计算机科学 ›› 2018, Vol. 45 ›› Issue (8): 54-62.doi: 10.11896/j.issn.1002-137X.2018.08.010
张新明1,2, 程金凤1, 康强1, 王霞1
ZHANG Xin-ming1,2, CHENG Jin-feng1, KANG Qiang1, WANG Xia1
摘要: 针对混合蛙跳算法(Shuffled Frog Leaping Algorithm,SFLA)存在的计算复杂度高、优化效率不理想等问题,提出了一种改进的混合蛙跳算法(Improved Shuffled Frog Leaping Algorithm,ISFLA)。在原始 SFLA的基础上进行如下改进:首先,将其中每次只更新组内最差青蛙的方式改为更新组内所有青蛙的方式,这既增大了获得优质解的概率,又省去了调整组内迭代次数的步骤,从而提升了优化效率和可操作性;其次,将基于局部最优更新的方法和基于全局最优更新的方法融合为一种混合扰动更新方法,从而避免了复杂条件的选择步骤,进一步提升了优化效率;最后,去掉随机更新方式,以免优质解被破坏,从而提高了整体的优化性能。将 ISFLA 用于 CEC2005和CEC2015连续基准函数的优化测试和基于Renyi 熵的灰度和彩色图像分割的多阈值选择实验中,结果表明,与 SFLA 和state-of-the-art的LSFLA 相比,ISFLA 具有更高的优化效率,更适用于多阈值图像分割的阈值选择。
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