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Teaching-learning based optimization with global crossover for global optimization problems

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  • Ouyang, Hai-bin
  • Gao, Li-qun
  • Kong, Xiang-yong
  • Zou, De-xuan
  • Li, Steven

Abstract

Teaching learning based optimization (TLBO) is a newly developed population-based meta-heuristic algorithm. It has better global searching capability but it also easily got stuck on local optima when solving global optimization problems. This paper develops a new variant of TLBO, called teaching learning based optimization with global crossover (TLBO-GC), for improving the performance of TLBO. In teaching phase, a perturbed scheme is proposed to prevent the current best solution from getting trapped in local minima. And a new global crossover strategy is incorporated into the learning phase, which aims at balancing local and global searching effectively. The performance of TLBO-GC is assessed by solving global optimization functions with different characteristics. Compared to the TLBO, several modified TLBOs and other promising heuristic methods, numerical results reveal that the TLBO-GC has better optimization performance.

Suggested Citation

  • Ouyang, Hai-bin & Gao, Li-qun & Kong, Xiang-yong & Zou, De-xuan & Li, Steven, 2015. "Teaching-learning based optimization with global crossover for global optimization problems," Applied Mathematics and Computation, Elsevier, vol. 265(C), pages 533-556.
    • Handle: RePEc:eee:apmaco:v:265:y:2015:i:c:p:533-556
    DOI: 10.1016/j.amc.2015.05.012
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    References listed on IDEAS

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    1. Basu, M., 2014. "Teaching–learning-based optimization algorithm for multi-area economic dispatch," Energy, Elsevier, vol. 68(C), pages 21-28.
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    2. He, Xinxin & Wang, Zhijian & Li, Yanfeng & Khazhina, Svetlana & Du, Wenhua & Wang, Junyuan & Wang, Wenzhao, 2022. "Joint decision-making of parallel machine scheduling restricted in job-machine release time and preventive maintenance with remaining useful life constraints," Reliability Engineering and System Safety, Elsevier, vol. 222(C).

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