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Nature-Inspired Optimizers pp 23–46Cite as

Ant Lion Optimizer: Theory, Literature Review, and Application in Multi-layer Perceptron Neural Networks

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 811))

Abstract

This chapter proposes an efficient hybrid training technique (ALOMLP) based on the Ant Lion Optimizer (ALO) to be utilized in dealing with Multi-Layer Perceptrons (MLPs) neural networks. ALO is a well-regarded swarm-based meta-heuristic inspired by the intelligent hunting tricks of antlions in nature. In this chapter, the theoretical backgrounds of ALO are explained in details first. Then, a comprehensive literature review is provided based on recent well-established works from 2015 to 2018. In addition, a convenient encoding scheme is presented and the objective formula is defined, mathematically. The proposed training model based on ALO algorithm is substantiated on sixteen standard datasets. The efficiency of ALO is compared with differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), and population-based incremental learning (PBIL) in terms of best, worst, average, and median accuracies. Furthermore, the convergence propensities are monitored and analyzed for all competitors. The experiments show that the ALOMLP outperforms GA, PBIL, DE, and PSO in classifying the majority of datasets and provides improved accuracy results and convergence rates.

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Authors and Affiliations

  1. School of Surveying and Geospatial Engineering, University of Tehran, Tehran, Iran

    Ali Asghar Heidari

  2. King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan

    Hossam Faris & Ibrahim Aljarah

  3. Institute of Integrated and Intelligent Systems, Griffith University, Nathan, Brisbane, QLD, 4111, Australia

    Seyedali Mirjalili

  4. Faculty of Engineering and Technology, Department of Computer Science, Birzeit University, PoBox 14, Birzeit, Palestine

    Majdi Mafarja

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  1. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia

    Seyedali Mirjalili

  2. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia

    Jin Song Dong

  3. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia

    Andrew Lewis

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Heidari, A.A., Faris, H., Mirjalili, S., Aljarah, I., Mafarja, M. (2020). Ant Lion Optimizer: Theory, Literature Review, and Application in Multi-layer Perceptron Neural Networks. In: Mirjalili, S., Song Dong, J., Lewis, A. (eds) Nature-Inspired Optimizers. Studies in Computational Intelligence, vol 811. Springer, Cham. https://doi.org/10.1007/978-3-030-12127-3_3

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