Abstract：The characteristics of strong global search ability with small population size lead to the quantum genetic algorithm is well popular in solving optimization problems.In order to further improve the convergence speed,search stability and overcome the pre-matureness of the quantum genetic algorithm,an improved adaptive mechanism based quantum genetic algorithm was presented in the paper.For the presented algorithm,the individual similarity evaluation operator,individual fitness evaluation operator and population mutation adjustment operator were defined and added into the self-adaptive based quantum genetic algorithm.The way of calculating the three operators were also proposed.Therefore,the current population state can be evaluated by the operators cooperatively,and the individual's mutation probability can be determined according to the current population state.The proposed algorithm can improve the global optimization ability and convergence speed,and reduces the probability of falling into local optimization.In addition,a parallel multi-universe mechanism is employed to improve the time efficiency of the algorithm.Experimental results show that the proposed algorithm has a good performance in the global search performance and time efficiency.
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2019, Vol. 47 
