Weighted Teaching-Learning-Based Optimization for Global Function Optimization

Affiliation(s)

Anil Neerukonda Institute of Technology and Sciences, Vishakapatnam, India.

Majhighariani Institute of Technology & Science, Rayagada, India.

Centurion University of Technology and Management, Paralakhemundi, India.

Anil Neerukonda Institute of Technology and Sciences, Vishakapatnam, India.

Majhighariani Institute of Technology & Science, Rayagada, India.

Centurion University of Technology and Management, Paralakhemundi, India.

ABSTRACT

Teaching-Learning-Based Optimization (TLBO) is recently being used as a new, reliable, accurate and robust optimization technique scheme for global optimization over continuous spaces [1]. This paper presents an, improved version of TLBO algorithm, called the Weighted Teaching-Learning-Based Optimization (WTLBO). This algorithm uses a parameter in TLBO algorithm to increase convergence rate. Performance comparisons of the proposed method are provided against the original TLBO and some other very popular and powerful evolutionary algorithms. The weighted TLBO (WTLBO) algorithm on several benchmark optimization problems shows a marked improvement in performance over the traditional TLBO and other algorithms as well.

Cite this paper

S. Satapathy, A. Naik and K. Parvathi, "Weighted Teaching-Learning-Based Optimization for Global Function Optimization,"*Applied Mathematics*, Vol. 4 No. 3, 2013, pp. 429-439. doi: 10.4236/am.2013.43064.

S. Satapathy, A. Naik and K. Parvathi, "Weighted Teaching-Learning-Based Optimization for Global Function Optimization,"

References

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[24] B. Alatas, “Chaotic Bee Colony Algorithms for Global Numerical Optimization,” Expert Systems with Applications, Vol. 37, No. 8, 2010, pp. 5682-5687. doi:10.1016/j.eswa.2010.02.042

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[26] F. Kang, J. J. Li and Z. Y. Ma, “Rosenbrock Artificial Bee Colony Algorithm for Accurate Global Optimization of Numerical Functions,” Information Sciences, Vol. 181, No. 16, 2011, pp. 3508-3531. doi:10.1016/j.ins.2011.04.024

[27] W. F. Gao and S. Y. Liu, “Improved Artificial Bee Colony Algorithm for Global Optimization,” Information Processing Letters, Vol. 111, No. 17, 2011, pp. 871-882. doi:10.1016/j.ipl.2011.06.002

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[29] S. Das, A. Abraham, U. K. Chakraborty and A. Konar, “Differential Evolution Using a Neighborhood-Based Mutation Operator,” IEEE Transactions on Evolutionary Computation, Vol. 13, No. 3, 2009, pp. 526-553. doi:10.1109/TEVC.2008.2009457

[1] R. V. Rao, V. J. Savsani and D. P. Vakharia, “Teaching-Learning-Based Optimization: A Novel Method for Constrained Mechanical Design Optimization Problems,” Computer-Aided Design, Vol. 43, No. 1, 2011, pp. 303-315. doi:10.1016/j.cad.2010.12.015

[2] R. V. Rao, V. J. Savsani and D. P. Vakharia, “Teaching-Learning-Based Optimization: An Optimization Method for Continuous Non-Linear Large Scale Problems,” INS 9211 No. of Pages 15, Model 3G 26 August 2011.

[3] R. V. Rao, V. J. Savsani and J. Balic, “Teaching Learning Based Optimization Algorithm for Constrained and Unconstrained Real Parameter Optimization Problems,” Engineering Optimization, Vol. 44, No. 12, 2012, pp. 1447-1462. doi:10.1080/0305215X.2011.652103

[4] R. V. Rao and V. K. Patel, “Multi-Objective Optimization of Combined Brayton and Inverse Brayton Cycles Using Advanced Optimization Algorithms,” Engineering Optimization, Vol. 44, No. 8, 2012, pp. 965-983. doi:10.1080/0305215X.2011.624183

[5] R. V. Rao and V. J. Savsani, “Mechanical Design Optimization Using Advanced Optimization Techniques,” Springer-Verlag, London, 2012. doi:10.1007/978-1-4471-2748-2

[6] V. Togan, “Design of Planar Steel Frames Using Teaching-Learning Based Optimization,” Engineering Structures, Vol. 34, 2012, pp. 225-232. doi:10.1016/j.engstruct.2011.08.035?

[7] R. V. Rao and V. D. Kalyankar, “Parameter Optimization of Machining Processes Using a New Optimization Algorithm,” Materials and Manufacturing Processes, Vol. 27, No. 9, 2011, pp. 978-985. doi:10.1080/10426914.2011.602792

[8] S. C. Satapathy and A. Naik, “Data Clustering Based on Teaching-Learning-Based Optimization. Swarm, Evolutionary, and Memetic Computing,” Lecture Notes in Computer Science, Vol. 7077, 2011, pp. 148-156, doi:10.1007/978-3-642-27242-4_18

[9] J. H. Holland, “Adaptation in Natural and Artificial Systems,” University of Michigan Press, Ann Arbor, 1975.

