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Advanced arithmetic optimization algorithm for solving mechanical engineering design problems

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  • Jeffrey O Agushaka
  • Absalom E Ezugwu

Abstract

The distributive power of the arithmetic operators: multiplication, division, addition, and subtraction, gives the arithmetic optimization algorithm (AOA) its unique ability to find the global optimum for optimization problems used to test its performance. Several other mathematical operators exist with the same or better distributive properties, which can be exploited to enhance the performance of the newly proposed AOA. In this paper, we propose an improved version of the AOA called nAOA algorithm, which uses the high-density values that the natural logarithm and exponential operators can generate, to enhance the exploratory ability of the AOA. The addition and subtraction operators carry out the exploitation. The candidate solutions are initialized using the beta distribution, and the random variables and adaptations used in the algorithm have beta distribution. We test the performance of the proposed nAOA with 30 benchmark functions (20 classical and 10 composite test functions) and three engineering design benchmarks. The performance of nAOA is compared with the original AOA and nine other state-of-the-art algorithms. The nAOA shows efficient performance for the benchmark functions and was second only to GWO for the welded beam design (WBD), compression spring design (CSD), and pressure vessel design (PVD).

Suggested Citation

  • Jeffrey O Agushaka & Absalom E Ezugwu, 2021. "Advanced arithmetic optimization algorithm for solving mechanical engineering design problems," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-29, August.
  • Handle: RePEc:plo:pone00:0255703
    DOI: 10.1371/journal.pone.0255703
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    Cited by:

    1. Heping Fang & Xiaopeng Fu & Zhiyong Zeng & Kunhua Zhong & Shuguang Liu, 2022. "An Improved Arithmetic Optimization Algorithm and Its Application to Determine the Parameters of Support Vector Machine," Mathematics, MDPI, vol. 10(16), pages 1-20, August.
    2. Mengnan Chen & Yongquan Zhou & Qifang Luo, 2022. "An Improved Arithmetic Optimization Algorithm for Numerical Optimization Problems," Mathematics, MDPI, vol. 10(12), pages 1-27, June.
    3. Ahmed. H. A. Elkasem & Salah Kamel & Mohamed H. Hassan & Mohamed Khamies & Emad M. Ahmed, 2022. "An Eagle Strategy Arithmetic Optimization Algorithm for Frequency Stability Enhancement Considering High Renewable Power Penetration and Time-Varying Load," Mathematics, MDPI, vol. 10(6), pages 1-38, March.
    4. Mahmoud Elsisi & Minh-Quang Tran & Hany M. Hasanien & Rania A. Turky & Fahad Albalawi & Sherif S. M. Ghoneim, 2021. "Robust Model Predictive Control Paradigm for Automatic Voltage Regulators against Uncertainty Based on Optimization Algorithms," Mathematics, MDPI, vol. 9(22), pages 1-19, November.
    5. Jaikumar Shanmuganathan & Aruldoss Albert Victoire & Gobu Balraj & Amalraj Victoire, 2022. "Deep Learning LSTM Recurrent Neural Network Model for Prediction of Electric Vehicle Charging Demand," Sustainability, MDPI, vol. 14(16), pages 1-28, August.
    6. Qingxin Liu & Ni Li & Heming Jia & Qi Qi & Laith Abualigah & Yuxiang Liu, 2022. "A Hybrid Arithmetic Optimization and Golden Sine Algorithm for Solving Industrial Engineering Design Problems," Mathematics, MDPI, vol. 10(9), pages 1-30, May.

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