摘要
人工萤火虫优化算法在寻找函数全局最优值时存在着收敛速度慢、易陷入局部最优、收敛成功率和计算精度低等缺点,为此,文中将人工鱼群算法的觅食行为嵌入到人工萤火虫算法,并与差分进化算法融合,提出一种基于人工萤火虫与差分进化的混合优化算法.最后,通过4个典型测试函数和1个应用实例进行测试,结果表明所提出的混合算法收敛速度快,计算精度高,其整体逼近性能比基本人工萤火虫和差分进化算法更优.
When searching for the globally optimal solution of function,there exist some shortcomings in artificial glowworm swam optimization（GSO）,such as the slow convergence speed,easily falling into the local optimum value,the low success rate of convergence and computational accuracy.This paper embeds predatory behavior of artificial fish swarm algorithm （AFSA） into GSO and proposes a hybrid optimization algorithm which combines the GSO with differential evolution （DE）.Finally,the algorithm is put through four typical test functions and an application example.The results show that the hybrid algorithm has better convergence efficiency and higher computational precision,and its overall approximation performance is superior to basic artificial GSO and DE.
出处
《信息与控制》
CSCD
北大核心
2011年第5期608-613,共6页
Information and Control
基金
国家民族事物委员会科研基金资助项目(08GX01)
广西省自然科学基金资助项目(0991086)
广西民族大学研究生教育创新计划资助项目(gxun-chx2009091)
关键词
人工鱼
人工萤火虫
差分进化
混合算法
artificial fish
artificial glowworm
differential evolution
hybrid algorithm
