Abstract
The Moth-Flame Optimization (MFO) algorithm is a nature-inspired search algorithm that has delivered good performance and efficiency in solving various optimization problems. In order to avoid local optimum and increase global exploration, each moth of MFO updates its position with respect to a specific MFO operation. However, MFO tends to suffer from a slow convergence speed and produces a low quality solution. This paper presents a new opposition-based scheme and embeds it into the MFO algorithm. The proposed algorithm is called OMFO. The experiments were conducted on a set of commonly used benchmark functions for performance evaluation. The proposed OMFO was compared with the original MFO and four other well-known algorithms, namely, PSO, DE, GSA and GWO. The results clearly showed that OMFO outperformed MFO and the four other algorithms used.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: 6th IEEE International Symposium on Micro Machine and Human Science, pp. 39–43. IEEE Press, New York (1995)
Karaboga, D., Basturk, B.: On the performance of Artificial Bee Colony (ABC) algorithm. Appl. Soft Comput. 8, 687–697 (2008)
Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization. IEEE Comput. Intell. Mag. 1, 28–39 (2006)
Filho, C.J.A.B., et al.: Fish school search. Nature-Inspired Algorithms for Optimization, vol. 193, pp. 261–277. Springer, Berlin (2009)
Corazza, M., Fasano, M., Gusso, R.: Particle swarm optimization with non-smooth penalty reformulation for a complex portfolio selection problem. Appl. Math. Comput. 224, 611–624 (2013)
Yang, J., Zhuang, J.: An improved ant colony optimization algorithm for solving a complex combinatorial optimization problem. Appl. Soft Comput. 10, 653–660 (2010)
Brajevic, I., Tuba, M.: An upgraded Artificial Bee Colony (ABC) algorithm for constrained optimization problems. J. Intell. Manuf. 24, 729–740 (2013)
Boulkabeit, I., et al.: Finite element model updating using fish school search optimization method. In: 11th Brazilian Congress on Computational Intelligence, pp. 447–452 (2013)
Mirjalili, S.: Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl. Based Syst. 89, 228–249 (2015)
Tizhoosh, H.R.: Opposition-based learning: a new scheme for machine intelligence. In: International Conference on Computational Intelligence for Modelling Control and Automation (CIMCA 2005), pp. 695–701 (2005)
Mirjalili, S.: The ant lion optimizer. Adv. Eng. Softw. 83(C), 80–98 (2015)
Tasgetiren, M.F., et al.: Differential evolution algorithms for the generalized assignment problem. In: IEEE Congress on Evolutionary Computation, pp. 2606–2613 (2009)
Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179, 2232–2248 (2009)
Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Apinantanakon, W., Sunat, K. (2018). OMFO: A New Opposition-Based Moth-Flame Optimization Algorithm for Solving Unconstrained Optimization Problems. In: Meesad, P., Sodsee, S., Unger, H. (eds) Recent Advances in Information and Communication Technology 2017. IC2IT 2017. Advances in Intelligent Systems and Computing, vol 566. Springer, Cham. https://doi.org/10.1007/978-3-319-60663-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-60663-7_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60662-0
Online ISBN: 978-3-319-60663-7
eBook Packages: EngineeringEngineering (R0)