改进的反向蛙跳算法求解函数优化问题

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 continuous function optimization problems. To overcome such shortages, this paper presented an improved shuffled frog leaping algorithm 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 carried on classic benchmark functions show that it performs significantly better both in terms of convergence speed and solution precision.