Efficiency management of engineering projects based on particle swarm multi objective optimization algorithm

With the development of information technology, computer technology has been increasingly applied to management. This study aims to address problems such as low efficiency, high cost, and unstable quality in engineering project management. A research proposes an optimization method for engineering project efficiency management based on multi-objective particle swarm optimization algorithm. The research can find a balance between cost, schedule and quality through multi-objective optimization, thereby achieving the maximization of comprehensive benefits. The multi-objective particle swarm algorithm combines a segmented pulse module and a cloud adaptation module. The segmented pulse module is used to improve the global search capability, while the cloud adaptation module achieves fast convergence and global optimization by dynamically adjusting inertia weights. The experimental results showed that the distribution index of the multi-objective particle swarm algorithm was 0.084, and the average convergence index was 0.33. After optimizing the application of a new model in a certain construction project, the cost decreased from 651100 yuan to 456500 yuan. The construction time was reduced from 132 days to 106.19 days. The quality coefficient of the main body increased from 0.78 to 0.96, an increase of 23.08%. This results indicated that the particle swarm optimization algorithm could provide efficient, cost-effective, and high-quality optimization solutions for engineering project management. The new model could effectively reduce the engineering cost, significantly shorten the construction time, and improve the construction quality coefficient. In addition, in medical resource allocation, multi-objective particle swarm optimization algorithm can optimize the resource allocation plan, balancing fairness and efficiency. In transportation planning, multi-objective particle swarm optimization algorithm can optimize path planning, improve transportation efficiency and resource utilization.