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Chaotic Variants of Grasshopper Optimization Algorithm and Their Application to Protein Structure Prediction

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Applied Nature-Inspired Computing: Algorithms and Case Studies

Part of the book series: Springer Tracts in Nature-Inspired Computing ((STNIC))

Abstract

It is a known fact that protein structure prediction is a challenging problem of computational biology. In the past, several attempts of application of metaheuristic approaches have been witnessed in this area. Deriving motivation from the literature, this chapter is the application proposal of chaotic variants of Grasshopper Optimization Algorithm (GOA) for solving protein folding optimization problem, which is applied to AB-OFF lattice model. The variants incorporate ten different chaotic maps in bridging mechanism of GOA between exploratory and exploitative states. These variants are named as Enhanced Chaotic Grasshopper Optimization Algorithms (ECGOAs). The variants are tested over artificial protein sequences and some real protein sequences for obtaining native protein structure. The performance measures of the variants are the mean, standard deviation, and best values of free energy values obtained from independent runs of optimization process. Further, different statistical tests, including Wilcoxon Rank-Sum test and trajectory analysis are conducted to exhibit the efficacy of the proposed variants. It is observed that proposed variants show better performance and proposed chaotic bridging mechanism enhances the performance of the algorithm.

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Acknowledgements

The authors acknowledge the support and encouragement provided by the authorities of the Malaviya National Institute of Technology, Jaipur, and Swami Keshvanand Institute of Technology, Jaipur.

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Authors and Affiliations

  1. Department of Electrical Engineering, Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur, Rajasthan, India

    Akash Saxena

  2. Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Rajesh Kumar

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  1. Akash Saxena
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Corresponding author

Correspondence to Rajesh Kumar .

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Editors and Affiliations

  1. Department of Information Technology, Techno India College of Technology, Kolkata, West Bengal, India

    Nilanjan Dey

  2. Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt

    Amira S. Ashour

  3. Research and Development and Academic Affairs, RCC Institute of Information Technology, Kolkata, India

    Siddhartha Bhattacharyya

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Saxena, A., Kumar, R. (2020). Chaotic Variants of Grasshopper Optimization Algorithm and Their Application to Protein Structure Prediction. In: Dey, N., Ashour, A., Bhattacharyya, S. (eds) Applied Nature-Inspired Computing: Algorithms and Case Studies. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-13-9263-4_7

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