Teaching-Learning-Based Optimization Algorithm for Dealing with Real-Parameter Optimization Problems

Abstract:

A latest optimization algorithm, named Teaching-Learning-Based Optimization (simply TLBO) was proposed by R. V. Rao et al, at 2011. Afterwards, some improvements and practical applications have been conducted toward TLBO algorithm. However, as far as our knowledge, there are no such works which categorize the current works concerning TLBO from the algebraic and analytic points of view. Hence, in this paper we firstly introduce the concepts and algorithms of TLBO, then survey the running mechanism of TLBO for dealing with the real-parameter optimization problems, and finally group its real-world applications with a categorizing framework based on the clustering, multi-objective optimization, parameter optimization, and structure optimization. The main advantage of this work is to help the users employ TLBO without knowing details of this algorithm. Meanwhile, we also give an experimental comparison for demonstrating the effectiveness of TLBO on 5 benchmark evaluation functions and conclude this work by identifying trends and challenges of TLBO research and development.

• Periodical: Applied Mechanics and Materials (Volumes 380-384)
• Pages: 1342-1345
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.380-384.1342
• Online since: August 2013
• Authors: Kai Lin Wang, Hui Bin Wang, Li Xia Yu, Xue Yu Ma, Yun Sheng Xue
• Keywords: Multi-Objective Optimization (MOP), Teaching-Learning-Based Optimization

References

[1] R. V. Rao, V. J. Savsani, D. P. Vakharia, Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems [J], Computer-Aided Design, vol. 43, no. 3, pp.303-315, (2011).

DOI: 10.1016/j.cad.2010.12.015

[2] J. Zhu, X. P. Li, W. M. Shen, Effective genetic algorithm for resourceconstrained project scheduling with limited preemptions [J], International Journal of Machine Learning and Cybernetics, vol. 2, no. 2, pp.55-65, (2011).

DOI: 10.1007/s13042-011-0014-3

[3] C. M. Lin, M. C. Li, A. B. Ting, M. H. Lin, A robust self-learning PID control system design for nonlinear systems using a particle swarm optimization algorithm [J], International Journal of Machine Learning and Cybernetics, vol. 2, no. 4, pp.225-234, (2011).

DOI: 10.1007/s13042-011-0021-4

[4] R. V. Rao, V. J. Savsani, J. Balic, Teaching-learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems [J], Engineering Optimization, vol. 44, no. 12, pp.1447-1462, (2012).

DOI: 10.1080/0305215x.2011.652103

[5] R. V. Rao, V. J. Savsani, D. P. Vakharia, Teaching-learning-based optimization an optimization method for continuous non-linear large scale problems [J], Information Sciences, vol. 183, no. 1, pp.1-15, (2012).

DOI: 10.1016/j.ins.2011.08.006

[6] E. Mezura-Montes, C. A. C. Coello, A simple multi-membered evolution strategy to solve constrained optimization problems [J], IEEE Transactions on Evolutionary Computation, vol. 9, no. 1, pp.1-17, (2005).

DOI: 10.1109/tevc.2004.836819

[7] A. E. M. Zavala, A. H. Aguirre, E. R. V. Diharce, Constrained optimization via evolutionary swarm optimization algorithm (PESO) [C], In Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, pp.209-216, (2005).

DOI: 10.1145/1068009.1068041

[10] D. Karaboga, B. Basturk, Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems [J], Lecture Notes in Artificial Intelligence, vol. 4529, pp.789-798, (2007).

DOI: 10.1007/978-3-540-72950-1_77

[11] R. V. Rao, V. Patel, An elitist teaching learning based optimization algorithm for solving complex constrained optimization problems [J], International Journal of Industrial Engineering Computations, vol. 3, no. 4, pp.535-560, (2012).

DOI: 10.5267/j.ijiec.2012.03.007

[12] M. Crepinsek, S. H. Liu, L. Mernik, A Note on Teaching-Learning-Based Optimization Algorithm [J], Information Sciences, vol. 212, no. 1, pp.79-93, (2012).

DOI: 10.1016/j.ins.2012.05.009