Application of Intelligent Optimization Algorithm in Employment Service Matching System

This article aims to explore the application of intelligent optimization algorithm in employment service matching system, especially the potential of ant colony optimization (ACO) algorithm in improving the efficiency and quality of employment matching. Aiming at the problems of inaccurate matching and low efficiency in the current employment market, a model of employment service matching system based on ACO algorithm is proposed. By improving the pheromone updating mechanism of ACO algorithm and introducing elite ant strategy, the accurate matching between job seekers and positions is realized. The changes of employment quality indicators before and after the implementation of the optimization strategy are compared through the experimental verification of simulated data sets. The experimental results show that after the implementation of the optimization strategy, the employment quality score of vocational university students has been significantly improved, the matching success rate and salary level have also been improved, and the job satisfaction has also been enhanced. To sum up, this study provides a new idea for the application of intelligent optimization algorithm in the field of employment services, which is helpful to improve the efficiency and quality of employment matching and provide more efficient employment services for job seekers and enterprises.