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中文摘要：
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。
英文摘要：
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.
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