Enhanced multi-objective mountain gazelle optimization via modified adaptive weight approach for construction time-cost trade-off problems

This study presents an enhanced multi-objective Mountain Gazelle Optimizer integrated with a Modified Adaptive Weight Approach (MAWA) to solve construction time-cost trade-off problems. The MAWA mechanism adaptively balances exploration and exploitation, improving convergence and Pareto-front quality. The proposed MAWA-MGO is evaluated using a 19-activity construction project and compared with Multi-Objective Particle Swarm Optimization and plain MGO. Performance is assessed using hypervolume, spread, and a number of function evaluations. Results show that MAWA-MGO achieves the highest hypervolume (0.697) with substantially reduced computational effort (27 % normalized NFE), indicating superior convergence and efficiency while maintaining competitive diversity. Statistical analyses further confirm improved robustness, with lower variability in both project duration and cost. A crowding-distance-based decision-making approach is applied to identify balanced scheduling solutions, demonstrating the practical applicability of the proposed method in construction project management.