Leng Yuefeng, Cui Chunlai, Jiang Zhichao
School of Mechanical Engineering, Liaoning Technical University, Fuxin, China.
PLoS One. 2025 Feb 5;20(2):e0318203. doi: 10.1371/journal.pone.0318203. eCollection 2025.
In high-dimensional scenarios, trajectory planning is a challenging and computationally complex optimization task that requires finding the optimal trajectory within a complex domain. Metaheuristic (MH) algorithms provide a practical approach to solving this problem. The Crayfish Optimization Algorithm (COA) is an MH algorithm inspired by the biological behavior of crayfish. However, COA has limitations, including insufficient global search capability and a tendency to converge to local optima. To address these challenges, an Enhanced Crayfish Optimization Algorithm (ECOA) is proposed for robotic arm trajectory planning. The proposed ECOA incorporates multiple novel strategies, including using a tent chaotic map for population initialization to enhance diversity and replacing the traditional step size adjustment with a nonlinear perturbation factor to improve global search capability. Furthermore, an orthogonal refracted opposition-based learning strategy enhances solution quality and search efficiency by leveraging the dominant dimensional information. Additionally, performance comparisons with eight advanced algorithms on the CEC2017 test set (30-dimensional, 50-dimensional, 100-dimensional) are conducted, and the ECOA's effectiveness is validated through Wilcoxon rank-sum and Friedman mean rank tests. In practical robotic arm trajectory planning experiments, ECOA demonstrated superior performance, reducing costs by 15% compared to the best competing algorithm and 10% over the original COA, with significantly lower variability. This demonstrates improved solution quality, robustness, and convergence stability. The study successfully introduces novel population initialization and search strategies for improvement, as well as practical verification in solving the robotic arm path problem. The results confirm the potential of ECOA to address optimization challenges in various engineering applications.
在高维场景中，轨迹规划是一项具有挑战性且计算复杂的优化任务，需要在复杂域内找到最优轨迹。元启发式（MH）算法为解决此问题提供了一种实用方法。小龙虾优化算法（COA）是一种受小龙虾生物行为启发的MH算法。然而，COA存在局限性，包括全局搜索能力不足和易收敛到局部最优解的倾向。为应对这些挑战，提出了一种用于机器人手臂轨迹规划的增强型小龙虾优化算法（ECOA）。所提出的ECOA包含多种新颖策略，包括使用帐篷混沌映射进行种群初始化以增强多样性，并用非线性扰动因子取代传统步长调整以提高全局搜索能力。此外，基于正交折射对立的学习策略通过利用主导维度信息提高了解的质量和搜索效率。此外，在CEC2017测试集（30维、50维、100维）上与八种先进算法进行了性能比较，并通过Wilcoxon秩和检验和Friedman平均秩检验验证了ECOA的有效性。在实际的机器人手臂轨迹规划实验中，ECOA表现出卓越性能，与最佳竞争算法相比成本降低了15%，比原始COA降低了10%，且变异性显著更低。这表明解的质量、鲁棒性和收敛稳定性得到了改善。该研究成功引入了新颖的种群初始化和搜索策略以进行改进，并在解决机器人手臂路径问题方面进行了实际验证。结果证实了ECOA在应对各种工程应用中的优化挑战方面的潜力。
