Wei Fengtao, Shi Xin, Feng Yue
School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, China.
Biomimetics (Basel). 2024 Aug 12;9(8):486. doi: 10.3390/biomimetics9080486.
Aiming at the problem that the Osprey Optimization Algorithm (OOA) does not have high optimization accuracy and is prone to falling into local optimum, an Improved Osprey Optimization Algorithm Based on a Two-Color Complementary Mechanism for Global Optimization (IOOA) is proposed. The core of the IOOA algorithm lies in its unique two-color complementary mechanism, which significantly improves the algorithm's global search capability and optimization performance. Firstly, in the initialization stage, the population is created by combining logistic chaos mapping and the good point set method, and the population is divided into four different color groups by drawing on the four-color theory to enhance the population diversity. Secondly, a two-color complementary mechanism is introduced, where the blue population maintains the OOA core exploration strategy to ensure the stability and efficiency of the algorithm; the red population incorporates the Harris Hawk heuristic strategy in the development phase to strengthen the ability of local minima avoidance; the green group adopts the strolling and wandering strategy in the searching phase to add stochasticity and maintain the diversity; and the orange population implements the optimized spiral search and firefly perturbation strategies to deepen the exploration and effectively perturb the local optimums, respectively, to improve the overall population diversity, effectively perturbing the local optimum to improve the performance of the algorithm and the exploration ability of the solution space as a whole. Finally, to validate the performance of IOOA, classical benchmark functions and CEC2020 and CEC2022 test sets are selected for simulation, and ANOVA is used, as well as Wilcoxon and Friedman tests. The results show that IOOA significantly improves convergence accuracy and speed and demonstrates high practical value and advantages in engineering optimization applications.
针对鱼鹰优化算法（OOA）优化精度不高且易陷入局部最优的问题，提出了一种基于全局优化双色互补机制的改进鱼鹰优化算法（IOOA）。IOOA算法的核心在于其独特的双色互补机制，该机制显著提高了算法的全局搜索能力和优化性能。首先，在初始化阶段，通过结合逻辑混沌映射和佳点集方法创建种群，并借鉴四色理论将种群划分为四个不同的颜色组，以增强种群多样性。其次，引入双色互补机制，其中蓝色种群保持OOA核心探索策略以确保算法的稳定性和效率；红色种群在开发阶段引入哈里斯鹰启发式策略以增强避免局部极小值的能力；绿色组在搜索阶段采用漫步和徘徊策略以增加随机性并保持多样性；橙色种群分别实施优化螺旋搜索和萤火虫扰动策略以深化探索并有效扰动局部最优，从而提高整体种群多样性，有效扰动局部最优以提高算法性能和整个解空间的探索能力。最后，为验证IOOA的性能，选择经典基准函数以及CEC2020和CEC2022测试集进行仿真，并使用方差分析以及威尔科克森和弗里德曼检验。结果表明，IOOA显著提高了收敛精度和速度，并在工程优化应用中展现出较高的实用价值和优势。
