An improved Jaya-based multi-objective optimization framework for time–cost–quality–safety trade-offs in large-scale construction

This study proposes an improved Jaya-based multi-objective optimization framework for large-scale construction projects, simultaneously addressing time, cost, quality, and safety performance. The model integrates these four interdependent dimensions into a unified decision-making structure and is evaluated using both benchmark test functions and a real civil construction case study. Comparative analyses against the original Jaya algorithm, Particle Swarm Optimization, and the Bat Algorithm demonstrate that the improved Jaya variant consistently achieves superior convergence and more balanced optimization across all objectives. The results highlight the algorithm’s enhanced ability to escape local optima and provide robust trade-off solutions, confirming its effectiveness as a practical tool for time–cost–quality–safety optimization in complex construction environments.