摘要
针对基本萤火虫优化(GSO)算法在求解全局优化问题存在易陷入局部极小值、收敛速度慢和求解精度不高等缺陷,首先对基本萤火虫优化算法采用混沌搜索技术进行初始化,使算法获得质量较高且分布较均匀的初始解,在此基础上再引入交尾行为,提出了一种带交尾行为的混沌萤火虫优化算法(MCGSO)。该算法在一定程度上防止了基本GSO算法易陷入局部最优,且能够获得精度更高的解甚至可达到理论最优解。最后,通过对8个标准测试函数进行测试,测试结果表明,带交尾行为的混沌萤火虫优化算法比基本萤火虫优化算法有更高的收敛速度和求解精度。
According to basic glowworm optimization （GSO） algorithm in solving global optimization problems easily into the local minimum value, slow convergence speed and higher precision, first of basic glowworm defects by chaotic search technology optimization algorithm initialized, make the algorithm can achieve high quality and are uniformly dis- tributed,the initial solution again on this foundation mating behavior, introduced proposed mating behavior with the chaotic fireflies optimization algorithm （MCGSO）. The algorithm to a certain extent prevent basic GSO algorithm easily trapped into local optimal, and can obtain higher accuracy can reach even the solution theory optimal solutions. Finally, based on 8 standard test functions, the test results show that mating behavior with the chaotic glowworm optimization algorithm than the basic glowworm optimization algorithm has higher convergence speed and precision.
出处
《计算机科学》
CSCD
北大核心
2012年第3期231-234,共4页
Computer Science
基金
广西自然科学基金(0991086)资助
关键词
全局优化
GSO
交尾行为
MCGSO
混沌搜索
Global optimization problem, Glowworm swarm optimization, Mating behavior, MCGSO, Chaotic search
