关键词: 斑马优化算法, 正切搜索, 双曲余弦函数, 野马优化算法, 双种群共生, 竞争交配, 机器人路径规划

Abstract: Aiming at the premature convergence and local optima entrapment issues in the zebra optimization algorithm (ZOA), a tangent search-competitive mating zebra optimization algorithm (TZOA) is proposed. Firstly, the algorithm uses a tangent search strategy to increase the diversity of population to prevent the local optimal solution, and uses the hyperbolic cosine factor as a regulatory parameter to avoid affecting the convergence speed. Secondly, the grazing behavior of the wild horse optimizer algorithm (WHO) and the foraging behavior of the zebra optimization algorithm form a double group symbiotic strategy to improve the global exploration and local convergence capabilities of the early stage of the algorithm. Then, this paper adds a new competitive mating mechanism to further improve the diversity and local exploration scope of population. Finally, the experiment is conducted on the 10, 30 and 50 dimensions of 14 CEC2017 test functions with improving strategies, excellent algorithms in recent years, and improved ZOA algorithms proposed by other authors. The population diversity analysis, Wilcoxon rank sum test, exploration and exploitation analysis, and runtime comparison graphs are used to verify the performance of the algorithm. The experimental results demonstrate that the proposed TZOA exhibits superior optimization performance and higher solution accuracy compared with several other intelligent optimization algorithms. TZOA is simultaneously applied to robot path planning problems, and the results obtained from both simple and complex map tests are the best, further proving the effectiveness of the improved algorithm TZOA.

Key words: zebra optimization algorithm, tangent search, hyperbolic cosine function, wild horse optimizer algorithm, symbiosis of two populations, competitive mating, robot path planning