Research On Quantum Evolutionary Algorithm And It's Applications

Quantum Evolutionary Algorithms (QEA) is a category of novel optimization algorithms evolving from merging quantum theory and Evolutionary Algorithms (EA). Compared to Conventional Evolutionary Algorithms (CEA), QEA has characteristics of small population, rapid convergence and strong capability of global search, because of adopting qubit representation to form the quantum chromosome.With the developing in QEA, lots of scholars propose different kinds of quantum operations, for example, quantum migration strategy, quantum crossover operation, quantum mutation operation etc. But, whether these operations are universal or how to use these operations, we know little about this. In this paper, Global quantum migration strategy is adopted by QEA and Sinusoid Size Ring Structure Quantum Evolutionary Algorithm (SRQEA ) and comparing the optimization results with the results not using migration strategy testing on three Knapsack Problems and five numerical optimization functions. Finally summarize the characteristics of global migration in applications.This paper proposes two improved algorithms (SRQEA2 and SRQEAb13) basing on SRQEA, which adopts the dynamic population size schema. Compared the results that using the Hεgate to that using the quantum rotation gate and summarize the characteristics of the gate in applications. Finally compared the optimization results between the two improved algorithms and QEA, SRQEA.The QEAs has not been used widely, because it starts late. This paper applies the QEA in the Evolvable HardWare (EHW) for the first time, and the results showed the characteristic of good searching capability and fast convergence rate.