Research on Performance Evaluation of Special Equipment Testing and Inspection Personnel Based on IWOA-SVM

To address the issues of low accuracy and difficult parameter selection in traditional support vector machine (SVM) for intelligent performance evaluation, this paper proposes a performance evaluation model IWOA-SVM, which optimizes SVM using an improved whale algorithm. The model first employs Tent chaotic mapping and pseudo-oppositional learning strategies to enhance the diversity and quality of the initial population, preventing the whale algorithm (WOA) from falling into local optima. Then, it incorporates a differential evolutionary mechanism to strengthen WOA's global optimization capability. Finally, the improved whale algorithm (IWOA) is used to optimize the penalty factor and kernel function parameters of SVM, enabling effective performance evaluation while obtaining optimal parameters. As revealed by the experimental outcomes, IWOA exhibits higher optimization speed and global search ability in the two benchmark test functions; on the performance evaluation data of special equipment testing and inspection personnel, the IWOA-SVM method can improve the evaluation results more effectively.