Zhang Yufei, Wang Limin, Zhao Jianping
School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China.
School of Information Science, Guangdong University of Finance and Economics, Guangzhou 510320, China.
Biomimetics (Basel). 2023 Aug 10;8(4):355. doi: 10.3390/biomimetics8040355.
To solve the problems of low convergence accuracy, slow speed, and common falls into local optima of the Chicken Swarm Optimization Algorithm (CSO), a performance enhancement strategy of the CSO algorithm (PECSO) is proposed with the aim of overcoming its deficiencies. Firstly, the hierarchy is established by the free grouping mechanism, which enhances the diversity of individuals in the hierarchy and expands the exploration range of the search space. Secondly, the number of niches is divided, with the hen as the center. By introducing synchronous updating and spiral learning strategies among the individuals in the niche, the balance between exploration and exploitation can be maintained more effectively. Finally, the performance of the PECSO algorithm is verified by the CEC2017 benchmark function. Experiments show that, compared with other algorithms, the proposed algorithm has the advantages of fast convergence, high precision and strong stability. Meanwhile, in order to investigate the potential of the PECSO algorithm in dealing with practical problems, three engineering optimization cases and the inverse kinematic solution of the robot are considered. The simulation results indicate that the PECSO algorithm can obtain a good solution to engineering optimization problems and has a better competitive effect on solving the inverse kinematics of robots.
为了解决鸡群优化算法（CSO）收敛精度低、速度慢以及易陷入局部最优等问题，提出了一种鸡群优化算法性能增强策略（PECSO），旨在克服其不足。首先，通过自由分组机制建立层次结构，增强层次结构中个体的多样性，扩大搜索空间的探索范围。其次，以母鸡为中心划分小生境数量。通过在小生境个体间引入同步更新和螺旋学习策略，能更有效地保持探索与利用之间的平衡。最后，利用CEC2017基准函数验证了PECSO算法的性能。实验表明，与其他算法相比，该算法具有收敛速度快、精度高和稳定性强的优点。同时，为了研究PECSO算法处理实际问题的潜力，考虑了三个工程优化案例和机器人逆运动学求解。仿真结果表明，PECSO算法能够获得工程优化问题的良好解，在求解机器人逆运动学方面具有较好的竞争效果。
