基于翻筋斗觅食的自适应蜣螂优化算法及应用

Somersault Foraging Adaptive Dung Beetle Optimization Algorithm and application

关键词:  蜣螂优化算法  蝠鲼觅食优化算法  精英反向学习  翻筋斗觅食  自适应策略  三维无人机路径规划

Key words:  Dung beetle optimization algorithm  Manta ray foraging optimization algorithm  Elite opposition-based learning  Somersault foraging  Adaptive strategy  Three-Dimensional unmanned aerial vehicle (UAV) path planning

摘要:

针对蜣螂优化算法(Dung Beetle Optimization Algorithm, DBO)存在收敛速度慢,勘探和开发不平衡和易陷入局部最优等不足,提出一种基于翻筋斗觅食的自适应蜣螂优化算法(Somersault Foraging Adaptive Dung Beetle Optimization Algorithm, SFADBO)。该算法使用精英反向学习策略作为初始种群的生成方法,产生更具有多样性的初始种群；针对算法勘探和开发不平衡的问题,使用自适应策略随着种群迭代动态调整滚球蜣螂和孵化球的数量；受蝠鲼觅食优化算法(MRFO)的启发,使用MRFO的翻筋斗觅食策略对最优个体位置进行扰动,增强种群跳出局部最优的能力。为验证改进策略的有效性,采用SFADBO算法对23个基准测试函数进行寻优实验,结果表明,SFADBO算法具有更优的求解精度和稳定性,在测试函数上的寻优结果优于DBO算法。最后,将SFADBO算法应用于三维无人机路径规划的实际应用问题中,得到了较好的优化路径,研究表明本文提出的SFADBO算法适用于实际寻优问题,且性能更优。

Abstract:

Aiming at the shortcomings of the Dung Beetle Optimization Algorithm (DBO), such as slow convergence speed, imbalance between exploration and exploitation, and susceptibility to local optima, a Somersault Foraging Adaptive Dung Beetle Optimization Algorithm (SFADBO) is proposed. This algorithm utilizes an elite opposition-based learning strategy as the method for generating the initial population, resulting in a more diverse initial population. To address the imbalance between exploration and exploitation in the algorithm, an adaptive strategy is employed to dynamically adjust the number of dung beetles and eggs with each iteration of the population. Inspired by the Manta Ray Foraging Optimization (MRFO) algorithm, we utilize its somersault foraging strategy to perturb the position of the optimal individual, thereby enhancing the algorithm"s ability to escape from local optima. To verify the effectiveness of the proposed improvements, the SFADBO algorithm is used to optimize 23 benchmark test functions. The results show that the SFADBO algorithm achieves better solution accuracy and stability, outperforming the DBO algorithm in terms of optimization results on the test functions. Finally, the SFADBO algorithm is applied to the practical problem of 3D unmanned aerial vehicle (UAV) path planning, resulting in a well-optimized path. The research demonstrates that the proposed SFADBO algorithm is suitable for practical optimization problems and exhibits superior performance.