[10] J. G. Digalakis and K. G. Margaritis, “An Experimental Study of Benchmarking Functions for Genetic Algorithms,” International Journal of Computer Mathematics, Vol. 79, No. 4, 2002, pp. 403-416. doi:10.1080/00207160210939

[11] R. C. Eberhart and Y. Shi, “Particle Swarm Optimization: Developments, Applications and Resources,” IEEE Proceedings of International Conference on Evolutionary Computation, Vol. 1, 2001, pp. 81-86.

[12] R. C. Eberhart and Y. Shi, “Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimization,” IEEE Proceedings of International Congress on Evolutionary Computation, Vol. 1, 2000, pp. 84-88.

[13] J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” IEEE Proceedings of International Conference on Neural Networks, Vol. 4, 1995, pp. 1942-1948. doi:10.1109/ICNN.1995.488968

[14] J. Kennedy, “Stereotyping: Improving Particle Swarm Performance with Cluster Analysis,” IEEE Proceedings of International Congress on Evolutionary Computation, Vol. 2, 2000, pp. 303-308.

[15] Y. Shi and R. C. Eberhart, “Comparison between Genetic Algorithm and Particle Swarm Optimization,” Lecture Notes in Computer Science—Evolutionary Programming VII, Vol. 1447, 1998, pp. 611-616.

[16] Y. Shi and R. C. Eberhart, “Parameter Selection in Particle Swarm Optimization,” Lecture Notes in Computer Science Evolutionary Programming VII, Vol. 1447, 1998, pp. 591-600. doi:10.1007/BFb0040810

[17] Y. Shi and R. C. Eberhart, “Empirical Study of Particle Swarm Optimization,” IEEE Proceedings of International Conference on Evolutionary Computation, Vol. 3, 1999, pp. 101-106.

[18] R. Storn and K. Price, “Differential Evolution—A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces,” Journal of Global Optimization, Vol. 11, No. 4, 1997, pp. 341-359. doi:10.1023/A:1008202821328

[19] R. Storn and K. Price, “Differential Evolution—A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces,” Technical Report, International Computer Science Institute, Berkley, 1995.

[20] K. Price, R. Storn and A. Lampinen, “Differential Evolution a Practical Approach to Global Optimization,” Springer Natural Computing Series, Springer, Heidelberg, 2005.

[21] S. Das, A. Konar and U. K. Chakraborty, “Two Improved Differential Evolution Schemes for Faster Global Search,” Genetic and Evolutionary Computation Conference, Washington DC, 25-29 June 2005.

[22] Z. H. Zhan, J. Zhang, Y. Li and S. H. Chung, “Adaptive Particle Swarm Optimization,” IEEE Transactions on Systems, Man, and Cybernetics—Part B, Vol. 39, No. 6, 2009, pp. 1362-1381. doi:10.1109/TSMCB.2009.2015956

[23] A. Ratnaweera, S. Halgamuge and H. Watson, “Self-Organizing Hierarchical Particle Swarm Optimizer with Time-Varying Acceleration Coefficients,” IEEE Transactions on Evolutionary Computation, Vol. 8, No. 3, 2004, pp. 240-255. doi:10.1109/TEVC.2004.826071

[24] B. Alatas, “Chaotic Bee Colony Algorithms for Global Numerical Optimization,” Expert Systems with Applications, Vol. 37, No. 8, 2010, pp. 5682-5687. doi:10.1016/j.eswa.2010.02.042

[25] G. P. Zhu and S. Kwong, “Gbest-Guided Artificial Bee Colony Algorithm for Numerical Function Optimization,” Applied Mathematics and Computation, Vol. 217, No. 7, 2010, pp. 3166-3173. doi:10.1016/j.amc.2010.08.049

[26] F. Kang, J. J. Li and Z. Y. Ma, “Rosenbrock Artificial Bee Colony Algorithm for Accurate Global Optimization of Numerical Functions,” Information Sciences, Vol. 181, No. 16, 2011, pp. 3508-3531. doi:10.1016/j.ins.2011.04.024

[27] W. F. Gao and S. Y. Liu, “Improved Artificial Bee Colony Algorithm for Global Optimization,” Information Processing Letters, Vol. 111, No. 17, 2011, pp. 871-882. doi:10.1016/j.ipl.2011.06.002

[28] S. Das and A. Abraham and A. Konar, “Automatic Clustering Using an Improved Differential Evolution Algorithm,” IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans, Vol. 38, No. 1, 2008.

[29] S. Das, A. Abraham, U. K. Chakraborty and A. Konar, “Differential Evolution Using a Neighborhood-Based Mutation Operator,” IEEE Transactions on Evolutionary Computation, Vol. 13, No. 3, 2009, pp. 526-553. doi:10.1109/TEVC.2008.2009457