首页|正余弦动态干扰哈里斯鹰算法的PCNN参数优化图像融合

正余弦动态干扰哈里斯鹰算法的PCNN参数优化图像融合

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哈里斯鹰优化算法存在前期全局开发种群分布不广泛、后期局部开发易陷入收敛精度不够的缺陷,因此提出一种正余弦动态干扰的哈里斯鹰优化算法.首先,在前期的全局开发阶段,对两种不同的进化策略分别采用余弦函数和正弦函数进行鹰群群体分布干扰,从而扩大群体分布范围,强化鹰群初期全局探索阶段的广度,为后期进行局部开发提供更好的条件;然后,在局部开发阶段,通过对猎物逃逸能量公式进行曲线化调整,使得猎物能量损耗与自然界中的真实能量损失更加匹配,进而提升开发阶段的捕获能力;最后,将改进的正余弦动态干扰的哈里斯鹰优化算法对脉冲耦合神经网络(PCNN)的链接输入、时间衰减系数、链接强度3个参数进行优化,并应用于可见光与ToF置信图的图像融合.采用6种对比算法及24个测试函数对改进后的算法进行仿真实验验证,证明了基于正余弦动态干扰的哈里斯鹰优化算法具有较好的寻优能力和更高的收敛精度.通过与其他融合算法进行对比实验,验证了改进后的融合算法相比原始算法的融合效果有显著提升.
Sine Cosine Dynamic Interference Harris Hawk Algorithm for PCNN Parameter Optimized Image Fusion
The Harris Hawk optimization algorithm suffers from the defects that the global exploitation population distribution is not extensive in the early stage and the local exploitation is easy to fall into the lack of convergence accuracy in the later stage,a Harris Hawk optimization algorithm with positive-cosine dynamic interference is proposed.Firstly,in the preliminary global development stage,two different evolution-ary strategies are used to disturb the Hawk population distribution by using cosine function and sine function respectively,so as to expand the range of the population distribution,strengthen the breadth of the initial global exploration stage of the Hawk population,and provide better conditions for the local development in the later stage.Then,in the local exploitation stage,the prey escape energy formula is curvilinearly ad-justed to make the prey energy loss match more closely with the real energy loss in nature,and thus enhance the capture ability in the exploita-tion stage.Finally,the improved Harris Hawk optimization algorithm with sine cosine dynamic interference is optimized for the three parame-ters of link input,time decay coefficient,and link strength of pulse-coupled neural network(PCNN)and applied to image fusion of visible and ToF confidence maps.The improved algorithm is validated by simulation experiments using six comparison algorithms and 24 test func-tions.The experimental data finally show that the Harris Hawk optimization algorithm based on sine cosine dynamic interference proposed in this paper can achieve better search capability and better convergence accuracy.Through the fusion comparison experiments with other fusion algorithms,it is verified that the improved fusion algorithm has significantly improved the fusion effect than the original algorithm.

Harris Hawk optimization algorithmdynamic interferenceescaping energypulse coupled neural networkimage fusion

刘立群、陈辉

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甘肃农业大学信息科学技术学院,甘肃兰州 730070

哈里斯鹰优化算法 动态干扰 逃逸能量 脉冲耦合神经网络 图像融合

甘肃省高等学校教师创新基金甘肃省科技计划甘肃农业大学青年导师基金甘肃农业大学信号与系统一流本科课程建设项目

2023A-05120JR5RA032GAU-QDFC-2020-082022-64-6-2

2024

10.11907/rjdk.231066

软件导刊
湖北省信息学会

软件导刊

影响因子：0.524
ISSN：1672-7800
年,卷(期)：2024.23(3)
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