• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

J4 ›› 2016, Vol. 38 ›› Issue (06): 1164-1170.

• 论文 • 上一篇    下一篇

全局信息共享的自适应FA算法

陆克中1,2,孙俊3   

  1. (1.池州学院计算机科学系,安徽 池州 247100;
    2.中国科学技术大学合肥微尺度物质科学国家实验室,安徽 合肥230026; 3.江南大学物联网工程学院,江苏 无锡 214021)
  • 收稿日期:2015-04-20 修回日期:2015-06-18 出版日期:2016-06-25 发布日期:2016-06-25
  • 基金资助:

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

An adaptive FA algorithm based on global information sharing  

LU Kezhong1,2,SUN Jun3   

  1. (1.Department of Computer Science,Chizhou University,Chizhou 247100;
    2.Hefei National Laboratory for Physical Sciences at the Microscale,University of Science and Technology of China,Hefei 230026;
     3.School of IoT Engineering,Jiangnan University,Wuxi 214021,China)
  • Received:2015-04-20 Revised:2015-06-18 Online:2016-06-25 Published:2016-06-25

摘要:

针对萤火虫算法FA对于高维复杂问题,收敛速度慢、求解精度低,优化效果不理想等缺点,提出一种基于全局信息共享的自适应FA算法。分别从三个方面对FA算法进行了改进:通过引入群体距离,改进γ值的调节方式,提升算法的自适应调节能力;通过增加过程搜索信息,加强算法的精细化调节能力;通过引入基于全局平均位置信息的量子空间下的δ势阱模式,增强算法的全局搜索能力。最后对几种典型函数的测试结果表明,改进算法在收敛速度与收敛精度上,较其它算法有明显提高。

关键词: 萤火虫算法, 自适应, 全局信息共享, 过程信息更新

Abstract:

The firefly algorithm (FA) for high dimensional problems has some disadvantages, including slow convergence speed, low solving precision and unsatisfactory optimization effect. To overcome these disadvantages, we propose a novel adaptive FA algorithm based on global information sharing. Firstly, an adaptive control for gamma value is designed by the swarm distance. Secondly, the search process information of the firefly algorithm is updated to enhance its adjustment capacity of refinement. Thirdly, the global searching ability is improved by introducing the Delta potential well of the vector subspace based on the global mean location information. Simulation results show that the proposal has better convergence speed and precision than the basic FA and the PSO.

Key words: firefly algorithm;adaptive;global information sharing;process information update