| Title: |
Optimal design of diversion dams based on Upgraded Multi-Objective Particle Swarm Optimization (UMOPSO). |
| Authors: |
Naderi, Shahab; Shabanlou, Saeid; Javaheri-Tafti, Mohammad Reza; Yaghoubi, Behrouz |
| Source: |
International Journal of Construction Management; Jun2025, Vol. 25 Issue 8, p876-886, 11p |
| Subject Terms: |
Diversion structures; Particle swarm optimization; Multidisciplinary design optimization; Mathematical programming; Multiple criteria decision making; Construction costs |
| Geographic Terms: |
Kermanshah (Kermanshahan, Iran) |
| Abstract: |
In the present research, the application of the Upgraded Multi-Objective Particle Swarm Optimization (UMOPSO) algorithm is investigated with the aim of reducing concreting costs through minimizing the diversion dam weight function. At the time of using optimization algorithms, regarding the nature of such a;gorithms and the research problem, making changes in algorithm running steps is necessary. Hence, in the current study, making some changes in the UMOPSO algorithm develops it to solve multi-objective optimization problems by considering their problem restrictions. In this paper, the study diversion dam is the Nazelian dam located in Kermanshah, Province, Iran. The decision variables in the optimization problem include the height of upstream and downstream cut off walls, the length and the thickness of the upstream concrete bed and the thickness of the stilling basin. By considering the initial population (n = 15), the optimal answer is achieved in iteration 100 and after algorithm run 1500. In this study, considering that the objective function should be evaluated for five decision variables with infinite possible values on a discrete range for each variable, the general result of the present study emphasizes on the desirable efficiency and speed of the UMOPSO algorithm in finding the optimal answer of the diversion dam optimal design problem which shows the strength of this algorithm. [ABSTRACT FROM AUTHOR] |
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| Database: |
Complementary Index |