A Gaussian convolutional optimization algorithm with tent chaotic mapping

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.