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

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.