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<p>This paper presents a novel nature-inspired optimization algorithm, the so-called water uptake and transport in plants (WUTP) algorithm, which can be used to solve numerical optimization problems. The basic thoughts and principles underlying the WUTP algorithm are inspired by modeling the process of water movement within plant membranes and how water flows from soil to plant roots and then to the plant leaves. These observations are mathematically designed to focus attention on exploration and exploitation of water flow process within plants in a particular search space. More importantly, WUTP can search the entire search area accurately with reasonable convergence speed. The performance of the proposed WUTP is entirely examined on a set of 23 well-known benchmark functions with varied levels of complexity. Further, the robustness and reliability of the WUTP algorithm are comprehensively evaluated on a collection of 29 well-known functions from the CEC-2017 benchmark for a diversity of dimensions. This test includes unimodal, multimodal, hybrid, and composition test functions with various levels of complexity. To show the reliability and appropriateness of WUTP in real-world problems, it is also applied to solve benchmark problems for nine engineering designs. A comprehensive study of the results of computational, qualitative, quantitative, and statistical analysis is presented to illustrate the efficacy and stability degrees of the proposed algorithm. The stability of the WUTP algorithm was tested in both exploration and exploitation, and the performance of WUTP was verified using several evaluation measures. The effectiveness of the proposed algorithm was further compared with several highly well-thought-of optimization methods based on the generated solutions and convergence rates. The obtained results demonstrate that WUTP generates better solutions, in terms of global optimality, solution accuracy, and reliability, in the majority of test problems compared to other promising optimization algorithms.</p>

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A novel meta-heuristic optimization algorithm inspired by water uptake and transport in plants

  • 期刊论文

  • 发表日期：

    2025年5月2日

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  • Malik Braik,
  • Heba Al-Hiary

摘要

This paper presents a novel nature-inspired optimization algorithm, the so-called water uptake and transport in plants (WUTP) algorithm, which can be used to solve numerical optimization problems. The basic thoughts and principles underlying the WUTP algorithm are inspired by modeling the process of water movement within plant membranes and how water flows from soil to plant roots and then to the plant leaves. These observations are mathematically designed to focus attention on exploration and exploitation of water flow process within plants in a particular search space. More importantly, WUTP can search the entire search area accurately with reasonable convergence speed. The performance of the proposed WUTP is entirely examined on a set of 23 well-known benchmark functions with varied levels of complexity. Further, the robustness and reliability of the WUTP algorithm are comprehensively evaluated on a collection of 29 well-known functions from the CEC-2017 benchmark for a diversity of dimensions. This test includes unimodal, multimodal, hybrid, and composition test functions with various levels of complexity. To show the reliability and appropriateness of WUTP in real-world problems, it is also applied to solve benchmark problems for nine engineering designs. A comprehensive study of the results of computational, qualitative, quantitative, and statistical analysis is presented to illustrate the efficacy and stability degrees of the proposed algorithm. The stability of the WUTP algorithm was tested in both exploration and exploitation, and the performance of WUTP was verified using several evaluation measures. The effectiveness of the proposed algorithm was further compared with several highly well-thought-of optimization methods based on the generated solutions and convergence rates. The obtained results demonstrate that WUTP generates better solutions, in terms of global optimality, solution accuracy, and reliability, in the majority of test problems compared to other promising optimization algorithms.