摘要
为增强量子进化算法的局部优化能力,结合禁忌搜索思想,提出一种具有逆学习机制的量子自适应禁忌搜索算法.算法采用一种量子自适应邻域映射机制,且禁忌表的禁忌长度可随量子态动态调整,这些策略较好的解决了集中性和多样性搜索的矛盾.另外,算法增加了一种能使个体尽快摆脱劣势区域的逆学习量子更新模式.设计的算法能较好的平衡全局和局部搜索,能有效避免量子过快陷入局部极值.通过实验表明提出的算法具有更好的局部搜索能力.
In order to enhance the local optimization capability of quantum-inspired evolutionary algorithm（QEA）,a novel QEA incorporating inverse learning mode is proposed based on adaptive tabu search.In this algorithm,the neighborhood structure and tabu tenure can be adjusted dynamically casing quantum entanglement states,so that the conflict between intensification and diversification is well solved.At the same time,a novel quantum updating mode named inverse learning is designed to help individuals get out of inferior region.Therefore,better balance between exploration and exploitation can be achieved to escape from a local optimum. Experiment results show that local optimization ability has been advanced effectively through the proposed algorithm.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2013年第6期1069-1075,共7页
Acta Electronica Sinica
基金
国家自然科学基金(No.70971020)
湖北省教育厅科研重点项目(No.D20131804)
关键词
量子进化算法
自适应
禁忌搜索
函数优化
组合优化
quantum-inspired evolutionary algorithm
adaptive
tabu search
function optimization
combinatorial optimization
