摘要
为解决量子遗传算法(QGA)用于连续多峰函数优化易陷入局部极值的问题,提出一种基于Bloch球面坐标的改进量子遗传算法(GLBQGA):该算法通过引入新的全局-局部变异算子,在保证全局特性基础上加入局部搜索机制,使算法在搜索到全局最优近似解之后能通过局部邻域搜索收敛到全局最优精确解;算法还进一步优化量子转角取值方案,在保证搜索空间不变的同时提高搜索效率。在机车二系支承载荷均匀性分配优化调整及短时交通流多步预测中的应用表明,GLBQGA有效克服了QGA早熟收敛的问题,在不显著增加搜索时间的前提下提高了求解精度。
An improved Bloch Spherical Quantum Genetic Algorithm （GLBQGA） was proposed to overcome theshortcoming of the quantum genetic algorithm （QGA） , i.e., local optimization, when it is used for the optimiza-tion of continuous functions with many extreme values. In order to make sure the algorithm can converge to theexact solution of optimal local neighborhood search after searching global optimum approximation, a new variationof global-local operator was introduced, and a local search mechanism was established based on globally attrib-utes. The quantum angular value program was further optimized, thile ensuring the search space and improvingthe efficiency of search. Calculative examples were made in optimization of locomotive secondary uniform loaddistribution and application of short- time traffic flow prediction, and the results show that GLBQGA canovercome the QGA premature convergence problems, and improve precision without increasing search time signif-icantly.
出处
《铁道科学与工程学报》
CAS
CSCD
北大核心
2016年第11期2262-2269,共8页
Journal of Railway Science and Engineering
基金
国家自然科学基金资助项目(51305467)
关键词
Bloch球面坐标
量子遗传算法
调簧
交通流预测
bloch spherical coordinate
quantum genetic algorithm
spring adjustment
traffic flow prediction
