Moss Growth Optimization (MGO): Concepts and performance

版本 1.0.0 (3.1 MB) 作者: Ali Asghar Heidari
MATLAB source code of The Moss Growth Optimization (MGO): Concepts and performance
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The moss growth optimization (MGO), introduced in this paper, is an algorithm inspired by the moss growth in the natural environment. The MGO algorithm initially determines the evolutionary direction of the population through a mechanism called the determination of wind direction, which employs a method of partitioning the population. Meanwhile, drawing inspiration from the asexual reproduction, sexual reproduction, and vegetative reproduction of moss, two novel search strategies, namely spore dispersal search and dual propagation search, are proposed for exploration and exploitation, respectively. Finally, the cryptobiosis mechanism alters the traditional metaheuristic algorithm's approach of directly modifying individuals' solutions, preventing the algorithm from getting trapped in local optima. In experiments, a thorough investigation is undertaken on the characteristics, parameters, and time cost of the MGO algorithm to enhance the understanding of MGO. Subsequently, MGO is compared with ten original and advanced CEC 2017 and CEC 2022 algorithms to verify its performance advantages. Lastly, this paper applies MGO to four real-world engineering problems to validate its effectiveness and superiority in practical scenarios. The results demonstrate that MGO is a promising algorithm for tackling real challenges.

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Zheng, Boli, et al. “The Moss Growth Optimization (MGO): Concepts and Performance.” Journal of Computational Design and Engineering, Oxford University Press (OUP), Sept. 2024, doi:10.1093/jcde/qwae080.

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