摘要
针对混洗蛙跳算法在求解连续函数优化问题中出现的收敛速度慢、求解精度低的缺点,提出了一种基于反向学习策略的改进算法,在种群初始化和进化过程中分别加入反向操作,产生更靠近优质解的种群,从而提高了算法的全局寻优能力,促进了算法收敛。实验仿真表明,新算法在寻优效率、计算精度等方面均优于原算法。
Classical shuffled frog leaping algorithm is slow in convergence, and has a low convergent precision to address con- tinuous function optimization problems. To overcome such shortages, this paper presented an improved shuffled frog leaping al- gorithm which combined the OBL strategy. The proposed approach employed OBL for population initialization and generation jumping to produce populations closer to high-quality solutions. The experiments carded on classic benchmark functions show that it performs significantly better both in terms of convergence speed and solution precision.
出处
《计算机应用研究》
CSCD
北大核心
2013年第3期760-763,共4页
Application Research of Computers
基金
福建省教育厅科技研究项目(JB09113)
福建农林大学青年教师科研基金资助项目(2010018)
关键词
混洗蛙跳算法
反向学习
函数优化
shuffled frog leaping algorithm（SFLA）
opposition-based learning（OBL）
function optimization
