Multi Objective Decision-Making in Construction Engineering Based on Particle Swarm Optimization Intelligent Optimization Control

Abstract

The emergence of artificial intelligence technology provides scientific decision-making and digital solutions for construction engineering. A collaborative decision-making method based on total quality control and improved particle swarm optimization algorithm is proposed for multi-objective optimization problems in construction engineering, including duration, cost, and quality. By integrating the concept of comprehensive quality control into the improved particle swarm algorithm framework, the objective functions of construction project duration, cost, and quality were analyzed, and a multi-objective optimization model was constructed. Chaos mapping and Gaussian mutation were introduced to enhance the convergence and solution set distribution of the algorithm. The findings show that the improved particle swarm algorithm exhibits good convergence and can escape from local optima while meeting accuracy requirements. Moreover, the research method has significantly improved the solution set quality (0.82) and distribution uniformity (0.15) compared to the baseline model. And the running time of the research method is less than 30 seconds, and the quality scores are all greater than 0.80, indicating good performance. Research methods can effectively improve the efficiency of construction projects, reduce resource waste, and enhance the quality of multi-objective decision-making.