Research on Scheduling Cooperation and Path Planning of Multi-AGV Based on Slime Moulds Algorithm

Yueming, Qiu; Liu, Xiangde; Zhang, Yi

doi:10.1145/3543303.3543314

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…The HG-SMA approach was evaluated in three workplaces with rectangular, circular, and mixed obstacles, and the results revealed that the suggested algorithm achieved better values for the path distance, smoothness, and stability. Yueming Q et al [153] developed both single/multi-objective methods for the path planning of automatic guided vehicles (AGVs) based on the SMA.…”

Section: Scheduling Optimizationmentioning

confidence: 99%

Advances in Slime Mould Algorithm: A Comprehensive Survey

Wei,

Othman,

Daud

et al. 2024

Biomimetics

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The slime mould algorithm (SMA) is a new swarm intelligence algorithm inspired by the oscillatory behavior of slime moulds during foraging. Numerous researchers have widely applied the SMA and its variants in various domains in the field and proved its value by conducting various literatures. In this paper, a comprehensive review of the SMA is introduced, which is based on 130 articles obtained from Google Scholar between 2022 and 2023. In this study, firstly, the SMA theory is described. Secondly, the improved SMA variants are provided and categorized according to the approach used to apply them. Finally, we also discuss the main applications domains of the SMA, such as engineering optimization, energy optimization, machine learning, network, scheduling optimization, and image segmentation. This review presents some research suggestions for researchers interested in this algorithm, such as conducting additional research on multi-objective and discrete SMAs and extending this to neural networks and extreme learning machining.

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Section: Scheduling Optimizationmentioning

confidence: 99%