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. 2024 Aug 29;9(9):519.
doi: 10.3390/biomimetics9090519.

An Adaptive Spiral Strategy Dung Beetle Optimization Algorithm: Research and Applications

Xiong Wang  1 , Yi Zhang  2 , Changbo Zheng  3 , Shuwan Feng  4 , Hui Yu  5 , Bin Hu  6 , Zihan Xie  7
Affiliations

An Adaptive Spiral Strategy Dung Beetle Optimization Algorithm: Research and Applications

Xiong Wang et al. Biomimetics (Basel). .

Abstract

The Dung Beetle Optimization (DBO) algorithm, a well-established swarm intelligence technique, has shown considerable promise in solving complex engineering design challenges. However, it is hampered by limitations such as suboptimal population initialization, sluggish search speeds, and restricted global exploration capabilities. To overcome these shortcomings, we propose an enhanced version termed Adaptive Spiral Strategy Dung Beetle Optimization (ADBO). Key enhancements include the application of the Gaussian Chaos strategy for a more effective population initialization, the integration of the Whale Spiral Search Strategy inspired by the Whale Optimization Algorithm, and the introduction of an adaptive weight factor to improve search efficiency and enhance global exploration capabilities. These improvements collectively elevate the performance of the DBO algorithm, significantly enhancing its ability to address intricate real-world problems. We evaluate the ADBO algorithm against a suite of benchmark algorithms using the CEC2017 test functions, demonstrating its superiority. Furthermore, we validate its effectiveness through applications in diverse engineering domains such as robot manipulator design, triangular linkage problems, and unmanned aerial vehicle (UAV) path planning, highlighting its impact on improving UAV safety and energy efficiency.

Keywords: adaptive strategy; engineering design; optimization algorithm; swarm intelligence; unmanned aerial vehicles.

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Conflict of interest statement

Author Yi Zhang was employed by the company Inellifusion Pty Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
The Gaussian chaotic distribution plot.
Figure 2
Figure 2
The dung beetle’s search trajectory.
Figure 3
Figure 3
The dung beetle’s search trajectory.
Figure 4
Figure 4
Nonlinear weight values.
Figure 5
Figure 5
The ADBO algorithm.
Figure 6
Figure 6
CEC2017 test curve chart (Dim = 30).
Figure 7
Figure 7
CEC2017 test curve chart (Dim = 100).
Figure 8
Figure 8
Multiple improved DBO vs. ADBO (Dim = 30).
Figure 9
Figure 9
Multiple improved DBO vs. ADBO (Dim = 100).
Figure 10
Figure 10
Mechanical arm image.
Figure 11
Figure 11
Mechanical arm convergence plot.
Figure 12
Figure 12
Triangle truss design.
Figure 13
Figure 13
Three-bar truss convergence curve diagram.
Figure 14
Figure 14
Threat cost.
Figure 15
Figure 15
Elevation cost.
Figure 16
Figure 16
Turn angle and climb angle description.
Figure 17
Figure 17
UAV scenarios.
Figure 18
Figure 18
Paths in UAV scenarios.
Figure 19
Figure 19
Overhead perspective of the UAV path.
Figure 20
Figure 20
Iteration graph of the UAV path.
See this image and copyright information in PMC

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