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A novel giant pacific octopus optimizer for real-world engineering problem

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Abstract

Giant Pacific Octopus Optimizer (GPOO), a new swarm computational-intelligence metaheuristic approach, is a natural algorithm based on the behavior of octopuses as they struggle to live in the environment. The GPOO model, which are two primary techniques—searching and attacking prey—are offered for algorithm optimization. The transitions between the exploration and exploitation stages are handled in a way that strikes an appropriate balance. A numerous of tests are carried out to verify the novel optimization process's capacity to identify the best solutions to different optimization issues. The GPOO model is evaluated using 10 complex benchmark functions for CEC2019 and 23 classical benchmark functions. Additionally, the results are compared to other metaheuristic algorithms. Globally, algorithms are also employed to address practical engineering technical issues. The outcomes show that GPOO demonstrates other comparable methods in terms of convergence speed and successfully locates all or most local/global optima.

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No datasets were generated or analysed during the current study.

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Acknowledgements

We acknowledge Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for supporting this study.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Faculty of Civil Engineering, Ho Chi Minh City University of Technology (HCMUT), Vietnam National University (VNU-HCM), Ho Chi Minh City, Vietnam

    Pham Vu Hong Son & Luu Ngoc Quynh Khoi

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  1. Pham Vu Hong Son
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  2. Luu Ngoc Quynh Khoi
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Both authors wrote the main manuscript text andrepared figures. All authors reviewed the manuscript.

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Correspondence to Luu Ngoc Quynh Khoi.

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Son, P.V.H., Khoi, L.N.Q. A novel giant pacific octopus optimizer for real-world engineering problem. Evol. Intel. (2024). https://doi.org/10.1007/s12065-024-00945-4

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