基于差分进化策略的天牛须搜索算法及其应用  

A beetle antennae search algorithm based on differential evolution strategy and its application

作　　者：叶坤涛[1] 舒蕾蕾 李文 侯春菊[1] YE Kun-tao;SHU Lei-lei;LI Wen;HOU Chun-ju(School of Science,Jiangxi University of Science and Technology,Ganzhou 341000,China)

机构地区：[1]江西理工大学理学院,江西赣州341000

出　　处：《计算机工程与科学》2023年第5期920-930,共11页Computer Engineering & Science

基　　金：国家自然科学基金(11547026)。

摘　　要：针对天牛须搜索(BAS)算法收敛结果高度依赖单个个体、勘探能力弱、容易陷入局部最优解的问题,提出一种基于差分进化策略的天牛须搜索(BASD)算法。该算法使用佳点集方法初始化天牛种群,提高了算法的种群多样性;引入动态差分进化思想,设计了一种精英演化竞争指导策略,较好地平衡了算法的开采和勘探能力。通过14个基准函数对BASD算法进行测试,并与几种先进智能优化算法的优化结果进行比较。结果显示,BASD算法的优化性能整体更好。将BASD算法应用于图像增强中,结果表明,使用BASD算法增强后的图像灰度分布更均匀、分布范围更大。Considering that the convergence of beetle antennae search algorithm(BAS)is of highly individual dependence,poor exploration ability and easily falling into local optimal solution,a beetle antennae search algorithm based on differential evolution strategy(BASD)is proposed.The algorithm not only uses the good point set method to initialize the beetle population to enhance the population diversity,but also introduces the concept of dynamic differential evolution to an elite evolutionary competition guidance strategy,which better balances the mining and exploration capabilities of the algorithm.The BASD algorithm is tested on 14 benchmark functions and compared with the optimization results of several advanced algorithms.The results show that the overall optimization performance of the BASD algorithm is better.Finally,the BASD algorithm is applied in image enhancement,and the result shows that the gray distribution of the image enhanced by the BASD algorithm is more uniform and the distribution range is larger.

关 键 词：天牛须搜索 差分进化 佳点集理论 图像增强 

分 类 号：TP306.1[自动化与计算机技术—计算机系统结构]