Abstract
Multimodal function optimization has attracted a growing interest especially in the evolutionary computation research community. Multimodal optimization deals with optimization tasks that involve finding all or most of the multiple solutions (as opposed to a single best solution). The challenge is to identify as many optima as possible to provide a choice of good solutions to the designers. A composite function is a combination of the two or more functions. The Teaching-Learning-Based Optimization (TLBO) algorithm is a teaching-learning process inspired algorithm based on the effect of influence of a teacher on the output of learners in a class. In this paper, the TLBO algorithm has been tested on six composite test functions for numerical global optimization. The TLBO algorithm has outperformed the other six algorithms for the composite test problems considered.
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References
Ahrari, A., Atai, A.A.: Grenade explosion method-A novel tool for optimization of multimodal functions. Applied Soft Computing 10, 1132–1140 (2010)
Akay, D., Karaboga, A.: Modified Artificial Bee Colony algorithm for real-parameter optimization. Information Sciences 192, 120–142 (2012)
Bergh, F., Engelbrecht, A.P.: A cooperative approach to particle swarm optimization. IEEE Transactions on Evolutionary Computation 8(3), 225–239 (2004)
Coello, C.A.C., Pulido, G.T., Lechuga, M.S.: Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation 8(3), 256–279 (2004)
Liang, J.J., Suganthan, P.N., Deb, K.: Novel Composition Test Functions for Numerical Global Optimization. IEEE Trans. on Evolutionary Computation 5(1), 1141–1153 (2005)
Leung, Y.W., Wang, Y.P.: An orthogonal genetic algorithm with quantization for global numerical optimization. IEEE Trans. on Evolutionary Computation 5(1), 41–53 (2001)
Rao, R.V., Patel, V.: An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. International Journal of Industrial Engineering Computations 3(4), 535–560 (2012)
Rao, R.V., Patel, V.: Multi-objective optimization of two stage thermoelectric cooler using a modified teaching–learning-based optimization algorithm. Engineering Applications of Artificial Intelligence (2012), doi:10.1016/j.engappai.2012.02.016
Rao, R.V., Savsani, V.J., Vakharia, D.P.: Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design 43, 303–315 (2011)
Rao, R.V., Savsani, V.J., Balic, J.: Teaching-learning-based optimization algorithm for unconstrained and constrained real parameter optimization problems. Engineering Optimization, doi:10.1080/0305215X.2011.652103
Suganthan, P.N., Hansen, N., Liang, J.J.: Problem definition and evaluation criteria for the CEC 2005 special session on real-parameter optimization, Technical Report, Nanyang Technological University, Singapore (2005)
Soloman, R.: Reevaluating genetic algorithm performance under coordinate rotation of benchmark functions. BioSystems 39, 263–278 (1996)
Akbari, R., Hedayatzadeh, R., Ziarati, K., Hassanizadeh, B.: A muti-objective artificial bee colony algorithm. Swarm and Evolutionary Computation 2, 39–52 (2012)
Wong, K., Wu, K., Peng, C., Zhang, Z.: Evolutionary multimodal optimization using the principle of locality. Information Sciences 194, 138–170 (2012)
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Rao, R.V., Waghmare, G.G. (2013). Solving Composite Test Functions Using Teaching-Learning-Based Optimization Algorithm. In: Satapathy, S., Udgata, S., Biswal, B. (eds) Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA). Advances in Intelligent Systems and Computing, vol 199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35314-7_45
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DOI: https://doi.org/10.1007/978-3-642-35314-7_45
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