摘要： 混合蛙跳算法在进化后期收敛速度慢、优化精度低。为解决该问题，提出改进的混合蛙跳算法。引入共享因子的思想，将其分为全局共享因子和局部共享因子，分别介绍引入2类共享因子后的3种算法，对其中基于全局共享因子的混合蛙跳算法优化性能进行分析，设计算法步骤，分析算法的运行效率。实验结果表明，在全局进化次数和收敛精度分别固定的条件下，该算法在单峰值和多峰值函数寻优问题上均具有较高的收敛速度和精度，能改进混合蛙跳算法的优化性能。

关键词: 混合蛙跳算法, 全局共享因子, 局部共享因子, 差异扰动, 指导能力, 优化性能

Abstract: To solve the problem of slow convergence speed and low optimization precision of Shuffled Frog Leaping Algorithm(SFLA) in later stage of evolution, the optimization SFLAs are proposed in this paper. By introducing the sharing factor, it is divided into global sharing factor and local sharing factor. The idea of three algorithms by adopting two types of sharing factor to SFLA is introduced, and the optimization performance of Shuffled Frog Leaping Algorithm Based on Global Sharing Factor(GSF2LA) is analyzed. Algorithm steps are given and its efficiency is analysed. Experimental results indicate that GSF2LA can effectively improve convergence speed and precision in optimization problem of single peak function and multi-peak function in respectively fixing the condition of global evolution times and convergence precision, and it improves the optimization performance of SFLA.

Key words: Shuffled Frog Leaping Algorithm(SFLA), global sharing factor, local sharing factor, difference disturbance, guidance capability, optimization performance

中图分类号: 

• TP301.6