Abstract
Teaching-Learning-Based Optimization (TLBO) is recently being used as a new, reliable, accurate and robust optimization technique for global optimization. It outperforms some of the well-known metaheuristics regarding constrained benchmark functions, constrained mechanical design, and continuous non-linear numerical optimization problems. However, the success of TLBO in solving some specific types of problems such as shifted function goes down. In this paper we have modified little bit in code of TLBO to improve its performance while solving shifted type of functions. The modified code of TLBO is named as vTLBO (variant TLBO). The performance of vTLBO algorithm is extensively evaluated on 9 shifted and 9 shifted rotated numerical optimization problems and compares favorably with the DE, PSO and conventional TLBO. The results show the better performance of the vTLBO algorithm. Also we have shown that whenever the performance of vTLBO compare with TLBO by taking simple benchmark function, its performance has been degraded.
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Mohankrishna, S., Naik, A., Satapathy, S.C., Rao, K.R.S., Biswal, B.N. (2014). Numerical Optimization of Novel Functions Using vTLBO Algorithm. In: Satapathy, S., Udgata, S., Biswal, B. (eds) Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2013. Advances in Intelligent Systems and Computing, vol 247. Springer, Cham. https://doi.org/10.1007/978-3-319-02931-3_27
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DOI: https://doi.org/10.1007/978-3-319-02931-3_27
Publisher Name: Springer, Cham
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