Improved QGA Based on Mutation Probability Analysis and Its Application
Dai Yong-qian · Jisuanji gongcheng · 2013
Standard Quantum Genetic Algorithm(QGA) is premature convergence to local optima when it is applied to combinatorial optimization.To solve this problem,this paper analyzes the mutation probability distribution of Q-bit by introducing the k bit variation subspace conception and points out the conflict of traditional random mutation mechanism and the QGA self-implied variation mechanism.Based on these analysis,a novel Stage Large-scale Variation Mechanism Based on Observation(SLVMBOO) is proposed.Mutation operator of SLVMBOO which is embedded in the quantum rotation policy table is simple to implement and it is highly efficient.The tests results of different scale of 0/1 knapsack problem show that this mechanism can effectively avoid the premature convergence and successfully jump out of local optima when it is applied to combinatorial optimization.The global optimization ability is superior to the standard QGA.