Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (11A): 68-72.doi: 10.11896/jsjkx.200200063

• Artificial Intelligence • Previous Articles     Next Articles

Bat Optimization Algorithm Based on Cosine Control Factor and Iterative Local Search

ZHENG Hao1, YU Jun-yang1,2, WEI Shang-fei1   

  1. 1 School of Software,Henan University,Kaifeng,Henan 475004,China
    2 Henan Intelligent Data Engineering Research Center,Kaifeng,Henan 475004,China
  • Online:2020-11-15 Published:2020-11-17
  • About author:ZHENG Hao,born in 1996,postgraduate.His main research interests include Intelligent algorithm.
    YU Jun-yang,born in 1982,Ph.D,professor,is a member of China Computer Federation.His main research interests include cloud computing and big data.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61672209).

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Abstract: To solve the problem that bat algorithm is easy to fall into local optimal solution when solving high-dimensional complex problems,an improved bat algorithm is proposed in this paper.Firstly,the nonlinear inertia weight controlled by cosine factor is added to the bat velocity formula to dynamically adjust the balance between global search and local search,so as to improve the accuracy and stability of the algorithm.Secondly,at the end of each iteration,the concept of iterated local search is introduced to perturb the local optimal solution to obtain the intermediate state,and then re-search the intermediate state to get the global optimal solution,which can make it jump out of the local optimal solution quickly.Finally,the simulation results on 12 complex benchmark functions with other algorithms show that the improved algorithm solves the problems of low precision,easy to fall into local extremum and unstable solution.

Key words: Bat algorithm, Cosine control factor, Disturbance, Iterative local search

CLC Number: 

  • TP301.6
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