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Improving teaching-learning-based optimization algorithm with golden-sine and multi-population for global optimization

Author

Listed:
  • Xing, Aosheng
  • Chen, Yong
  • Suo, Jinyi
  • Zhang, Jie

Abstract

Teaching-learning-based optimization (TLBO) is an optimization algorithm that has become very popular in recent years and has shown excellent performance in solving many scientific development issues. However, several recent studies have revealed that TLBO struggles with handling complicated issues and has a significant propensity to move back to the original. This research suggests a unique golden-sine and multi-population teaching-learning-based algorithm (GMTLBO) to address these issues. The main innovations of the algorithm are: Firstly, Instead of using the conventional random approach to find the initial individuals, the good point set strategy is utilized, which results in a more uniform initialization of the number of individuals in the search space and enhances the level of accuracy of the initial solution. Second, in the teacher phase, the origin offset issue is resolved by using the golden-sine search model to maintain the appropriate balance between worldwide exploration and regional exploitation. Finally, we incorporated the multi-population learner phase following the learner phase. Based on individual fitness values, the population is segmented into three identically sized subpopulations. Distinct mechanisms are then employed to allocate movement strategies to each sub-population for additional diversification and to prevent the algorithm from converging on regionally optimal solutions. Several validation tests were carried out on 23 traditional standard functions and the CEC2017 and CEC2019 test sets for assessing the effectiveness of the GMTLBO algorithm for resolving global optimization issues. The results demonstrate that GMTLBO converges faster and solves with higher accuracy compared to other algorithms. Additionally, GMTLBO was tested on four engineering design problems to assess its capability to solve restricted optimization issues. The suggested algorithm exhibits outstanding efficacy and competitiveness, according to experimental data.

Suggested Citation

  • Xing, Aosheng & Chen, Yong & Suo, Jinyi & Zhang, Jie, 2024. "Improving teaching-learning-based optimization algorithm with golden-sine and multi-population for global optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 221(C), pages 94-134.
    • Handle: RePEc:eee:matcom:v:221:y:2024:i:c:p:94-134
    DOI: 10.1016/j.matcom.2024.02.008
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    References listed on IDEAS

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    Cited by:

    1. Yindi Ma & Yanhai Li & Longquan Yong, 2024. "Teaching–Learning-Based Optimization Algorithm with Stochastic Crossover Self-Learning and Blended Learning Model and Its Application," Mathematics, MDPI, vol. 12(10), pages 1-19, May.

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