摘要
基于量子位的混沌特性和相干特性,提出针对一般双层规划问题的层次混沌量子遗传算法(HCQGA).结合进化博弈及多目标优化非支配排序的思想,通过两个混沌量子遗传算法的交互迭代来模拟决策者之间的博弈寻优过程,从而获得使各方利益最大化的双层规划问题的最优解.算法测试结果表明,该算法不仅可以获得Pareto最优解集合,而且还可以克服现有双层规划算法在解决大规模问题时存在的算法复杂度及计算效率问题.
This paper proposed a hierarchical chaotic quantum-inspired genetic algorithm to solve general bilevel programming problems based on the chaotic and coherent characters of Q-bit. In order to maximize the interests of all parties, by means of the interactions of two CQGA iterations to simulate the interaction between policy-makers during the game searching, it can obtain the optimal solution of bilevel programming problems combing the idea of evolution games and multi-objective optimization non-dominated sorts. The simulation shows that the proposed HCQGA not only obtained the Pareto optimal solution set, but also avoided the shortcomings of the complexity and efficiency issues of the existing bilevel programming algorithms in solving large scale problems.
出处
《系统工程学报》
CSCD
北大核心
2013年第2期159-166,共8页
Journal of Systems Engineering
基金
国家自然科学基金资助项目(60905066)
重庆市教委自然科学基金资助项目(KJ070509)
重庆邮电大学自然科学基金资助项目(A2009-03)
关键词
层次混沌量子遗传算法
双层规划
约束优化
hierarchical chaotic quantum-inspired genetic algorithm
bilevel programming
constrained optimization
