Fungal growth optimizer: A novel nature-inspired metaheuristic algorithm for stochastic optimization

•Introduces Fungal Growth Optimizer (FGO), inspired by fungal growth behaviors.•Balances exploration and exploitation to address optimization challenges.•Evaluated on four CEC benchmarks and eleven engineering design problems.•Outperforms sixteen state-of-the-art algorithms in majority of test funct...

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Vydané v:Computer methods in applied mechanics and engineering Ročník 437; s. 117825
Hlavní autori: Abdel-Basset, Mohamed, Mohamed, Reda, Abouhawwash, Mohamed
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 15.03.2025
Predmet:
Constrained optimization
Fungal growth optimizer
Metaheuristic
Stochastic optimization
Structural truss bar problems
Fungal growth optimizer
Metaheuristic
Stochastic optimization
Structural truss bar problems
Constrained optimization
ISSN:0045-7825
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Abstract •Introduces Fungal Growth Optimizer (FGO), inspired by fungal growth behaviors.•Balances exploration and exploitation to address optimization challenges.•Evaluated on four CEC benchmarks and eleven engineering design problems.•Outperforms sixteen state-of-the-art algorithms in majority of test functions.•Offers a robust alternative for solving complex stochastic optimization problems. This study presents a new nature-inspired metaheuristic algorithm, known as the fungal growth optimizer (FGO), which is inspired by fungal growth behavior in nature. Fungal growth behavior includes hyphal growth, branching, and spore germination. Hyphal growth behavior replicates hyphal extension and chemotropism to precisely explore the search space, and thus, reach and exploit nutrient-rich regions. This behavior provides a variety of search patterns in FGO, promoting its performance against stagnation into local optima and slow convergence speed. The branching behavior replicates how new hyphal branches from the side of an existing hypha explore the surrounding regions in search of more nutrients, promoting the exploratory operator throughout the optimization process. The final behavior is spore germination, which represents how existing hyphae explore new environments to reach safer and nutrient-rich areas. When spores land in an environment that is rich in moisture and nutrition, they germinate and grow. FGO assumes that spores will land in a random position at the beginning of the optimization process to promote the exploratory operator. As the optimization process is exceeded, this random position is transformed into a position between the best-so-far solution and a random position, promoting the exploitation operator while preventing premature convergence. FGO is evaluated against four well-known Congress on Evolutionary Computation (CEC) benchmarks (CEC2020, CEC2017, CEC2014, and CEC2022) and eleven engineering design problems. In addition, it is compared with fifteen recently proposed algorithms and eleven highly-performing algorithms, such as L-SHADE, LSHADE-cnEpSin, AL-SHADE, mantis search algorithm (MSA), IMODE, AGSK, SOMA_T3A, HyDE-DF, modified LSHADE-SPACMA, SHADE, and LSHADE-SPACMA, to demonstrate its superiority. According to the experimental results, FGO outperforms or is competitive with all of the compared algorithms for the majority of the test functions, implying that it is a high-performing optimizer and a powerful alternative technique for dealing with complex optimization problems. The FGO source code is available on this link https://drive.mathworks.com/sharing/7b881d79-c7cb-4b64-bdfa-99ab7f57d984
AbstractList •Introduces Fungal Growth Optimizer (FGO), inspired by fungal growth behaviors.•Balances exploration and exploitation to address optimization challenges.•Evaluated on four CEC benchmarks and eleven engineering design problems.•Outperforms sixteen state-of-the-art algorithms in majority of test functions.•Offers a robust alternative for solving complex stochastic optimization problems. This study presents a new nature-inspired metaheuristic algorithm, known as the fungal growth optimizer (FGO), which is inspired by fungal growth behavior in nature. Fungal growth behavior includes hyphal growth, branching, and spore germination. Hyphal growth behavior replicates hyphal extension and chemotropism to precisely explore the search space, and thus, reach and exploit nutrient-rich regions. This behavior provides a variety of search patterns in FGO, promoting its performance against stagnation into local optima and slow convergence speed. The branching behavior replicates how new hyphal branches from the side of an existing hypha explore the surrounding regions in search of more nutrients, promoting the exploratory operator throughout the optimization process. The final behavior is spore germination, which represents how existing hyphae explore new environments to reach safer and nutrient-rich areas. When spores land in an environment that is rich in moisture and nutrition, they germinate and grow. FGO assumes that spores will land in a random position at the beginning of the optimization process to promote the exploratory operator. As the optimization process is exceeded, this random position is transformed into a position between the best-so-far solution and a random position, promoting the exploitation operator while preventing premature convergence. FGO is evaluated against four well-known Congress on Evolutionary Computation (CEC) benchmarks (CEC2020, CEC2017, CEC2014, and CEC2022) and eleven engineering design problems. In addition, it is compared with fifteen recently proposed algorithms and eleven highly-performing algorithms, such as L-SHADE, LSHADE-cnEpSin, AL-SHADE, mantis search algorithm (MSA), IMODE, AGSK, SOMA_T3A, HyDE-DF, modified LSHADE-SPACMA, SHADE, and LSHADE-SPACMA, to demonstrate its superiority. According to the experimental results, FGO outperforms or is competitive with all of the compared algorithms for the majority of the test functions, implying that it is a high-performing optimizer and a powerful alternative technique for dealing with complex optimization problems. The FGO source code is available on this link https://drive.mathworks.com/sharing/7b881d79-c7cb-4b64-bdfa-99ab7f57d984
ArticleNumber 117825
Author Mohamed, Reda
Abouhawwash, Mohamed
Abdel-Basset, Mohamed
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  surname: Abdel-Basset
  fullname: Abdel-Basset, Mohamed
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– sequence: 2
  givenname: Reda
  surname: Mohamed
  fullname: Mohamed, Reda
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  givenname: Mohamed
  orcidid: 0000-0003-2846-4707
  surname: Abouhawwash
  fullname: Abouhawwash, Mohamed
  email: saleh1284@mans.edu.eg, mohamed.abouhawwash@kfupm.edu.sa
  organization: Department of Industrial and Systems Engineering, Interdisciplinary Research Center of Smart Mobility and Logistics, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
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Keywords Fungal growth optimizer
Metaheuristic
Stochastic optimization
Structural truss bar problems
Constrained optimization
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Snippet •Introduces Fungal Growth Optimizer (FGO), inspired by fungal growth behaviors.•Balances exploration and exploitation to address optimization...
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SubjectTerms Constrained optimization
Fungal growth optimizer
Metaheuristic
Stochastic optimization
Structural truss bar problems
Title Fungal growth optimizer: A novel nature-inspired metaheuristic algorithm for stochastic optimization
URI https://dx.doi.org/10.1016/j.cma.2025.117825
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