Journal of Computer Applications ›› 2013, Vol. 33 ›› Issue (03): 796-799.DOI: 10.3724/SP.J.1087.2013.00796

• Artificial intelligence • Previous Articles     Next Articles

Chaos-based dynamic population firefly algorithm

FENG Yanhong1*, LIU Jianqin2, HE Yichao1   

  1. 1.School of Information Engineering, Shijiazhuang University of Economics, Shijiazhuang Hebei 050031, China;
    2.Department of International Education, Shijiazhuang Information Engineering Vocational College, Shijiazhuang Hebei 050035, China
  • Received:2012-09-18 Revised:2012-10-09 Online:2013-03-01 Published:2013-03-01

基于混沌理论的动态种群萤火虫算法

冯艳红1*,刘建芹2,贺毅朝1   

  1. 1.石家庄经济学院 信息工程学院, 石家庄 050031;
    2.石家庄信息工程职业学院 国际教育部, 石家庄 050035
  • 通讯作者: 冯艳红
  • 作者简介:冯艳红(1978-),女,河北卢龙人,讲师,硕士,主要研究方向:人工智能; 刘建芹(1966-),女,河北灵寿人,副教授,主要研究方向:智能算法; 贺毅朝(1969-),男,河北晋州人,教授,CCF会员,主要研究方向:智能计算、算法理论、计算机密码学。
  • 基金资助:

    河北省高等学校科学技术研究项目(Z2011143); 河北省科学技术研究与发展计划项目(11213593); 河北省自然科学基金资助项目(F2012208016); 河北省高等学校科学研究项目(Q2012153)。

Abstract: The Firefly Algorithm (FA) has a few disadvantages in the global searching, including slow convergence speed, low solving precision and high possibility of being trapped in local optimum. A FA based on chaotic dynamic population was proposed. Firstly, chaotic sequence generated by cube map was used to initiate individual position, which strengthened the diversity of global searching; secondly, through dynamic monitoring of population, whenever the algorithm meets the preset condition, the new population individuals were generated using chaotic sequences, thus effectively improving convergence speed; thirdly, a Gaussian disturbance would be given on the global optimum of each generation, thus the algorithm could effectively jump out of local minima. Based on six complex test functions, the test results show that chaos-based dynamic population FA improves the capacity of global searching optimal solution, convergence speed and computational precision of solution.

Key words: Firefly Algorithm (FA), chaos, cube mapping, function optimization

摘要: 针对萤火虫算法在全局寻优搜索中收敛速度慢、求解精度低,易陷入局部极值区域等缺陷,提出一种基于混沌理论的动态种群萤火虫算法。首先,该算法采用立方映射产生的混沌序列对萤火虫位置进行初始化,为全局搜索的多样性奠定基础; 其次,通过对种群的动态监测,每当算法满足预设条件时,基于混沌序列生成部分新的个体,以提高算法的收敛速度; 最后,对每一代产生的全局最优解,适时采用高斯扰动进行变异操作,使算法更具有跳出局部极小的能力。通过对6个复杂Benchmark函数进行测试,实验结果表明,该算法提高了全局搜索能力、收敛速度和解的精度。

关键词: 萤火虫算法, 混沌, 立方映射, 函数优化

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