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

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