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Information acquisition optimizer: a new efficient algorithm for solving numerical and constrained engineering optimization problems

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Abstract

This paper addresses the increasing complexity of challenges in the field of continuous nonlinear optimization by proposing an innovative algorithm called information acquisition optimizer (IAO), which is inspired by human information acquisition behaviors and consists of three crucial strategies: information collection, information filtering and evaluation, and information analysis and organization to accommodate diverse optimization requirements. Firstly, comparative assessments of performance are conducted between the IAO and 15 widely recognized algorithms using the standard test function suites from CEC2014, CEC2017, CEC2020, and CEC2022. The results demonstrate that IAO is robustly competitive regarding convergence rate, solution accuracy, and stability. Additionally, the outcomes of the Wilcoxon signed rank test and Friedman mean ranking strongly validate the effectiveness and reliability of IAO. Moreover, the time comparison analysis experiments indicate its high efficiency. Finally, comparative tests on five real-world optimization difficulties affirm the remarkable applicability of IAO in handling complex issues with unknown search spaces. The code for the IAO algorithm is available at https://ww2.mathworks.cn/matlabcentral/fileexchange/169331-information-acquisition-optimizer.

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The data that support the findings of this study are available from the corresponding author.

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Funding

This work was supported by the National Natural Science Foundation of China (52275480), National Key Research and Development Plan Project (2020YFB171330), Guizhou Provincial Science and Technology Department (QKHZYD[2023]002), and Guiyang Science and Technology Platform Construction Project under Grant (ZKHT[2023]7-2).

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Guizhou University, 550025, Guiyang, People’s Republic of China

    Xiao Wu

  2. State Key Laboratory of Public Big Data, Guizhou University, 550025, Guiyang, People’s Republic of China

    Shaobo Li & Xinghe Jiang

  3. Jiangnan Institute of Mechanical and Electrical Design, 550025, Guiyang, People’s Republic of China

    Yanqiu Zhou

Authors
  1. Xiao Wu
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  2. Shaobo Li
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Contributions

X.W., X.J., and Y.Z. contributed to conceptualization; X.W. and X.J. were involved in methodology and formal analysis; X.W. provided software and contributed to validation, investigation, writing—original draft preparation, and writing—review and editing; S.L. was involved in resources, project administration, and funding acquisition; and S.L., X.J., and Y.Z. contributed to supervision. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Shaobo Li.

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Appendices

Appendix 1

See Tables

Table 10 Experimental results of 12 algorithms on the CEC 2014 (10D)
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Table 11 Experimental results of 12 algorithms on the CEC 2017 (10D)
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Table 12 Experimental results of 12 algorithms on the CEC 2020 (10D)
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Table 13 Experimental results of 12 algorithms on the CEC 2022 (10D)
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Table 14 Experimental results of 12 algorithms on the CEC 2014 (50D)
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Table 15 Experimental results of 12 algorithms on the CEC 2017 (100D)
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Table 16 Experimental results of 12 algorithms on the CEC 2020 (20D)
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Table 17 Experimental results of 12 algorithms on the CEC 2022 (20D)
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Appendix 2

See Tables

Table 18 Welded beam design problem
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18,

Table 19 Multiple disk clutch brake design problem
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Table 20 Weight minimization of a speed reducer problem
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Table 21 Planetary gear train design optimization problem
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Table 22 Robot gripper problem
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22.

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Wu, X., Li, S., Jiang, X. et al. Information acquisition optimizer: a new efficient algorithm for solving numerical and constrained engineering optimization problems. J Supercomput 80, 25736–25791 (2024). https://doi.org/10.1007/s11227-024-06384-3

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