Enhanced multi-objective mountain gazelle optimization via modified adaptive weight approach for construction
time-cost trade-off problems
Tayfun Dede1* (orcid id: 0000-0001-9672-2232)
Mohammad Azim Eirgash1 (orcid id: 0000-0001-5399-115X)
Andrzej Kysiak2 (orcid id: 0000-0002-0842-2051)
Hacı Abdullah Uçan3 (orcid id: 0000-0001-9049-8858)
Miroslav Fabian3
¹ Karadeniz Technical University, Turkey
² Czestochowa University of Technology, Poland
³ Ministry of Environment, Urbanization and Climate Change, General Directorate for Protection of
Natural Assets, Turkey
Article (PDF)
KEYWORDS
time-cost-trade-off problems, modified adaptive weight approach, mountain gazelle
algorithm, multi-criteria decision-making tool
ABSTRACT
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, improv- ing convergence and
Pareto-front quality. The proposed MAWA-MGO is evaluated using a 19-activity construction project
and compared with Multi-Objective Particle Swarm Opti- mization 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 anal- yses 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.
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