Research on Move-to-Escape Enhanced Dung Beetle Optimization and Its Applications

Abstract

The dung beetle optimization (DBO) algorithm is acknowledged for its robust optimization capabilities and rapid convergence as an efficient swarm intelligence optimization technique. Nevertheless, DBO, similar to other swarm intelligence algorithms, often gets trapped in local optima during the later stages of optimization. To mitigate this challenge, we propose the Move-to-Escape dung beetle optimization (MEDBO) algorithm in this paper. MEDBO utilizes a good point set strategy for initializing the swarm's initial population, ensuring a more uniform distribution and diminishing the risk of local optima entrapment. Moreover, it incorporates convergence factors and dynamically balances the number of offspring and foraging individuals, prioritizing global exploration initially and local exploration subsequently. This dynamic adjustment not only enhances the search speed but also prevents local optima stagnation. The algorithm's performance was assessed using the CEC2017 benchmark suite, which confirmed MEDBO's significant improvements. Additionally, we applied MEDBO to three engineering problems: pressure vessel design, three-bar truss design, and spring design. MEDBO exhibited an excellent performance in these applications, demonstrating its practicality and efficacy in real-world problem-solving contexts.

Keywords: DBO; combinatorial optimization; discrete optimization; evolutionary algorithms; optimization algorithms.

Figure 1

The value of tan(x).

Figure 2

The components of the dung beetle’s foraging behavior.

Figure 3

The optimal path combining the walk function.

Figure 4

CEC2017 test curves chart (Dim = 30).

Figure 5

CEC2017 test curves chart (Dim = 100).

Figure 6

The convergence curves by the DBO algorithm and other optimizers on benchmark test functions (Dim = 30).

Figure 6

The convergence curves by the DBO algorithm and other optimizers on benchmark test functions (Dim = 30).

Figure 7

The convergence curves by the DBO algorithm and other optimizers on benchmark test functions (Dim = 100).

Figure 8

Extension (compression) spring-design issues.

Figure 9

The tension (compression) spring-design convergence plot.

Figure 10

Triangle truss design.

Figure 11

Three-bar truss convergence curve diagram.

Figure 12

Pressure vessel parameter diagram.

Figure 13

The iteration plot of the pressure vessel problem.