Research on Optimization Algorithm of Multi-Objective Cost Control Model in Virtual Design of Intelligent Construction

This study introduces a multi-objective cost management framework grounded in a multi-objective particle swarm optimization (MOPSO) approach. Initially, by establishing a virtual design framework for intelligent construction that incorporates diverse aspects such as financial expenditure, timelines, and resource allocation, a comprehensive multi-objective cost management structure is developed. Subsequently, a refined version of the MOPSO algorithm is crafted to perform complex multi-dimensional optimizations encompassing cost, duration, and quality across various project scenarios, thereby fulfilling the distinct demands of numerous projects. The results from simulation experiments indicate that this algorithm achieves superior convergence precision and solution equilibrium in virtual design contexts, notably enhancing both the convergence rate and optimization precision of the model. Comparative analysis with traditional algorithms and alternative parameter configurations highlights the proposed algorithm's pronounced benefits in terms of objective function performance, allowing for a balanced optimization of multiple objectives and, consequently, achieving a more effective cost management outcome. This research offers an innovative and efficient approach to multi-objective cost management within intelligent construction's virtual design sphere, demonstrating strong potential for practical use and promising application opportunities.