摘要
利用混沌映射的遍历性和实编码遗传算法的全局优化性,通过在遗传进化过程中加入混沌变异操作,在变量的定义域内投放大量的混沌初始群体,在实编码遗传算法进化过程中加入单纯形法学习算子,建立了一种新的混沌高效遗传算法(chaos higher efficient genetic algorithm,CHEGA).应用该法对3个非线性、高维、多峰值测试函数进行了仿真,在收敛速度和全局优化方面好于现有的简单遗传算法和改进的遗传算法.建立了水库含沙量预报模型.并将CHEGA用于求解上述模型的参数优化问题,与实数编码加速遗传算法(RAGA)、二进制加速遗传算法和随机优化算法等方法相比,CHEGA可以遍历到整个区域,较好的保持了种群的多样性,并且精度高、收敛速度快.CHEGA对求解实际水库计算模型的参数优化问题非常有效.
A new algorithm, chaos higher efficient genetic algorithm （CHEGA）, is proposed for the forecast of the sediment concentration in reservoir, in which initial population is generated by chaos algorithm and new chaos mutation operation is used. With the shrinking of searching range, the method gradually directs to optimal result by the excellent individuals obtained by real-encoded genetic algorithm embedding with simplex searching operator and simplex algorithm. The forecast model of the sediment concentration in reservoir is established. It is very efficient in maintaining the population diversity during the evolution process of genetic algorithm. Its efficiency is verified by application of three test functions compared with standard binary-encoded genetic algorithm and improved genetic algorithm. Compared with real-encoded accelerating genetic algorithm, binary-encoded accelerating genetic algorithm and random algorithm, CHEGA can get to the whole searching range and it has rapider convergent speed and higher calculation precision. It is good for the global optimization in the practical reservoir calculation.
出处
《北京师范大学学报（自然科学版）》
CAS
CSCD
北大核心
2007年第2期194-198,共5页
Journal of Beijing Normal University(Natural Science)
基金
水资源与水电工程科学国家重点实验室开放资助项目(2005B021)
国家重点基础研究发展规划资助项目(G2003CB415204)
关键词
预报模型
参数优化
遗传算法
混沌
含沙量
forecast model
parameter optimization
genetic algorithm
chaos
sediment concentration
