جستجوی مقالات مرتبط با کلیدواژه « MOPSO » در نشریات گروه « عمران »

Population growth and development of economic relations have led to increase utilization of transportation infrastructures. The bridge is one of the important components of road construction that always needs special attention during the whole period of design, execution and operation. To ensure that the stairs are regularly inspected, evaluated and operated, management is needed to meet this need in the stairs by performing appropriate maintenance and repairs. Therefore, in this study, a three-objective optimization model includes minimization of maintenance costs, maximization of bridge performance index and reliability of bridges. For this purpose, a complex integer mathematical programming model has been developed. In order to solve the problem, a multi-objective particle mass optimization algorithm has been developed. From the solution of the proposed algorithm, different Pareto answers are obtained so that the decision makers can choose the answer from the obtained Pareto answers that is their priority.Also, after performing a sensitivity analysis on the parameters of failure rate and failure rate during the effect, it has been observed that by increasing the scenario number in two cases, which indicates an increase in failure rate and failure rate during the effect, the amount of costs increases and The overall performance of the system has been declining, which seems natural and correct.

Irregular withdrawals from water resources, unprincipled agriculture at the upstream of Shadegan wetland, and the drainage of agricultural drains and municipal effluents have severely reduced the quality of water entering the wetland. Also, the construction of the Marun and Jarreh dams on the rivers supplying the wetland demand has led to severe hydrological changes in the river and eventually, its environmental demands have not been met in some years. Common water resources operation methods focus on maximizing socio-economic benefits and pay little attention to meeting ecosystem demands. The aim of this paper is to investigate the performance of meta-exploratory algorithms in planning and proper allocation management to resource and uses at the upstream of Shadegan Wetland and the determination of the ecosystem demand of the downstream so that in addition to maximizing the percentage of the basin demand supply during the operation period, this algorithm attempts to reduce the salinity of the inflow to Shadegan Wetland. Due to the importance of the wetland as a seasonal habitat for birds and also one of the important tourist attractions and the importance of protecting its ecosystem, the development of a quantitative-qualitative optimization model for optimal use of available water resources is the purpose of this study. Initially, based on current conditions, the prepared model entitled "reference scenario" is developed for a future 30-year period (2021 to 2050). To achieve the best system response in terms of qualitative and quantitative criteria, the efficiencies of the MOICA and MOPSO algorithms as the optimal scenario are compared. The results indicate that the MOICA algorithm has a better performance in supplying various demands as well as decreasing the salinity of the inflow to Shadegan Wetland more than the MOPSO. With the implementation of the optimal solution obtained by the MOICA, in addition to supplying demands with high reliability in the whole system, the amount of river salinity at the entrance to Shadegan Wetland, especially in low water months, is reduced by about 55%. The coupling model proposed in this research is applicable for other study areas with a quantitative-qualitative operation approach and is able to calculate the environmental demands of the river as well as the downstream ecosystem by taking into account all uses.

Today, urban land use planning and management is an essential need for many developing countries. So far, lots of multi objective optimization models for land use allocation have been developed in the world. These models will provide set of non-dominated solutions, all of which are simultaneously optimizing conflicting social, economic and ecological objective functions, making it more difficult for urban planners to choose the best solution. An issue that is often left unnoticed is the application of spatial pattern and structures of urban growth on models. Clearly solutions that correspond with urban spatial patterns are of higher priority for planners. Quantifying spatial patterns and structures of the city requires the use of spatial metrics. Thus, the main objective of this study is to support decision-making using multi objective Meta-heuristic algorithms for land use optimization and sorting the solutions with respect to the spatial pattern of urban growth. In the first step in this study, we applied the non-dominated sorting genetic algorithm ΙΙ (NSGA_II) and multi objective particle swarm optimization (MOPSO) to optimize land use allocation in the case study. The four objective functions of the proposed model were maximizing compatibility of adjacent land uses, maximizing physical land suitability, maximizing accessibility of each land use to main roads, and minimizing the cost of land use change. In the next step, the two mentioned optimization models were compared and solutions were sorted with respect to the spatial patterns of the city acquired through the use of spatial metrics. A case study of Tehran, the largest city in Iran, was conducted. The six land use classes of industrial, residential, green areas, wetlands, Barren, and other uses were acquired through satellite imagery during the period of 2000 and 2012. Three scenarios were predicted for urban growth spatial structure in 2018; the continuation of the existing trend from 2000 to 2018, fragmented growth, and aggregated growth of the patches. Finally, the convergence and repeatability of the two algorithms were in acceptable levels and the results clearly show the ability of the selected set of spatial metrics in quantifying and forecasting the structure of urban growth in the case study. In the resulted arrangements of land uses, the value of the objective functions were improved in comparison with the present arrangement. In conclusion planners will be able to better sort outputs of the proposed algorithms using spatial metrics, allowing for more reliable decisions regarding the spatial structure of the city. This achievement also indicates the ability of the proposed model in simulation of different scenarios in urban land use planning.