Optimizing Multiobjective Time–Cost–Quality Problems in Construction Projects: Efficacy of Strength Pareto-Based Rao Algorithms

In construction project management, the optimization of time, cost, and quality trade-off is essential for maximizing overall project benefits. While various algorithms have been proposed to address this multiobjective optimization problem, there remains a significant research gap in the efficiency and effectiveness of selection operators in guiding the search process toward optimal solutions. Additionally, many existing methods are computationally expensive, making them impractical for large-scale projects. Therefore, this study proposes use of strength Pareto-based Rao algorithms (SP2-Rao-1 and -2) as an approach to solving multiobjective time–cost–quality problems in construction projects with better optimization performance at lower computation costs. Furthermore, to explore the impact of the selection operators that determine the candidates for subsequent iterations in multiobjective optimization tasks, Rao algorithms with nondominated sorting and a crowding distance operator (NDSII-Rao-1 and -2) were also implemented. The performance of the strength Pareto-based Rao algorithms was evaluated using two case studies with 18 and 60 activities, respectively. The developed SP2-Rao series were compared with opposition-based multiple objective differential evolution (OMODE) and multiobjective artificial bee colony with differential evolution (MOABCDE) algorithms, which have previously been used to solve similar problems. The findings indicated that the SP2-Rao series surpassed earlier techniques in addressing multiobjective construction time–cost–quality problems, achieving greater efficiency and effectiveness with reduced function evaluations. Moreover, the SP2-Rao algorithms produced significantly better results compared to the NDSII-Rao algorithms.