摘要
提出了一种改进的混合量子遗传算法(IHQGA),该算法首先在量子个体上实施量子交叉,这一操作有利于保留相对较好的基因段;其次,采用量子比特相位法更新量子门和自适应调整搜索网格的策略;最后,引入拟Newton算法进行局部搜索操作,使得种群的多样性强,解得的收敛精度高,收敛速度快;通过复杂函数测试标明此算法的优化质量和效率都强于传统遗传算法和量子遗传算法;另外,从理论上也证明了该算法以概率1收敛于全局最优解。
This paper proposes an Improved Hybrid Quantum Genetic Algorithm （IHQGA）. First,the quantum crossover is used which can maintain the relatively good gene blocks. Second, the strategies of updating quantum gate using qubit phase approach and adjusting search grid adaptively are introduced. Third, the similar Newton method is introduced as a local searching scheme, which is characterized by rapid convergence, good global searching capability and short computing time. Test results of complex functions and application example demonstrate that the algorithm is superior to conventional genetic algorithms and quantum genetic algorithm in quality and efficiency.
出处
《计算机科学》
CSCD
北大核心
2008年第8期112-115,共4页
Computer Science
基金
山东省自然科学基金(Q2006003)
