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针对量子进化计算中反馈信息利用不充分并容易早熟的不足,将量子进化计算与及蚂蚁寻优策略融合,提出了一种新的优化方法—混合量子进化算法(HQEA).以量子染色体表示智能蚂蚁所有可能的搜索路径,初始阶段采用量子进化学习,设计了智能蚂蚁网络及衔接算子,进化学习所得结果表示智能蚂蚁路径选择的概率,并利用蚁群寻优策略继续搜索求精确解.理论证明该算法具有全局收敛性.最后以背包问题对算法进行了测试.
Aiming at the deficiency of insufficient and precocious feedback information in quantum evolutionary computation, a new optimization method named Hybrid Quantum Evolution Algorithm (HQEA) is proposed, which combines quantum evolution computation and ant optimization strategy. Ants all the possible search paths, the initial phase of quantum evolutionary learning, intelligent ant network design and convergence operator, the evolutionary learning results represent the probability of intelligent ant path selection, and use the ant colony optimization strategy to search for exact solutions. It is proved that the algorithm has global convergence. Finally, the algorithm is tested by knapsack problem.