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. 2025 Feb 7;15(1):4655.
doi: 10.1038/s41598-025-88347-z.

An enhanced dung beetle optimizer with multiple strategies for robot path planning

Wei Hu  1 , Qi Zhang  2 , Shan Ye  1
Affiliations

An enhanced dung beetle optimizer with multiple strategies for robot path planning

Wei Hu et al. Sci Rep. .

Abstract

In order to make up for the shortcomings of the original dung beetle optimization algorithm, such as low population diversity, insufficient of global exploration ability, being easy to fall into local optimization and unsatisfactory convergence accuracy, etc. An improved dung beetle optimization algorithm using hybrid multi- strategy is proposed. Firstly, the cubic chaotic mapping approach is used to initialize the population to improve the diversity, expand the search range of the solution space, and enhance the global optimization ability. Secondly, the cooperative search algorithm is utilized to strength communication between individual dung beetles and dung beetle groups in foraging stage to expand the search range of the solution space and enhance the global optimization ability. Thirdly, T-distribution mutation and differential evolutionary variation strategies are introduced to provide perturbation to enhance the diversity of the population and avoid falling into local optimization. Fourthly, the proposed algorithm(named as SSTDBO) is compared with other optimization algorithms, including GODBO, QHDBO, DBO, KOA, NOA, WOA and HHO, by 29 benchmark test functions in CEC2017. The results show that the proposed algorithm has stronger robustness and optimization ability, and algorithm's performance has substantially enhanced. Finally, the proposed algorithm is applied to solve the real-world robot path planning engineering cases, to demonstrate its effectiveness in dealing with real optimization engineering cases, which further verified how noteworthy the enhanced strategy's efficacy and the enhanced algorithm's superiority are in addressing real-world engineering cases.

Keywords: CEC2017; Chaotic mapping strategy; Cooperative Search Algorithm; Differential Evolutionary variation strategies; Dung Beetle Optimizer; T-Distribution variation strategies.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Chaotic sequence generation initialized population results.
Fig. 2
Fig. 2
The convergence curves and search history of the proposed technique and other five algorithms for IEEE CEC2017 benchmark functions.
Fig. 2
Fig. 2
The convergence curves and search history of the proposed technique and other five algorithms for IEEE CEC2017 benchmark functions.
Fig. 2
Fig. 2
The convergence curves and search history of the proposed technique and other five algorithms for IEEE CEC2017 benchmark functions.
Fig. 3
Fig. 3
Boxplots for the proposed technique and other five algorithms for IEEE CEC2017 benchmark functions.
Fig. 3
Fig. 3
Boxplots for the proposed technique and other five algorithms for IEEE CEC2017 benchmark functions.
Fig. 4
Fig. 4
The convergence curves and search history of the proposed technique and other five algorithms for IEEE CEC2017 benchmark functions.
Fig. 4
Fig. 4
The convergence curves and search history of the proposed technique and other five algorithms for IEEE CEC2017 benchmark functions.
Fig. 4
Fig. 4
The convergence curves and search history of the proposed technique and other five algorithms for IEEE CEC2017 benchmark functions.
Fig. 5
Fig. 5
Boxplots for the proposed technique and other five algorithms for IEEE CEC2017 benchmark functions.
Fig. 5
Fig. 5
Boxplots for the proposed technique and other five algorithms for IEEE CEC2017 benchmark functions.
Fig. 6
Fig. 6
The proposed approach’s search history and convergence curves, as well as the remaining algorithms based on the benchmark functions of IEEE CEC2017.
Fig. 6
Fig. 6
The proposed approach’s search history and convergence curves, as well as the remaining algorithms based on the benchmark functions of IEEE CEC2017.
Fig. 6
Fig. 6
The proposed approach’s search history and convergence curves, as well as the remaining algorithms based on the benchmark functions of IEEE CEC2017.
Fig. 7
Fig. 7
Boxplots for the proposed technique and other five algorithms for IEEE CEC2017 benchmark functions.
Fig. 7
Fig. 7
Boxplots for the proposed technique and other five algorithms for IEEE CEC2017 benchmark functions.
Fig. 8
Fig. 8
Path planning results of different algorithms.
Fig. 9
Fig. 9
Path optimization iteration curves of different algorithms.
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