计算机应用 ›› 2017, Vol. 37 ›› Issue (5): 1363-1368.DOI: 10.11772/j.issn.1001-9081.2017.05.1363

• 人工智能 • 上一篇    下一篇

基于人工萤火虫局部决策域的改进生物地理学优化算法

王智昊1,2, 刘培玉1,2, DING Ding3   

  1. 1. 山东师范大学 信息科学与工程学院, 济南 250014;
    2. 山东省分布式计算机软件新技术重点实验室, 济南 250014;
    3. Department of Mathematics, University of Padua, Padua 35100, Italy
  • 收稿日期:2016-10-16 修回日期:2016-12-01 出版日期:2017-05-10 发布日期:2017-05-16
  • 通讯作者: 刘培玉
  • 作者简介:王智昊(1986-),男,山东济南人,博士研究生,主要研究方向:智能算法;刘培玉(1960-),男,山东临朐人,教授,CCF会员,主要研究方向:信息安全;DING Ding (1987-),男,山东济南人,博士研究生,主要研究方向:网络安全。
  • 基金资助:
    国家自然科学基金资助项目(61373148,61502151);山东省自然科学基金资助项目(ZR2014FL010);山东省社会科学规划项目(2012BXWJ01,15CXWJ13,16CFXJ05)。

Improved biogeography-based optimization algorithm based on local-decision domain of glowworm swarm optimization

WANG Zhihao1,2, LIU Peiyu1,2, DING Ding3   

  1. 1. School of Information Science and Engineering, Shandong Normal University, Jinan Shandong 250014, China;
    2. Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Jinan Shandong 250014, China;
    3. Department of Mathematics, University of Padua, Padua 35100, Italy
  • Received:2016-10-16 Revised:2016-12-01 Online:2017-05-10 Published:2017-05-16
  • Supported by:
    This work is partially supported by National Natural Science Foundation (61373148, 61502151), the Shandong Province Natural Science Foundation (ZR2014FL010), the Shandong Province Social Science Project (2012BXWJ01, 15CXWJ13, 16CFXJ05).

摘要: 针对生物地理学优化(BBO)算法搜索能力不足的缺点,提出基于萤火虫算法局部决策域策略的改进迁移操作来提算法的全局寻优能力。改进的迁移操作能够在考虑不同栖息地各自的迁入率与迁出率的基础上,进一步利用栖息地之间的相互影响关系。将改进算法应用于12个典型的函数优化问题来测试改进生物地理学优化算法的性能,验证了改进算法的有效性。与BBO、改进BBO(IBBO)、基于差分进化的BBO(DE/BBO)算法的实验结果表明,改进算法提高了算法的全局搜索能力、收敛速度和解的精度。

关键词: 生物地理学优化, 迁移策略, 萤火虫算法, 局部决策域, 邻域范围

Abstract: Aiming at the lack of searching ability of Biogeography-Based Optimization (BBO) algorithm, an improved migration operation based on local-decision domain was proposed to improve the global optimization ability of the algorithm. The improved migration operation can further utilize the interaction between habitats in consideration of the respective migration rates and evapotranspiration rates of different habitats. The improved algorithm was applied to 12 typical function optimization problems to test the performance, and the effectiveness of the improved algorithm was verified. Compared with BBO, Improved BBO (IBBO) and Differential Evolution/BBO (DE/BBO), the experimental results show that the proposed algorithm can improve the capacity of global searaching optimal solution, convergence speed and computational precision of solution.

Key words: Biogeography-Based Optimization (BBO), migration operation, Glowworm Swarm Optimization (GSO), local-decision domain, neighborhood range

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