A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Adaptive opposition slime mold algorithm for time–cost–quality–safety trade-off for construction projects
https://orcid.org/http://orcid.org/0000-0002-9788-0627
https://orcid.org/http://orcid.org/0000-0001-5419-0258
It is challenging to optimize the time–cost–quality–safety (TCQS) trade-off in projects since the variables constantly conflict with one another. This study suggests a hybrid model for TCQS trade-off optimization in construction building in India called the adaptive opposition slime mold algorithm (AOSMA). The initial slime mold algorithm served as the basis for AOSMA, which combines opposition-based learning, one of the well-known techniques to enhance exploration, hasten convergence, and minimize local optimization. In addition, to demonstrate the effectiveness and superiority of AOSMA, the results of the proposed model are contrasted with those of the LHS-based NSGA III algorithm, multi-objective particle swarm optimization (MOPSO), and nondominated sorting genetic algorithm III (NSGA III). Compared to the previous hybrid models, AOSMA exhibits superior diversification and convergence and offers a more reliable optimal solution, according to our findings.
Adaptive opposition slime mold algorithm for time–cost–quality–safety trade-off for construction projects
https://orcid.org/http://orcid.org/0000-0002-9788-0627
https://orcid.org/http://orcid.org/0000-0001-5419-0258
It is challenging to optimize the time–cost–quality–safety (TCQS) trade-off in projects since the variables constantly conflict with one another. This study suggests a hybrid model for TCQS trade-off optimization in construction building in India called the adaptive opposition slime mold algorithm (AOSMA). The initial slime mold algorithm served as the basis for AOSMA, which combines opposition-based learning, one of the well-known techniques to enhance exploration, hasten convergence, and minimize local optimization. In addition, to demonstrate the effectiveness and superiority of AOSMA, the results of the proposed model are contrasted with those of the LHS-based NSGA III algorithm, multi-objective particle swarm optimization (MOPSO), and nondominated sorting genetic algorithm III (NSGA III). Compared to the previous hybrid models, AOSMA exhibits superior diversification and convergence and offers a more reliable optimal solution, according to our findings.
Adaptive opposition slime mold algorithm for time–cost–quality–safety trade-off for construction projects
https://orcid.org/http://orcid.org/0000-0002-9788-0627
https://orcid.org/http://orcid.org/0000-0001-5419-0258
It is challenging to optimize the time–cost–quality–safety (TCQS) trade-off in projects since the variables constantly conflict with one another. This study suggests a hybrid model for TCQS trade-off optimization in construction building in India called the adaptive opposition slime mold algorithm (AOSMA). The initial slime mold algorithm served as the basis for AOSMA, which combines opposition-based learning, one of the well-known techniques to enhance exploration, hasten convergence, and minimize local optimization. In addition, to demonstrate the effectiveness and superiority of AOSMA, the results of the proposed model are contrasted with those of the LHS-based NSGA III algorithm, multi-objective particle swarm optimization (MOPSO), and nondominated sorting genetic algorithm III (NSGA III). Compared to the previous hybrid models, AOSMA exhibits superior diversification and convergence and offers a more reliable optimal solution, according to our findings.
Adaptive opposition slime mold algorithm for time–cost–quality–safety trade-off for construction projects
Asian J Civ Eng
Son, Pham Vu Hong (author) / Khoi, Luu Ngoc Quynh (author)
Asian Journal of Civil Engineering ; 24 ; 1927-1942
2023-11-01
16 pages
Article (Journal)
Electronic Resource
English
Optimization in Construction Management Using Adaptive Opposition Slime Mould Algorithm
| DOAJ | 2023
| Online Contents | 2012