计算机应用 ›› 2016, Vol. 36 ›› Issue (11): 3055-3061.DOI: 10.11772/j.issn.1001-9081.2016.11.3055

• 先进计算 • 上一篇    下一篇

基于模拟退火的混合萤火虫Memetic算法

刘翱1,2, 邓旭东1, 李维刚3,4   

  1. 1. 武汉科技大学 管理学院, 武汉 430081;
    2. 智能信息处理与实时工业系统湖北省重点实验室, 武汉 430065;
    3. 武汉科技大学 信息科学与工程学院, 武汉 430081;
    4. 冶金工业过程系统科学湖北省重点实验室, 武汉 430081
  • 收稿日期:2016-05-23 修回日期:2016-06-22 出版日期:2016-11-10 发布日期:2016-11-12
  • 通讯作者: 李维刚
  • 作者简介:刘翱(1987-),男,江西吉安人,讲师,博士,CCF会员,主要研究方向:智能优化、优化调度;邓旭东(1964-),男,湖北武汉人,教授,硕士,主要研究方向:系统工程;李维刚(1978-),男,湖北通城人,教授,博士,主要研究方向:工业复杂系统建模、控制与优化。
  • 基金资助:
    国家自然科学基金资助项目(11271356);教育部人文社会科学研究青年基金资助项目(16YJCZH056);智能信息处理与实时工业系统湖北省重点实验室开放基金资助项目(2016znss18B);冶金工业过程系统科学湖北省重点实验室开放基金资助项目(Z201501);武汉科技大学青年科技骨干培育计划项目(2016xz017)。

Hybrid firefly Memetic algorithm based on simulated annealing

LIU Ao1,2, DENG Xudong1, LI Weigang3,4   

  1. 1. School of Management, Wuhan University of Science and Technology, Wuhan Hubei 430081, China;
    2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan Hubei 430065, China;
    3. College of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan Hubei 430081, China;
    4. Hubei Province Key Laboratory of Systems Science in Metallurgical Process, Wuhan Hubei 430081, China
  • Received:2016-05-23 Revised:2016-06-22 Online:2016-11-10 Published:2016-11-12
  • Supported by:
    This work is partially supported by National Natural Science Foundation of China (11271356), the Humanity and Social Science Youth Foundation of Ministry of Education of China (16YJCZH056), Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System (2016znss18B), Hubei Province Key Laboratory of Systems Science in Metallurgical Process (Z201501), the Young Incubation Program of Wuhan University of Science and Technology (2016xz017).

摘要: 针对标准萤火虫算法(FA),首先,从数学理论上分析并揭示了其存在的种群过早收敛、容易陷入局部最优等不足,然后提出一种基于模拟退火的混合萤火虫Memetic算法。该算法利用标准萤火虫算法对上一代种群进行全局搜索以保持种群的多样性和算法的全局探索能力;使用模拟退火算子对当前种群中的部分个体进行局部搜索,以一定概率接受适应度较差的个体以避免算法陷入局部最优,该算法同步进行萤火虫吸引过程和模拟退火过程以降低算法复杂度。最后,对该算法在10个标准测试函数上进行对比仿真实验。实验结果表明,该算法在6个测试函数中均能找到最优解,最优值、平均值、方差等指标比对比算法高出一定数量级,在4个复合函数中效果均优于萤火虫算法。

关键词: 模拟退火, 萤火虫算法, 局部搜索, Memetic算法

Abstract: A mathematical analysis was carried out theoretically to reveal the fact that the Firefly Algorithm (FA) gets the risk of premature convergence and being trapped in local optimum. A hybrid Memetic algorithm based on simulated annealing was proposed. In the hybrid algorithm, the FA was employed to keep the diversity of firefly population and global exploration ability of the proposed algorithm. And then, the simulated annealing operator was incorporated to get rid of local optimum, which was utilized to carry out local search with partial firefly individuals by accepting bad solutions with some probability, and the proposed algorithm conducted simultaneously the attracting process and the annealing process to reduce the complexity. Finally, the performance of the proposed algorithm and other comparison algorithms were tested on ten standard functions, respectively. The experimental results show that the proposed algorithm can find the optimal solutions in six functions, outperform firefly algorithm, particle swarm optimization, etc, in terms of optimal value, mean value and standard deviation, and find better solutions than firefly algorithm in four functions.

Key words: simulated annealing, Firefly Algorithm (FA), local search, Memetic algorithm

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