Quadruple parameter adaptation growth optimizer with integrated distribution, confrontation, and balance features for optimization

Gao, Hao; Zhang, Qingke; Bu, Xianglong; Zhang, Huaxiang

doi:10.1016/j.eswa.2023.121218

Cited by 9 publications

(1 citation statement)

References 107 publications

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“…Relevant studies have shown that GO has strong exploration capability and application scalability. For example, GO has been successfully applied to the parameter identification of solar photovoltaic cells [29,30], multi-level threshold image segmentation and wireless sensor network node deployment [31], and enhancing intrusion detection systems in the internet of things and cloud environments [32].…”

Section: Introductionmentioning

confidence: 99%

Multi-Strategy-Improved Growth Optimizer and Its Applications

Xie,

Yu,

et al. 2024

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The growth optimizer (GO) is a novel metaheuristic algorithm designed to tackle complex optimization problems. Despite its advantages of simplicity and high efficiency, GO often encounters localized stagnation when dealing with discretized, high-dimensional, and multi-constraint problems. To address these issues, this paper proposes an enhanced version of GO called CODGBGO. This algorithm incorporates three strategies to enhance its performance. Firstly, the Circle-OBL initialization strategy is employed to enhance the quality of the initial population. Secondly, an exploration strategy is implemented to improve population diversity and the algorithm’s ability to escape local optimum traps. Finally, the exploitation strategy is utilized to enhance the convergence speed and accuracy of the algorithm. To validate the performance of CODGBGO, it is applied to solve the CEC2017, CEC2020, 18 feature selection problems, and 4 real engineering optimization problems. The experiments demonstrate that the novel CODGBGO algorithm effectively addresses the challenges posed by complex optimization problems, offering a promising approach.

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