A simultaneous perturbation stochastic approximation enhanced teaching-learning based optimization

Liu, Ao; Deng, Xudong; Tong, Zeping; Luo, Yongliang; Liu, Bo

doi:10.1109/cec.2016.7744192

Cited by 4 publications

(1 citation statement)

References 31 publications

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“…The experimental results show that the search performances of TLBO-BFGS are better than GA, PSO. In [149], the simultaneous perturbation stochastic approximation (SPSA) and basic TLBO algorithm are combined to enhance local search ability and balance the ability of global exploration and local exploitation. Where SPSA goes on local optimization, TLBO performs the global optimization.…”

Section: B Fusion With Other Optimization Methodsmentioning

confidence: 99%

A Survey of Application and Classification on Teaching-Learning-Based Optimization Algorithm

Ren

2020

IEEE Access

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Teaching-Learning-based Optimization is an optimization technique which does not require any algorithm-specific parameters and is popular for its less computational cost and high consistency. Therefore, it has achieved great success application by the researchers in various disciplines of engineering. It works on the philosophy of teaching and learning which is used to solve multi-dimensional, linear and nonlinear problems with appreciable efficiency. Recently the basic TLBO algorithm is improved to enhance its exploration and exploitation capacities and the performance. However, there is less surveys on TLBO algorithm recent advances and its application. In this paper, the successful researches of TLBO algorithm of the past decade are surveyed. Firstly, the available intelligent optimization algorithms were reviewed. Then the application fields of TLBO and the improved TLBO were discussed and analyzed. Furthermore, some representative TLBO methods were classified into three main groups: 1) Improvement of teaching process; and 2) Fusion with Other Optimization Methods; and 3) Weight Methods and Others. Finally, our viewpoints were shared on the open issues and challenges in TLBO as well as research trends in the future.

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Section: B Fusion With Other Optimization Methodsmentioning

confidence: 99%