摘要
萤火虫优化算法(Glowworm swarm optimization,GSO)是最近新出现的群智能优化方法.针对基本萤火虫算法在求解复杂函数全局最优值时,存在着求解精度较低、容易陷入局部最优和收敛速度较慢等问题,经过深入分析得知原有算法中采用固定步长是导致这些问题的主要原因,提出一种改进的变步长萤火虫优化算法.该算法中步长随着迭代次数的增加而呈曲线递减,这样在迭代开始时由于步长较大,群体可保持较高的全局搜索能力;随着迭代进行步长逐步递减,从而能提高群体的局部搜索能力.最后通过6个标准测试函数的仿真实验,表明了该算法操作简单,在求解精度和收敛速度上都要优于基本萤火虫优化算法.
Glowworm swarm optimization （ GSO ） is a newly appeared method for swarm intelligence optimization. Because the GSO algorithm has low precision defects, easily falling into local optimum value and slow convergence speed when solving the optimal value of complex functions, an improved change the step of GSO algorithm was proposed. Step in the algorithm, with the increase in the number of iterations and the curve of diminishing. So at the beginning of iteration the step is bigger and the group due to stronger global search capability, and in later iterations for smaller step that group has a strong local searching ability. With the experimental results on 6 standard test functions, the results show that this method is superior to GSO in simple operation, computational precision and convergence rate.
出处
《小型微型计算机系统》
CSCD
北大核心
2014年第6期1396-1400,共5页
Journal of Chinese Computer Systems
基金
国家自然科学基金项目(61075049)资助
安徽高校省级自然科学研究项目(KJ2011A268)资助
关键词
萤火虫算法
变步长
函数优化
进化计算
Glowworm Swarm optimization
changing step
function optimization
evolutionary computation
