School of Science, University of Science and Technology Liaoning, Anshan 114051, China.
Comput Intell Neurosci. 2022 Aug 1;2022:5692427. doi: 10.1155/2022/5692427. eCollection 2022.
This paper proposed a fast convergence and balanced adolescent identity search algorithm (FCBAISA) for numerical and engineering design problems. The main contributions are as follows. Firstly, a hierarchical optimization strategy is proposed to balance the exploration and exploitation better. Secondly, a fast search strategy is proposed to avoid the local optimization and improve the accuracy of the algorithm; that is, the current optimal solution combines with the random disturbance of Brownian motion to guide other adolescents. Thirdly, the Chebyshev functional-link network (CFLN) is improved by recursive least squares estimation (RSLE), so as to find the optimal solution more effectively. Fourthly, the terminal bounce strategy is designed to avoid the algorithm falling into local optimization in the later stage of iteration. Fifthly, FCBAISA and comparison algorithms are tested by CEC2017 and CEC2022 benchmark functions, and the practical engineering problems are solved by algorithms above. The results show that FCBAISA is superior to other algorithms in all aspects and has high precision, fast convergence speed, and excellent performance.
本文提出了一种快速收敛和平衡青少年身份搜索算法（FCBAISA），用于数值和工程设计问题。主要贡献如下。首先，提出了一种分层优化策略，以更好地平衡探索和开发。其次，提出了一种快速搜索策略，以避免局部优化并提高算法的准确性；即当前最优解与布朗运动的随机干扰相结合，以指导其他青少年。第三，通过递归最小二乘估计（RSLE）改进 Chebyshev 功能链接网络（CFLN），以更有效地找到最优解。第四，设计终端反弹策略以避免算法在迭代后期陷入局部优化。第五，通过 CEC2017 和 CEC2022 基准函数对 FCBAISA 和比较算法进行测试，并使用上述算法解决实际工程问题。结果表明，FCBAISA 在各个方面都优于其他算法，具有高精度、快速收敛速度和优异的性能。
