Qi Yanying, Jiang Aipeng, Gao Yuhang
Hangzhou Dianzi University, Baiyang Street, Hangzhou, 310018, China.
Sci Rep. 2024 Dec 28;14(1):31027. doi: 10.1038/s41598-024-82277-y.
To solve the problems of the traditional convolution optimization algorithm (COA), which are its slow convergence speed and likelihood of falling into local optima, a Gaussian mutation convolution optimization algorithm based on tent chaotic mapping (TCOA) is proposed in this article. First, the tent chaotic strategy is employed for the initialization of individual positions to ensure a uniform distribution of the population across a feasible search space. Subsequently, a Gaussian convolution kernel is used for an extensive depth search within the search space to mitigate the likelihood of any individuals converging to a local optimum. The proposed approach is validated by simulation using 23 benchmark functions and six recent evolutionary algorithms. The simulation results show that the TCOA achieves superior results in low-dimensional optimization problems and solves practical, spring-related industrial design problems. This algorithm has important applications to solving optimization problems.
为了解决传统卷积优化算法（COA）收敛速度慢和易陷入局部最优的问题，本文提出了一种基于帐篷混沌映射的高斯变异卷积优化算法（TCOA）。首先，采用帐篷混沌策略对个体位置进行初始化，以确保种群在可行搜索空间内均匀分布。随后，使用高斯卷积核在搜索空间内进行广泛的深度搜索，以降低个体收敛到局部最优的可能性。通过使用23个基准函数和六种最新进化算法进行仿真验证了所提方法。仿真结果表明，TCOA在低维优化问题中取得了优异的结果，并解决了与弹簧相关的实际工业设计问题。该算法在解决优化问题方面具有重要应用。
