Chaotic multi-verse optimizer-based feature selection
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The multi-verse optimizer (MVO) is a new evolutionary algorithm inspired by the concepts of multi-verse theory namely, the white/black holes, which represents the interaction between the universes. However, the MVO has some drawbacks, like any other evolutionary algorithms, such as slow convergence and getting stuck in local optima (maximum or minimum). This paper provides a novel chaotic MVO algorithm (CMVO) to avoid these drawbacks, where chaotic maps are used to improve the performance of MVO algorithm. The CMVO algorithm is applied to solve the feature selection problem, in which five benchmark datasets are used to evaluate the performance of CMVO algorithm. The results of CMVO is compared with standard MVO and two other swarm algorithms. The experimental results show that logistic chaotic map is the best chaotic map that increases the performance of MVO, and also the MVO is better than other swarm algorithms.
KeywordsMulti-verse optimizer Chaotic maps Feature selection Dimensionality reduction
Compliance with ethical standards
Conflict of interest
The authors state that there are no conflicts of interest, and this study was carried out without any funding sources.
- 6.Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, MHS’95., vol 1. New York, IEEE, pp 39–43Google Scholar
- 7.Basturk B, Karaboga D (2006) An artificial bee colony (ABC) algorithm for numeric function optimization. In: IEEE swarm intelligence symposium, vol 8. pp 687–697Google Scholar
- 14.Anter AM, Hassanien AE, ElSoud MA, Kim T-H (2015) Feature selection approach based on social spider algorithm: case study on abdominal ct liver tumor. In: 2015 Seventh International Conference on Advanced Communication and Networking (ACN). IEEE, pp 89–94Google Scholar
- 15.Yamany W, Emary E, Hassanien AE (2015) New rough set attribute reduction algorithm based on grey wolf optimization. In: 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), Springer, Egypt, pp 241–251Google Scholar
- 16.Nakamura RYM, Pereira LAM, Costa KA, Rodrigues D, Papa JP, Yang X-S (2012) BBA—a binary bat algorithm for feature selection. In: 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images. IEEE, pp 291–297Google Scholar
- 20.Zhou Z, Zhu S, Zhang D (2015) A novel K-harmonic means clustering based on enhanced firefly algorithm. In: International Conference on Intelligent Science and Big Data Engineering. Springer International Publishing, pp 140–149Google Scholar
- 29.Li M, Du W, Yuan L (2010) Feature selection of face recognition based on improved chaos genetic algorithm. In: 2010 Third International Symposium on Electronic Commerce and Security (ISECS). IEEE, pp 74–78Google Scholar
- 34.Bache K, Lichman M (2013) UCI machine learning repository. University of California, Irvine, School of Information and Computer Sciences. http://archive.ics.uci.edu/ml. Accessed 3 Jan 2017