Application of Improved Discrete Particle Swarm Optimization (IDPSO) Algorithm in Optimizing Surface Stress Distribution of Building Composite laminates

Composite laminates are increasingly used in building structures due to their high strength-to-weight ratio, corrosion resistance, and design flexibility. However, achieving a uniform surface stress distribution while maintaining structural stability under realistic service loads remains a significant challenge. This study proposes a novel integrated optimization framework combining the Finite Element Method (FEM) with an Improved Discrete Particle Swarm Optimization (IDPSO) algorithm to optimize the stacking sequence of laminated composite panels used in building applications. The novelty of the proposed approach lies in the integration of a discrete swarm-based optimization strategy with a parametric FEM model to simultaneously minimize maximum surface stress and surface stress variance, thereby improving stress uniformity while satisfying structural safety constraints. The laminate design variables, including ply orientation, thickness, and stacking sequence, are discretized to enable efficient exploration of the complex design space using the IDPSO algorithm. The FEM model evaluates structural responses under realistic building loads such as dead, service, and wind loads. Results show that the optimized laminate configuration achieves a 24.6% reduction in maximum surface stress and a 31.2% decrease in surface stress variance compared to the baseline design. Additionally, structural performance improves with a 10.4% reduction in mid-span deflection and a 16.4% increase in buckling load factor, demonstrating enhanced stiffness and stability. The proposed FEM-IDPSO framework also improves computational efficiency, reducing optimization runtime by 27% compared with conventional optimization methods. These findings confirm that the proposed approach provides an effective tool for designing structurally efficient and reliable composite laminates for building applications.