Research on Multi Objective Optimization Control of Complex Industrial Processes Based on Intelligent Optimization Algorithms

With the deepening of Industry 4.0, complex industrial processes show strong coupling, nonlinearity, and dynamic uncertainty, requiring coordinated optimization of production efficiency, quality, energy consumption, and emissions. Traditional control methods are often unable to balance conflicting objectives or adapt to changing operating conditions. This paper first analyzes the core requirements and existing problems of multi-objective optimization control for complex industrial processes, and then improves the multi-objective particle swarm optimization algorithm and Bayesian optimization algorithm. An intelligent optimization control framework integrating the advantages of both algorithms is constructed and verified through mechanism analysis and experimental testing. The proposed fusion algorithm balances convergence speed and solution-set uniformity, and establishes a multi-objective dynamic balance model suitable for complex industrial processes, including advanced electromagnetic manufacturing scenarios such as antenna-material processing and RF component production. In a chemical raw-material proportioning process, the method improves raw-material utilization by 20.0%, reduces unit energy consumption by 15.0%, and enhances control stability and adaptability, confirming its engineering effectiveness and practical applicability.