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Modified grasshopper optimization algorithm-based genetic algorithm for global optimization problems: the system of nonlinear equations case study

  • Optimization
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Abstract

Grasshopper optimization algorithm (GOA) is one of the promising optimization algorithms for optimization problems. However, it has the main drawback of trapping into a local minimum, which causes slow convergence or inability to detect a solution. Several modifications and combinations were suggested to overcome this problem. This paper presents a modified grasshopper optimization algorithm (MGOA)-based genetic algorithm to overcome this problem. Modifications rely on certain mathematical assumptions and varying the domain of the control parameter, Cmax, to escape from the local minimum and move the search process to an improved point. Parameter C is one of the essential parameters in GOA, where it balances the exploration and exploitation of the search space. These modifications aim to speed up the convergence rate by reducing the repeated solutions and the number of iterations. Both the original GOA and the proposed algorithms are tested with 19 main test functions to investigate the influence of the proposed modifications. In addition, the algorithm will be applied to solve five different cases of nonlinear systems with different types of dimensions and regularity to show the reliability and efficiency of the proposed algorithm. Promising results are achieved compared to the original GOA. The proposed approach shows an average percentage of improvement of 96.18 as illustrated in the detailed results.

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The authors received no specific funding for this work.

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Authors and Affiliations

  1. Department of Basic Engineering Science, Faculty of Engineering, Menoufia University, Shebin El-Kom, 32511, Egypt

    Hala A. Omar & M. A. El-Shorbagy

  2. Department of Mathematics, College of Science and Humanities in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia

    M. A. El-Shorbagy

Authors
  1. Hala A. Omar
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  2. M. A. El-Shorbagy
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Contributions

MAE contributed to conceptualization; MAE and HAO contributed to investigation, writing—review and editing, have read, and agreed to the published version of the manuscript; and HAO contributed to methodology and writing—original draft.

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Correspondence to M. A. El-Shorbagy.

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Omar, H.A., El-Shorbagy, M.A. Modified grasshopper optimization algorithm-based genetic algorithm for global optimization problems: the system of nonlinear equations case study. Soft Comput 26, 9229–9245 (2022). https://doi.org/10.1007/s00500-022-07219-0

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