基于文化混洗蛙跳算法求解连续空间优化问题  

Cultural Shuffled Frog Leaping Algorithm for Continuous Space Optimization Problem

作　　者：张强[1] 朱刘涛 王颖[1] ZHANG Qiang;ZHU Liutao;WANG Ying(School of Computer and Information Technology,Northeast Petroleum University,Daqing 163318,Heilongjiang Province,China)

机构地区：[1]东北石油大学计算机与信息技术学院,黑龙江大庆163318

出　　处：《吉林大学学报（理学版）》2020年第6期1443-1451,共9页Journal of Jilin University:Science Edition

基　　金：国家自然科学基金(批准号:61702093);黑龙江省自然科学基金(批准号:F2018003)。

摘　　要：针对混洗蛙跳算法在求解高维函数时易陷入局部最优解的问题,提出一种文化混洗蛙跳算法,利用群体空间和信念空间的个体通过接受函数和影响函数完成信息交换和全局寻优.首先,信念空间个体通过螺旋更新和随机游走的方式在较优个体附近寻找更优个体;其次,群体空间的最差个体通过借鉴不同知识平衡局部寻优与全局探索的关系,进而提高算法的寻优精度并加快收敛速度;最后,将该算法与12种智能算法进行寻优对比,对典型高维基准函数的测试结果表明,该算法的收敛精度和计算速度均较好.Aiming at the problem that the cultural shuffled frog leaping algorithm was easy to fall into local optimal solution when solving high-dimensional functions,we proposed a cultural shuffled frog leaping algorithm,which used the individuals in group space and belief space to complete information exchange and global optimization through reception function and influence function.Firstly,belief space individuals searched for better individuals around the superior individuals by spiral updating and random walk.Secondly,the worst individuals in group space balanced the relationship between local optimization and global exploration by learning from different knowledge,so as to improve the accuracy of optimization and speed up convergence of the algorithm.Finally,compared the proposed algorithm with 12 intelligent algorithms,the test results of typical high-dimensional benchmark function show that the algorithm has good convergence accuracy and calculation speed.

关 键 词：混洗蛙跳算法 文化算法 变异 优化 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]