Optimization of Construction Projects Time-Cost-Quality-Environment Trade-off Problem Using Adaptive Selection Slime Mold Algorithm

Document Type : Regular Article


1 Associate Professor, Faculty of Civil Engineering, Ho Chi Minh City University of Technology (HCMUT), Vietnam

2 M.Sc. Student, Faculty of Civil Engineering, Ho Chi Minh City University of Technology (HCMUT), Vietnam


In order to address optimization problems, artificial intelligence (AI) is employed in the construction industry, which aids in the growth and popularization of AI. This study utilizes a Hybrid algorithm called Adaptive Selection Slime Mold Algorithm (ASSMA), which combines the Tournament Selection (TS) and Slime Mould Algorithm (SMA) to address the four-factor optimization problem in projects. This combination will improve the original algorithm's performance, speed up result finding and achieve good convergence via Pareto Front. Hence, efficient resource management must be comprehended in order to optimize the time, cost, quality and environmental impact trade-off (TCQE). Case studies are used to illustrate the capabilities of the new model, and ASSMA results are compared to those of the data envelopment analysis (DEA) method used by the previous researcher. To improve the suggested model's superiority and effectiveness, it is compared to the multiple-target swarm algorithm (MOPSO), multi-objective artificial bee colony (MOABC) and non-dominant sort genetic algorithm (NSGA-II). Based on the overall results, it is clear that the ASSMA model illustrates diversification and offers a robust and convincing optimal solution for readers to understand the potential of the proposed model.


  • The ASSMA recommendation method for time-cost-quality-EI trade-off problem.
  • ASSMA hybrid approach has a strong potential to advance and become more effective than the original algorithm.
  • The proposed model and the previously existing algorithms change, resulting in the viability of the ASSMA model.
  • ASSMA is not only a workable method but also competitive when measured against other academic optimization methods.


Main Subjects

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