Optimization of Heliostat Field Layout Using an Improved Multi-Objective Particle Swarm Algorithm

To address multiple objective problems and nonlinear constraints and other complex issues existing in the layout optimization of heliostat fields for concentrating solar power tower plants, this study proposes an improved multi-objective particle swarm algorithm to optimize high-dimensional complex problems in practical engineering. A multi-objective optimization model integrating annual average optical efficiency and heliostat quantity was established, with algorithmic enhancements including dynamic inertia weight adjustment, periodic acceleration coefficient coordination, partitioned reinforcement update, embedded engineering constraints, and crowding distance guidance. Experimental results demonstrate that the proposed algorithm outperforms traditional genetic algorithms in both convergence speed and optimization accuracy. Taking the Gemasolar solar power tower plant in Spain as a case study, the heliostat field was initialized using Campo layout rules. The reliability of optical efficiency calculations was validated through practical engineering benchmarks. After applying IMO-MOPSO, the annual average efficiency of the heliostat field increased from 56.89% to 60.09%, an increase of 3.2%, improvement and verifying the algorithm's effectiveness.