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Time–cost–quality–CO2 emissions optimization in construction management using slime mold algorithm opposition tournament mutation

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Abstract

The concurrent time–cost–quality–CO2 (TCQC) emission trade-off optimization in projects in urban areas is difficult because the factors always contradict each other. This study proposes a hybrid model—slime mold algorithm opposition tournament mutation (SMAOTM)—for TCQC trade-off optimization in three real construction projects in Vietnam. SMAOTM is inspired by the original slime mold algorithm combined with three well-known methods—opposition-based learning, tournament selection, and mutation and crossover—to improve exploration ability, speed up convergence, and reduce local optimization to find optimal results. However, these combinations increase the complexity to process data in the optimization process. The complexity of the actual projects will help to exploit the potential of the proposed model to bring about a successful project implementation solution. Further, to show the efficiency and superiority of SMAOTM, we compared it with well-known previous hybrid models (MOSGO, MODE, MOPSO, NSGA-II, and CAMODE). Based on our results, SMAOTM shows better diversification and convergence and provides a more robust optimal solution than the previous hybrid models.

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Acknowledgements

For this work, we gratefully recognize the time and facilities provided by Ho Chi Minh City University of Technology (HCMUT), VNU-HCM.

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  1. Faculty of Civil Engineering, Ho Chi Minh City University of Technology (HCMUT), Vietnam National University, Ho Chi Minh, 700000, Vietnam

    Pham Vu Hong Son & Luu Ngoc Quynh Khoi

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  1. Pham Vu Hong Son
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Correspondence to Luu Ngoc Quynh Khoi.

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Son, P.V.H., Khoi, L.N.Q. Time–cost–quality–CO2 emissions optimization in construction management using slime mold algorithm opposition tournament mutation. Soft Comput 27, 12071–12098 (2023). https://doi.org/10.1007/s00500-023-08387-3

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