基于灾变因子的量子遗传算法研究

Research of Quantum Genetic Algorithm Based on Catastrophe Factor

论文先介绍智能寻优算法的理论概念,然后着重通过对量子遗传算法原理的了解,从数学的角度对量子遗传算法进行合理的描述和过程的定义,并对量子遗传算法中加入了灾变算子,来解决不成熟收敛问题。最后编写该算法的Matlab代码实现对3个峰值函数进行仿真测试。通过测试结果比较基于灾变因子的量子遗传算法和标准遗传算法分别在对峰值函数寻优结果的精确度。对结果进行分析,可以根据测试结果发现,基于灾变因子的量子遗传算法在对峰值函数的寻优结果的精确度优于遗传算法。这表明了基于灾变因子的量子遗传算法在连续空间优化的可行性和有效性,同时也表明了基于灾变因子的量子遗传算法具有良好的应用前景。

This paper firstly introduces the theory of the concept of intelligent optimization algorithm,then focuses on the principle of quantum genetic algorithm,the definition and description of the reasonable process of quantum genetic algorithm from the point of view of mathematics,then propose a quantum genetic algorithm based on catastrophe factor,to solve the problem of immature convergence.Finally,the Matlab code of the algorithm to realize the simulation test of 3 peak functions is written.The quantum genetic algorithm based on catastrophe factor is compared with the standard genetic algorithm respectively.Through the test result,the quantum genetic algorithm based on catastrophe factor has better accuracy than the genetic algorithm in the optimization of the peak function.This shows the feasibility and effectiveness of quantum genetic algorithm based on catastrophe factor in continuous space optimization,and it also has a good application prospect.