Research on Intelligent Optimisation Decision Making Based on NSGA-II Genetic Algorithm and Multiple Regression Model

The aim of this paper is to study how to optimise resource allocation and decision-making under complex constraints to enhance the overall efficiency and economic benefits of the system through multi-objective optimisation and scenario analysis models. We adopt advanced intelligent algorithms and techniques, such as historical data alignment and integration processing, to ensure data consistency and accuracy. During the construction of the optimisation model, a multi-objective optimisation method based on linear programming is introduced to maximise the system revenue and derive the optimal decision-making strategies under different scenarios. To further enhance the robustness and adaptability of the model, we use the Local Sensitive Hashing (LSH) method to sample and cut down the scenarios to efficiently simulate the decision-making behaviours in potentially risky environments. Subsequently, the optimisation process is thoroughly analysed and improved by combining the NSGA-II genetic algorithm and multiple regression model. The results show that the application of computer-related advanced optimisation algorithms can effectively deal with the resource allocation problems in complex scenarios, significantly improve the system effectiveness and economic returns, and provide a powerful support for decision-making optimisation in related fields.