Optimization of Software Engineering Supervision Network Based on Genetic Algorithm

This paper proposes an optimization method for the software engineering supervision network based on genetic algorithm. Aiming at the deficiencies of traditional supervision network optimization methods in dynamic adaptability, multi-objective optimization efficiency, and global optimality, a collaborative optimization model covering task scheduling, resource allocation, and risk control is constructed. By employing a double-layer chromosome encoding mechanism and a dynamic fitness function, the comprehensive utility of cost, time, and quality is quantified through the combination of the Pareto frontier theory and the entropy weight method. Additionally, a sliding window mechanism is introduced to update the environmental state in real-time, thereby enhancing the algorithm's adaptability to dynamic changes. Experimental results show that this method outperforms traditional methods in terms of reducing project completion time and improving resource utilization, and it exhibits higher stability in high-disturbance scenarios. It provides theoretical support and a practical paradigm for the intelligent upgrade of software engineering supervision networks.