保持个体活性的改进FA算法  

Improving firefly algorithm by keeping individual activity

作　　者：陆克中[1] 章哲庆[1] 孙俊 

机构地区：[1]池州学院计算机科学系,安徽池州247100 [2]江南大学物联网学院,江苏无锡214021

出　　处：《中国科学技术大学学报》2016年第2期120-129,共10页JUSTC

基　　金：国家自然科学基金(61170119);安徽省自然科学研究项目(KJ2016A514)资助

摘　　要：在对基本FA算法进行分析的基础上,指出FA算法存在全局搜索能力不足,以及因聚集而早熟收敛现象.为克服FA算法的不足,提出了保持个体活性的改进FA(IFA)算法,分别从γ值自适应调节、过程萤火虫位置更新、聚集萤火虫重新激活以及最优个体的局部处理等多个方面对基本FA算法进行了改进.通过在10个基准测试函数上的测试,并与基本FA算法、PSO算法、ABC算法和其他改进FA算法进行对比,实验结果表明,改进的IFA算法能够很好地保持种群的多样性,具有较快的收敛速度与较好的求解精度,适合复杂函数优化问题.There are some disadvantages in the basic firefly algorithm(FA),such as low solving precision,premature convergence and etc.To overcome these disadvantages,a novel improved FA(IFA)was proposed that keeps individual activity.Firstly,an adaptive control for gamma value was designed by using swarm distance.Secondly,aposition calculation for fireflies was updated by using the search process information.Thirdly,a special mutation for the firefly swarm was executed to activate individuals and to make them explore the search space when losing activity.Finally,aperturbation and local search method for the best individual was proposed.Based on ten multi-model test functions,the test results show that the IFA has a better convergence speed and precision than the basic FA,PSO,ABC and other improved FA.The improved FA is a good method for complex function optimization.

关 键 词：群智能 萤火虫算法 多样性 函数优化 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]