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The limitation of freshwater resources and the growing demand for water, make the issue of water resource development planning and water allocation among stakeholders even more important. Ideally, water allocation should be economically efficient and socially equitable. In this study, a water allocation model is presented in an integrated framework that considers the interaction of water supply and demand according to economic and social factors. To achieve this, a reliability-based multi-objective optimization - simulation approach has been employed. The objective functions of the problem are: 1) maximizing GDP from agricultural sectors and 2) maximizing social equality in different provinces of the basin (measured using the Williamson coefficient). The fair development and allocation among the shared provinces in the basin can reduce conflicts in the region. Karkheh basin has been considered as a case study and decision variables of the problem are area under cultivation of agricultural development sectors in different provinces. The results show that, without harming the income of the agricultural sector, the spatial distribution of development projects can be done in such a way that equality (according to income level and the number of people working in each province) is achieved. One of the solutions of Pareto front compared to previous studies shows that, in addition to an increase of about 12% of the objective function 1 (GDP), the value of the objective function 2 (Williamson coefficient) decreased from 1.19 to 0.98. This indicates a decrease in income inequality among the provinces of the basin.
相似文献The paper proposes a novel metaheuristic based on integrating chaotic maps into a Henry gas solubility optimization algorithm (HGSO). The new algorithm is named chaotic Henry gas solubility optimization (CHGSO). The hybridization is aimed at enhancement of the convergence rate of the original Henry gas solubility optimizer for solving real-life engineering optimization problems. This hybridization provides a problem-independent optimization algorithm. The CHGSO performance is evaluated using various conventional constrained optimization problems, e.g., a welded beam problem and a cantilever beam problem. The performance of the CHGSO is investigated using both the manufacturing and diaphragm spring design problems taken from the automotive industry. The results obtained from using CHGSO for solving the various constrained test problems are compared with a number of established and newly invented metaheuristics, including an artificial bee colony algorithm, an ant colony algorithm, a cuckoo search algorithm, a salp swarm optimization algorithm, a grasshopper optimization algorithm, a mine blast algorithm, an ant lion optimizer, a gravitational search algorithm, a multi-verse optimizer, a Harris hawks optimization algorithm, and the original Henry gas solubility optimization algorithm. The results indicate that with selecting an appropriate chaotic map, the CHGSO is a robust optimization approach for obtaining the optimal variables in mechanical design and manufacturing optimization problems.
相似文献Shear connectors play a prominent role in the design of steel-concrete composite systems. The behavior of shear connectors is generally determined through conducting push-out tests. However, these tests are costly and require plenty of time. As an alternative approach, soft computing (SC) can be used to eliminate the need for conducting push-out tests. This study aims to investigate the application of artificial intelligence (AI) techniques, as sub-branches of SC methods, in the behavior prediction of an innovative type of C-shaped shear connectors, called Tilted Angle Connectors. For this purpose, several push-out tests are conducted on these connectors and the required data for the AI models are collected. Then, an adaptive neuro-fuzzy inference system (ANFIS) is developed to identify the most influencing parameters on the shear strength of the tilted angle connectors. Totally, six different models are created based on the ANFIS results. Finally, AI techniques such as an artificial neural network (ANN), an extreme learning machine (ELM), and another ANFIS are employed to predict the shear strength of the connectors in each of the six models. The results of the paper show that slip is the most influential factor in the shear strength of tilted connectors and after that, the inclination angle is the most effective one. Moreover, it is deducted that considering only four parameters in the predictive models is enough to have a very accurate prediction. It is also demonstrated that ELM needs less time and it can reach slightly better performance indices than those of ANN and ANFIS.
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