several intelligent algorithm function optimization problems with complex properties.Through the improvement of the original framework of the algorithm,the quantum genetic algorithm can achieve a good balance of optimization performance when dealing with practical problems.The local search strategy enables the algorithm to perform both sufficient local optimization and efficient exploration of the solution space.At the same time,the application of the population partitioning strategy enriches the diversity of the population and avoids the phenomenon of premature convergence of the algorithm.After a large amount of experimental data collection and comparison of the algorithm,the optimization value solved by the algorithm is better than the calculation result under the traditional situation. |