Optimization of Power Material Sampling Implementation Process Based on Multi-Objective Optimization Algorithm

At present, there are problems in the random inspection of power materials, such as low inspection efficiency, unreasonable resource allocation, and insufficient coverage. It is urgent to optimize the plan to improve resource utilization and detection effect. Based on the multi-objective optimization algorithm, this paper proposes a method to optimize the implementation process of power material sampling inspection. By constructing a multi-objective function with the goals of minimizing sampling cost, maximizing coverage and optimizing efficiency, an improved genetic algorithm is used to solve it. This study comprehensively analyzes the sampling process, clarifies the key links and constraints, establishes a mathematical model, sets the initial population based on actual scenarios, and improves the crossover and mutation operations to enhance the Pareto optimal solution set search capability and algorithm convergence efficiency. The experiment uses the 2023 sampling data of a certain power company as an example to verify the optimization effect. The results showed that under the condition of unchanged budget, the coverage rate increased by 8%, the efficiency increased by an average of 13.8%, and the cost allocation was more reasonable. Research shows that this method significantly improves the efficiency and coverage of random inspection resources, provides strong support for the lean management of power enterprises, and has the potential to be promoted in other fields of material sampling.