With the acceleration of urbanization

intelligent buildings are confronted with the challenges of low collaborative efficiency among multiple systems (such as HVAC

lighting

security

etc.) and insufficient adaptability to dynamic environments. This study proposes a multi-objective collaborative optimization framework based on swarm intelligence. By simulating the pheromone feedback mechanism of ant colonies and the dynamic optimization strategy of particle swarms

a three-tier collaborative architecture of "perception-decision-execution" is constructed to achieve self-organizing optimization among building subsystems. Experiments show that in the application of this strategy in typical commercial complexes

energy consumption is reduced by 22.3% (compared to traditional PID control)

user comfort is improved by 18.7% (PMV index optimization)

and the fault response time is shortened by 40%. The experimental results provide both theoretical support and practical paradigms for multi-system collaborative optimization in complex building environments.