摘要
为了有效改善萤火虫算法对初始解依赖性,迭代后期收敛速度较慢以及搜索精度不高等问题,提出一种基于Tent混沌映射的萤火虫优化算法.该算法将混沌优化思想应用到萤火虫算法中,利用混沌映射初始化种群,在迭代后期对种群中较优解进行混沌优化,同时在整个迭代过程中自适应调整萤火虫算法的步长因子.基准测试函数的测试结果显示,改进的萤火虫算法在收敛速度和搜索精度方面都得到提高,并且与其他基于混沌理论思想的优化算法相比,同样具有一定的优势.
In order to effectively improve the dependence of the firefly algorithm on the initial solution,the slower convergence speed in the late iteration and the low search accuracy,the chaos optimization idea is applied to the firefly algorithm,and the step size factor of the firefly algorithm is adjusted adaptively.A firefly optimization algorithm based on the Tent chaotic map is proposed.The algorithm first uses chaotic mapping to initialize the population.Next,chaos optimization is performed for the better solution in the population at the end of the iteration,and adaptive adjust ment of the step size factor during the entire iteration is realized.The test results of the benchmark function show that the improved firefly algorithm has better performance both in convergence speed and search accuracy,and has advantages compared with other chaos optimization algorithm.
作者
刘园园
贺兴时
LIU Yuanyuan;HE Xingshi(School of Science,Xi’an Polytechnic University,Xi’an 710048,China)
出处
《纺织高校基础科学学报》
CAS
2018年第4期511-518,共8页
Basic Sciences Journal of Textile Universities
基金
陕西省软科学研究计划项目(2014KRM2801)
西安市教育科技重大招标项目(2015ZB-ZY04)
关键词
萤火虫算法
混沌映射
混沌优化算法
参数优化
基准测试函数
firefly algorithm
chaotic map
chaos optimization algorithm
parameter optimization
benchmarking function
