摘要
针对SFLA算法运行速度较慢、在优化部分函数问题时精度不高和易陷入局部最优的缺点,提出了一种单种群混合蛙跳算法SPSFLA。该算法采用单个种群,无需对整个种群进行排序,每个个体通过向群体最优个体和群体中心位置学习进行更新。如果当前个体学习没有进步,则对群体最优个体进行变异,并用变异的结果替代当前个体,加快了算法的运行速度和收敛速度,提高了优化精度。仿真实验结果表明,该算法具有更好的优化性能。
Aiming at the shortcomings of shuffled frog leaping algorithm（SFLA） such as ease of trapping into local optimum, low optimization precision and slow speed when it is used to optimize some functions, a Single Population Shuffled Frog Leaping Algorithm （SPSFLA） is proposed. Without sor- ting the whole population, this new algorithm adopts single population. The individuals are updated by learning from the global best individual and the global middle position. If the current individual is not improved, the global best individual will be mutated and the current individual will be replaced by the new one. Those enhance the running speed, the convergence rate and the optimization precision of SPS- FLA. The simulation results show that the improved algorithm has better optimization performance.
出处
《计算机工程与科学》
CSCD
北大核心
2014年第3期463-468,共6页
Computer Engineering & Science
基金
国家自然科学基金资助项目(61063028)
甘肃省教育信息化发展战略研究项目(2011)
关键词
群体智能
混合蛙跳算法
单种群
加速因子
聚群行为
swarm intelligence
shuffled frog leaping algorithm
single population
acceleration fac-tor
swarm behavior
