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A Comparative Analysis of DNA Protein Synthesis for Solving Optimization Problems: A Novel Nature-Inspired Algorithm

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Innovations in Bio-Inspired Computing and Applications (IBICA 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1372))

Abstract

In this paper, we present a new algorithm to find the optimal proteins generated through DNA synthesis. The algorithm executes in five stages: in the first stage, it takes a DNA sequences and consider it as the initial populations of lions, determined the main positions of each lion and the main distances among lions and goal point then consider this distance as fitness of that lions, after that sort the lions based on their fitness to preparing it to the second stage. The second stage develops lion optimization algorithm (LOA) by adding four new features on it, each feature performance one task, a replacing the kernel of LOA (i.e., searching machnizam) by spirally searching & Bubble net searching to increase the accuracy, at the same time reduce the execution time to reach of the goal achieve by A Smart feature. The main purpose of the third stage is determining lion active or more yauld where each lion in population need update the positions and fitness after each move in searching space to reach of their goal., this achieved through Yauld feature. The fourth stage applies the Cooperative features to convert the active sequence of DNA (i.e., Yauld lion) into mRNA after that built tRNA from it after splitting it into triplet to start to generate the proteins. Synthesis of all triplet of tRNA to generated final proteins result by new optimization algorithm achieved based on deep composite that satisfies the four rules, this feature called Deep feature and represent the final stage of the algorithm. The new algorithm appears as a pragmatic optimization model, it proves their robust to work with dynamic length of DNA sequence. It increases accuracy and reduces execution times.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Faculty of Science for Women (SCIW), University of Babylon, Babylon, Iraq

    Samaher Al-Janabi

  2. Department of Chemistry Science, Faculty of Science for Women (SCIW), University of Babylon, Babylon, Iraq

    Ayad F. Alkaim

Authors
  1. Samaher Al-Janabi
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  2. Ayad F. Alkaim
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Corresponding author

Correspondence to Samaher Al-Janabi .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. National Institute of Information and Communications Technology (NICT), Koganei, Tokyo, Japan

    Hideyasu Sasaki

  3. Universidade Federal da Bahia, Salvador, Brazil

    Ricardo Rios

  4. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  5. Institute of Technology and Science, Ghaziabad, Uttar Pradesh, India

    Umang Singh

  6. School of Information Science and Engineering, University of Jinan, Jinan, Shandong, China

    Kun Ma

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Al-Janabi, S., Alkaim, A.F. (2021). A Comparative Analysis of DNA Protein Synthesis for Solving Optimization Problems: A Novel Nature-Inspired Algorithm. In: Abraham, A., Sasaki, H., Rios, R., Gandhi, N., Singh, U., Ma, K. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2020. Advances in Intelligent Systems and Computing, vol 1372. Springer, Cham. https://doi.org/10.1007/978-3-030-73603-3_1

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