Akademik Veri Yönetim Sistemi
Asian Journal of Civil Engineering, 2024 (Scopus)
Trade-off problem requires a balance between the project objectives taken as time and cost, known as the NP-hard optimization problem. Due to this, any metaheuristic algorithm like the arithmetic optimization algorithm (AOA) gaining popularity for its simplicity and fast convergence might suffer from finding the optimal solution(s) when the construction project scale is increasing. To improve the overall optimization ability and overcome the drawbacks of the plain AOA in solving the time–cost trade-off optimization problems, in this study, the generation jumping phase of the opposition-based learning strategy is proposed and integrated with AOA. This enhancement realizes complementary advantages of the opposition jumping rate to avoid falling into the local optimum and premature convergence. Construction engineering projects involving 63, 81, and 146 activities are applied to verify the effectiveness and feasibility of the enhanced AOA. The experimental results reveal that the proposed model is more effective than the plain AOA and other emerging algorithms for simultaneously optimizing the trade-off problems in construction management.