Dragonfly Algorithm: Theory, Literature Review, and Application in Feature Selection
In this chapter, a wrapper-based feature selection algorithm is designed and substantiated based on the binary variant of Dragonfly Algorithm (BDA). DA is a successful, well-established metaheuristic that revealed superior efficacy in dealing with various
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Abstract In this chapter, a wrapper-based feature selection algorithm is designed and substantiated based on the binary variant of Dragonfly Algorithm (BDA). DA is a successful, well-established metaheuristic that revealed superior efficacy in dealing with various optimization problems including feature selection. In this chapter we are going first present the inspirations and methamatical modeds of DA in details. Then, the performance of this algorithm is tested on a special type of datasets that contain a huge number of features with low number of samples. This type of datasets makes the optimization process harder, because of the large search space, and the lack of adequate samples to train the model. The experimental results showed the ability of DA to deal with this type of datasets better than other optimizers in the literature. Moreover, an extensive literature review for the DA is provided in this chapter.
M. Mafarja Faculty of Engineering and Technology, Department of Computer Science, Birzeit University, PoBox 14, Birzeit, Palestine e-mail: [email protected] A. A. Heidari School of Surveying and Geospatial Engineering, University of Tehran, Tehran, Iran e-mail: [email protected] H. Faris · I. Aljarah King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan e-mail: [email protected] I. Aljarah e-mail: [email protected] S. Mirjalili (B) Institute of Integrated and Intelligent Systems, Griffith University, Nathan, Brisbane, QLD 4111, Australia e-mail: [email protected] © Springer Nature Switzerland AG 2020 S. Mirjalili et al. (eds.), Nature-Inspired Optimizers, Studies in Computational Intelligence 811, https://doi.org/10.1007/978-3-030-12127-3_4
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1 Introduction Some creatures’ behaviors have been the inspiration source for many successful optimization algorithms. The main behavior that inspired many researchers to develop new algorithms was the strategy that those creatures use to seek the food sources. Ant Colony Optimization (ACO) [25, 26] and Artificial Bees Colony (ABC) [51] were originally inspired by the behavior of ants and bees respectively in locating food sources and collecting their food. Swarming behavior is another source of inspiration that was used to propose new optimization algorithms. Particle Swarm Optimization (PSO) [25] is a primary swarm based optimization algorithm that mimics the swarming behavior of birds. The key issue about all the previously mentioned creatures is that they live in groups or folks (called swarms) [53]. An individual in those swarms usually makes a decision based on local information from itself and from the interactions with the other swarm members, also from the environment. Such interactions are the main reason that contribute to the improvement of the social intelligence in these swarms. Most of the swarms contain different organisms from the same species (bees, ants, birds, etc.). By the intelligent collaboration (or swarm intelligence (SI)) between all individuals
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