Abstract
This is a comparative study of Teaching Learning-Based Optimization (TLBO) as a human-based algorithm against other types of metaheuristic algorithm: single-agent finite impulse response optimizer (SAFIRO), simulated Kalman filter (SKF), particle swarm optimization algorithm (PSO), black hole algorithm (BH), and genetic algorithm (GA), in solving CEC2014 test suite. The TLBO algorithm is inspired by the process of teaching and learning in a classroom. The advantages of TLBO are it only has two main tasks: teaching phase and learning phase and has no parameter setting. The TLBO performance provides a balance between exploration and exploitation. Statistical analysis is then carried out to rank the TLBO results to those obtained by other type of metaheuristic algorithm. The experimental result show that the TLBO algorithm is a promising approach and comparative to SAFIRO and SKF and has better than PSO, BH, and GA.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Talbi E-G (2009) Metaheuristics. John Wiley & Sons Inc., Hoboken NJ USA
Holland JH (1984) Genetic algorithms and adaptation. Adap Control Ill Def Syst 16:317–333
Kennedy J, Eberhart R (1995) Particle swarm optimisation. Proc IEEE Int Conf Neural Netw 1972–1978
Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inf Sci 222
Ibrahim Z, Aziz NHA, Nor NA, Razali S, Mohamad MS (2016) Simulated Kalman Filter: a novel estimation-based metaheuristic optimization algorithm. Adv Sci Lett 22(10):2941–2946
Kalman RE (1960) A new approach to linear filtering and prediction problems. J Fluids Eng Trans ASME 82(1)
Ab Rahman T, Ibrahim Z, Aziz NAA, Zhao S, Abdul Aziz NH (2018) Single-agent finite impulse response optimizer for numerical optimization problems. IEEE Access 6:9358–9374
Uribe-Murcia K, Andrade-Lucio JA, Shmaliy YS, Xu Y (2021) Unbiased FIR filtering under bernoulli-distributed binary randomly delayed and missing data. Eur Signal Process Conf 2021(Janua):2408–2412
Shmaliy YS, Khan S, Zhao S (2016) Ultimate iterative UFIR filtering algorithm. Meas J Int Meas Confed 92:236–242
Rao RV, Savsani VJ, Vakharia DP (2012) Teaching-learning-based optimization: an optimization method for continuous non-linear large scale problems. Inf Sci 183(1):1–15
Liang JJ, Qu BY, Suganthan PN (2014) Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization 2013(December)
Alcalá-Fdez J et al (2009) KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput 13(3)
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69(Mar):46–61
Derrac J, GarcÃa S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Musa, Z., Ibrahim, Z., Shapiai, M.I. (2024). Teaching Learning-Based Optimization for Solving CEC2014 Test Suite: A Comparative Study. In: Mohd. Isa, W.H., Khairuddin, I.M., Mohd. Razman, M.A., Saruchi, S.'., Teh, SH., Liu, P. (eds) Intelligent Manufacturing and Mechatronics. iM3F 2023. Lecture Notes in Networks and Systems, vol 850. Springer, Singapore. https://doi.org/10.1007/978-981-99-8819-8_25
Download citation
DOI: https://doi.org/10.1007/978-981-99-8819-8_25
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8818-1
Online ISBN: 978-981-99-8819-8
eBook Packages: EngineeringEngineering (R0)