摘要
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.
To tackle the shortcoming of deficient using of feedback information in quantum-inspired evolutionary computing, a novel algorithm based on quantum-inspired evolutionary computation and ant colony optimization, hybrid quantum-inspired evolutionary algorithm （HQEA） is proposed in this paper. Solution is represented by quantum chromosome. Quantum-inspired evolutionary algorithm is adopted firstly. Ant colony optimization is employed to give the precision of the solution. Art intelligent ant colony network is proposed. The characteristic of quantum bit is chosen to present the probability of choosing a path by intelligent ant. Theoretical analyses show that HQEA converges to the global optimum. At last, knapsack problem is used to test the algorithm.
出处
《小型微型计算机系统》
CSCD
北大核心
2011年第2期305-309,共5页
Journal of Chinese Computer Systems
基金
国家自然科学基金项目(70971020)资助
国家社科基金项目(08XTQ011)资助
广西社科基金项目(08CJY003)资助
广西大学科研基金项目(X081054)资助
关键词
量子计算
量子进化算法
蚁群优化
quantum computation
quantum-inspired evolutionary algorithm
ant colony optimization
