Low-Carbon and Energy-Saving Path Optimization Scheduling of Material Distribution in Machining Shop Based on Business Compass Model

Xiao, Yongmao; Zhang, Hao; Wang, Ruping

doi:10.3390/pr11071960

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…It can be observed that dung beetles utilize the sun for navigation, with the arrow indicating the rolling direction [32]. Assuming that the intensity of the light source also influences the rolling path of dung beetles, the position of the roller beetle is updated and can be represented as follows [33]: Assuming that the intensity of the light source also influences the rolling path of dung beetles, the position of the roller beetle is updated and can be represented as follows [33]:…”

Section: Roller Beetlementioning

confidence: 99%

Enhancing Swarm Intelligence for Obstacle Avoidance with Multi-Strategy and Improved Dung Beetle Optimization Algorithm in Mobile Robot Navigation

Li,

Liu,

Shao

et al. 2023

Electronics

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The Dung Beetle Optimization (DBO) algorithm is a powerful metaheuristic algorithm that is widely used for optimization problems. However, the DBO algorithm has limitations in balancing global exploration and local exploitation capabilities, often leading to getting stuck in local optima. To overcome these limitations and address global optimization problems, this study introduces the Multi-Strategy and Improved DBO (MSIDBO) Algorithm. The MSIDBO algorithm incorporates several advanced computational techniques to enhance its performance. Firstly, it introduces a random reverse learning strategy to improve population diversity and mitigate early convergence or local stagnation issues present in the DBO algorithm. Additionally, a fitness-distance balancing strategy is employed to better manage the trade-off between diversity and convergence within the population. Furthermore, the algorithm utilizes a spiral foraging strategy to enhance precision, promote strong exploratory capabilities, and prevent being trapped in local optima. To further enhance the global search ability and particle utilization of the MSIDBO algorithm, it combines the Optimal Dimension-Wise Gaussian Mutation strategy. By minimizing premature convergence, population diversity is increased, and the convergence of the algorithm is accelerated. This expansion of the search space reduces the likelihood of being trapped in local optima during the evolutionary process. To demonstrate the effectiveness of the MSIDBO algorithm, extensive experiments are conducted using benchmark test functions, comparing its performance against other well-known metaheuristic algorithms. The results highlight the feasibility and superiority of MSIDBO in solving optimization problems. Moreover, the MSIDBO algorithm is applied to path planning simulation experiments to showcase its practical application potential. A comparison with the DBO algorithm shows that MSIDBO generates shorter and faster paths, effectively addressing real-world application problems.

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Section: Roller Beetlementioning

confidence: 99%

Enhancing Swarm Intelligence for Obstacle Avoidance with Multi-Strategy and Improved Dung Beetle Optimization Algorithm in Mobile Robot Navigation

Li,

Liu,

Shao

et al. 2023

Electronics

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“…Compared to the grey wolf algorithm, whale algorithm, and sparrow algorithm, DBO holds advantages such as robust optimization ability, rapid convergence, high solution accuracy, and robustness. Scholars have used these advantages to solve complex parameter optimization problems [22,23]. Consequently, DBO is utilized for optimizing VMD's parameters.…”

Section: Introductionmentioning

confidence: 99%

Compound fault diagnosis of rolling bearings based on AVMD and IMOMEDA

Lu,

Yan,

Wang

et al. 2024

Meas. Sci. Technol.

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The intricate nature of compound fault diagnosis in rolling bearings during nonstationary operations poses a challenge. To address this, a novel technique combines adaptive variational mode decomposition (AVMD) with improved multipoint optimal minimum entropy deconvolution adjustment (IMOMEDA). The compound fault signal is isolated through AVMD, with internal parameters obtained via a new indicator termed integrated fault-impact measure index (IFIMI) guiding the improved dung beetle optimizer (IDBO). An adaptive selection method, using a weight factor, chooses the intrinsic mode function (IMF) containing principal fault data. IMOMEDA whose key parameters are determined by a novel combinatorial strategy is then employed to deconvolute selected fault components, enhancing periodic fault impulses by removing complex interferences and ambient noise. The deconvoluted signal undergoes enhanced envelope spectrum processing to extract fault frequencies and identify fault types. Numerical simulations and experimental data confirm the method’s effectiveness and feasibility for compound faults diagnosis of rolling bearings, showcasing its superiority over existing techniques.

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