基于自适应权重和莱维飞行的改进海鸥优化算法

在齿轮系设计问题中, 传统算法存在计算复杂与精度低等缺点, 海鸥优化算法(SOA)得益于其算法原理简单、通用性强、参数少等特性, 现多用于工程设计问题. 然而, 标准海鸥优化算法易出现寻优精度低、搜索速度慢等问题, 本文提出一种混合策略改进的海鸥优化算法(WLSOA). 首先, 利用非线性递减策略增强海鸥优化算法的探索开发能力, 提高寻优精度. 其次, 在海鸥攻击阶段引入自适应权重平衡全局与局部的搜索能力和加入莱维飞行步长对当前最优解进行扰动, 提高算法跳出局部最优值的能力. 然后分别使用WLSOA、黄金正弦算法、鲸鱼优化算法、粒子群优化算法、传统海鸥优化算法及最新提出的改进海鸥优化算法, 通过在9个经典的测试函数上进行仿真实验来探究WLSOA的性能. 结果表明, WLSOA比其他6种算法寻优精度更高, 收敛速度更快. 最后, 在齿轮系设计问题上, 通过与其他13种常见的群智能算法的比较表明, WLSOA的求解性能优于其他算法.

Improved Seagull Optimization Algorithm Based on Adaptive Weight and Levy Flight

In gear train design, traditional algorithms exhibit drawbacks such as computational complexity and low accuracy. The seagull optimization algorithm (SOA) benefits from its simple algorithmic principle, strong universality, and few parameters, and is now commonly used in engineering design problems. However, the standard SOA is prone to problems such as low optimization accuracy and slow search speed. This study proposes a hybrid strategy improved seagull optimization algorithm (WLSOA). Firstly, it utilizes a nonlinear descent strategy to enhance the exploration and development capabilities of the SOA and improve optimization accuracy. Secondly, the adaptive weight balancing of global and local search capabilities and the addition of Levy flight steps to perturb the current optimal solution are introduced to improve the ability of the algorithm to jump out of the local optimal value. The performance of WLSOA is then explored through simulation experiments on 9 classic test functions, using WLSOA, golden sine algorithm, whale optimization algorithm, particle swarm optimization algorithm, traditional seagull optimization algorithm, and the newly proposed improved seagull optimization algorithm. The results show that WLSOA has higher optimization accuracy and faster convergence speed than the other six algorithms. Finally, in gear train design, a comparison with 13 other common swarm intelligence algorithms reveals that WLSOA has a better solving ability than other algorithms.