Optimizing Parameters of Support Vector Machines Using an Enhanced Whale Optimization Algorithm

Abstract

To enhance the parameters of the kernel function in the conventional support vector machine (SVM) - σ and penalty factor C - an advanced whale optimization algorithm (IWOA) is introduced within this article to optimize the SVM parameter model (IWOA-SVM). The IWOA algorithm is employed to augment the optimization capability of the original whale optimization algorithm, focusing on three key aspects: Firstly, the chaotic circle mapping technique is utilized to produce the initial positions of the initial whale population, which serves as a foundation for population diversity during the algorithm's global search process; Secondly, an adaptable weight parameter is integrated into the spiral ascent phase of the whale, which reinforces the local exploration capability of IWOA, accelerates its rate of convergence and augments the precision of the algorithm; Lastly, the Cauchy mutation perturbation is employed to alter the current optimal solution, thereby averting the algorithm from being confined to a local optima state. The optimization of parameters for the SVM kernel function is achieved through the Improved Whale Optimization Algorithm, by tuning the kernel function's parameters such as σ and penalty factor C, and then verified on the UCI dataset. In comparison to conventional SVM, Particle Swarm Optimization SVM, Genetic Algorithm Optimization SVM, and Original Whale Algorithm Optimization SVM, IWOA-SVM demonstrates the highest classification accuracy, indicating its effectiveness as an SVM parameter optimization algorithm.