摘要
针对混合蛙跳算法在迭代后期收敛速度变慢的现象,提出一种改进的混合蛙跳算法。该算法引入混沌优化机制,取代随机搜索,对当前群体中的最差个体实施混沌搜索,并替换群体中的部分个体,改善算法摆脱局部极值点的能力。仿真结果表明:改进的蛙跳算法的全局寻优能力明显优于基本的混合蛙跳算法。
In order to prevent appearing slow convergence in post-search of Shuffled Frog Leaping Algorithm（SFLA）,an Improved Shuffled Frog Leaping Algorithm（Im-SFLA） was proposed.By integrating chaotic optimization,instead of the random search,some new individual are reproduced by chaotic searching on the current global worst individual and replace the same number of the stochastic selected individual from the current population,which improves the abilities of stepping out the local extremum.Simulation results demonstrate that Im-SFLA is superior to SFLA.
出处
《中南林业科技大学学报》
CAS
CSCD
北大核心
2011年第10期177-180,共4页
Journal of Central South University of Forestry & Technology
基金
国家自然科学基金(60873247)
关键词
混合蛙跳算法
混沌优化
随机搜索
Shuffled Frog Leaping Algorithm
chaotic optimization
random search
