Optimization of Construction Projects Time-Cost-Quality-Environment Trade-off Problem Using Adaptive Selection Slime Mold Algorithm
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...
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| Format: | Article |
| Language: | English |
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Pouyan Press
2024-01-01
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| Series: | Journal of Soft Computing in Civil Engineering |
| Subjects: | |
| Online Access: | https://www.jsoftcivil.com/article_172917_014e32b9f7fb44b2920bbcab5f1539eb.pdf |
| _version_ | 1797368305102618624 |
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| author | Pham Vu Hong Son Luu Ngoc Quynh Khoi |
| author_facet | Pham Vu Hong Son Luu Ngoc Quynh Khoi |
| author_sort | Pham Vu Hong Son |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T17:30:35Z |
| format | Article |
| id | doaj.art-5232b1de74624bb0bf4800699726511f |
| institution | Directory Open Access Journal |
| issn | 2588-2872 |
| language | English |
| last_indexed | 2024-03-08T17:30:35Z |
| publishDate | 2024-01-01 |
| publisher | Pouyan Press |
| record_format | Article |
| series | Journal of Soft Computing in Civil Engineering |
| spelling | doaj.art-5232b1de74624bb0bf4800699726511f2024-01-02T15:39:49ZengPouyan PressJournal of Soft Computing in Civil Engineering2588-28722024-01-018110712510.22115/scce.2023.390042.1622172917Optimization of Construction Projects Time-Cost-Quality-Environment Trade-off Problem Using Adaptive Selection Slime Mold AlgorithmPham Vu Hong Son0Luu Ngoc Quynh Khoi1Associate Professor, Faculty of Civil Engineering, Ho Chi Minh City University of Technology (HCMUT), VietnamM.Sc. Student, Faculty of Civil Engineering, Ho Chi Minh City University of Technology (HCMUT), VietnamIn 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.https://www.jsoftcivil.com/article_172917_014e32b9f7fb44b2920bbcab5f1539eb.pdfadaptive selection slime mold algorithmdata envelopment analysistime-cost-quality-environmental trade-off problemtournament selection |
| spellingShingle | Pham Vu Hong Son Luu Ngoc Quynh Khoi Optimization of Construction Projects Time-Cost-Quality-Environment Trade-off Problem Using Adaptive Selection Slime Mold Algorithm Journal of Soft Computing in Civil Engineering adaptive selection slime mold algorithm data envelopment analysis time-cost-quality-environmental trade-off problem tournament selection |
| title | Optimization of Construction Projects Time-Cost-Quality-Environment Trade-off Problem Using Adaptive Selection Slime Mold Algorithm |
| title_full | Optimization of Construction Projects Time-Cost-Quality-Environment Trade-off Problem Using Adaptive Selection Slime Mold Algorithm |
| title_fullStr | Optimization of Construction Projects Time-Cost-Quality-Environment Trade-off Problem Using Adaptive Selection Slime Mold Algorithm |
| title_full_unstemmed | Optimization of Construction Projects Time-Cost-Quality-Environment Trade-off Problem Using Adaptive Selection Slime Mold Algorithm |
| title_short | Optimization of Construction Projects Time-Cost-Quality-Environment Trade-off Problem Using Adaptive Selection Slime Mold Algorithm |
| title_sort | optimization of construction projects time cost quality environment trade off problem using adaptive selection slime mold algorithm |
| topic | adaptive selection slime mold algorithm data envelopment analysis time-cost-quality-environmental trade-off problem tournament selection |
| url | https://www.jsoftcivil.com/article_172917_014e32b9f7fb44b2920bbcab5f1539eb.pdf |
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