Swarm intelligence optimization algorithms exhibit excellent performance in handling single-objective optimization problems due to their bionic characteristics, but practical engineering applications often involve conflicting multi-objective optimization problems. To mitigate the limitations of the basic dung beetle optimization algorithm in balancing convergence rate, solution diversity, and distribution characteristics for multi-objective scenarios, this work presents an enhanced multi-objective dung beetle optimization algorithm. A dominance degree matrix mechanism is embedded to reduce the count of objective function comparisons in non-dominated sorting, while kernel density estimation crowding degree calculation method is integrated to sustain population diversity. To boost the algorithm’s global search performance, this study further develops an elite opposition-based learning strategy with adaptive learning probability adjustment, enabling the population to explore unexploited search spaces. Finally, comparative experiments are conducted between this algorithm and other algorithms under different multi-objective test functions. The simulation results show that the proposed algorithm has good performance under various indicators, which verifies the applicability and superiority of the algorithm proposed in this paper. Meanwhile, the algorithm developed in this work is implemented to tackle the multi-objective path planning optimization problem for mobile robot, and numerical simulation results verify that the method achieves sound performance in resolving practical engineering issues.
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