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The New Adaptive ETLBO Algorithms with K-Armed Bandit Model

  • Xitong Wang
  • Yonggang Zhang
  • Jiaxu Cui
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11062)

Abstract

TLBO is a novel efficient swarm intelligent algorithm. In this paper, we first analyze TLBO and ETLBO algorithms in detail. Aiming at the disadvantage of ETLBO that it has to adjust the number of elite according to the different problems, we propose an improved adaptive ETLBO algorithm AETLBO-KAB that is based on K-armed bandit model. Experiments are carried out on six popular continuous non-linear test functions, and the results show that AETLBO-KAB algorithm is effective and brings dramatic improvement compared with TLBO and ETLBO. Furthermore, a new perturbation strategy—discussion group strategy is proposed. And the experimental results indicate that the efficiency of AETLBO-KAB with discussion group algorithm exceeds AETLBO-KAB algorithm.

Keywords

Constraint optimization ETLBO K-armed bandit Adaptive 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China (61170314,61373052), the Project of Jilin Provincial Science and Technology Development(20170414004GH).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyJilin UniversityChangchunChina
  2. 2.Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of EducationJilin UniversityChangchunChina

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