6 results on '"Sadollah, Ali"'
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2. Water cycle algorithm for solving multi-objective optimization problems.
- Author
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Sadollah, Ali, Eskandar, Hadi, Bahreininejad, Ardeshir, and Kim, Joong
- Subjects
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HYDROLOGIC cycle , *ALGORITHMS , *MATHEMATICAL optimization , *METAHEURISTIC algorithms , *PARETO optimum , *MATHEMATICAL models - Abstract
In this paper, the water cycle algorithm (WCA), a recently developed metaheuristic method is proposed for solving multi-objective optimization problems (MOPs). The fundamental concept of the WCA is inspired by the observation of water cycle process, and movement of rivers and streams to the sea in the real world. Several benchmark functions have been used to evaluate the performance of the WCA optimizer for the MOPs. The obtained optimization results based on the considered test functions and comparisons with other well-known methods illustrate and clarify the robustness and efficiency of the WCA and its exploratory capability for solving the MOPs. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
3. Geometry optimization of a cylindrical fin heat sink using mine blast algorithm.
- Author
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Sadollah, Ali, Eskandar, Hadi, and Kim, Joong
- Subjects
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MINE explosions , *HEAT sinks , *MATHEMATICAL optimization , *MULTIDISCIPLINARY design optimization , *PARAMETER estimation - Abstract
The heat sinks are utilized in electronic devices to eliminate heat from the chip and efficiently transmit it to the environment. Therefore, the optimal geometry sizes of fin heat sinks are the point of concern for manufacturers and designers. For this reason, the importance of optimization techniques particularly metaheuristics is understood. The design variables are width of heat sink, number of fins, fin height, and fin diameter. The various responses that have been considered are electromagnetic emitted radiations, thermal resistance, and mass of the heat sink investigated separately and simultaneously (multi-objective). Mine blast algorithm (MBA), as a recently developed optimizer, is inspired from explosion of mines. The optimum dimensions and values for each response have been obtained by the MBA and have been compared with other optimization methods in the literature. In terms of thermal resistance and mass responses, the MBA has offered better values, while for the emitted radiations, the obtained results obtained by Taguchi-based gray relational analysis (TGRA) was preferred. For manufacturing point of view, the MBA and TGRA both suggested better and efficient design. In addition, the value path analysis has been carried out to compare the trade-off among the considered responses. Finally, parametric sensitivity analyses have been implemented for design parameters, and discussions and comparisons have been carried out for the effects of each decision variable. By considering all responses, width of heat sink and fin height are considered as the most important and effective design parameters, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2014
- Full Text
- View/download PDF
4. Urban transit network optimization under variable demand with single and multi-objective approaches using metaheuristics: The case of Daejeon, Korea.
- Author
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Almasi, Mohammad Hadi, Oh, Yoonseok, Sadollah, Ali, Byon, Young-Ji, and Kang, Seungmo
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PUBLIC transit , *URBAN growth , *METAHEURISTIC algorithms , *CITIES & towns , *GENETIC algorithms , *PUBLIC transit ridership , *BUS transportation - Abstract
Internationally, there are heightened demands for efficient public transportation systems due to high population growth rates in urban areas and their associated increased trip demands within and across city boundaries. An ideal and sustainable public transportation system should satisfy its passengers while minimizing operation costs that are often associated with energy consumptions. One such cost-effective approach is establishing an integrated public transit system. A transit system generally includes a set of bus routes and rail lines connected by transfer stations. The main objective of this research is to propose a sustainable and integrated transit establishment model to design an optimal bus transit system in combination with an existing railway system dealing with both fixed and variable demands while satisfying multiple objectives. Moreover, this paper finds an optimum set of transit routes that corresponds to chosen tradeoffs between user cost, operator cost and, notably, unsatisfied demand cost. Optimal transit networks have been achieved using single and multi-objective approaches via metaheuristic optimization algorithms including the genetic algorithm and the non-dominated sorting genetic algorithm II (NSGA-II). The study area is chosen as Daejeon City, South Korea for its strategic location. Compared with existing transit networks, the proposed approach shows significant improvements in terms of costs. In addition, the proposed approach can provide an efficient methodology for finding alternative alignments of existing transit systems for decision makers. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
5. Optimization of laminate stacking sequence for minimizing weight and cost using elitist ant system optimization
- Author
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Hemmatian, Hossein, Fereidoon, Abdolhossein, Sadollah, Ali, and Bahreininejad, Ardeshir
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LAMINATED materials , *STACKING machines , *COMPUTER systems , *ANT algorithms , *HYBRID systems , *COST analysis , *GENETIC algorithms , *MATHEMATICAL optimization - Abstract
Abstract: This paper presents the application of ant colony optimization (ACO) for the multi-objective optimization of hybrid laminates for obtaining minimum weight and cost. The investigated laminate is made of glass–epoxy and graphite–epoxy plies to combine the lightness and economical attributes of the first with the high-stiffness property of the second using a modified variation of ACO so called the elitist ant system (EAS) in order to make the tradeoff between the cost and weight as the objective functions. First natural frequency was considered as a constraint. The obtained results using the EAS method including the Pareto set, optimum stacking sequences, and the number of plies made of either glass or graphite fibers were compared with those using the genetic algorithm (GA) and any colony system (ACS) reported in literature. The comparisons confirm the advantage of hybridization and showed that the EAS algorithm outperformed the GA and ACS in terms of function’s value and constraint accuracy. [Copyright &y& Elsevier]
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- 2013
- Full Text
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6. Minimizing the levelized cost of energy in an offshore wind farm with non-homogeneous turbines through layout optimization.
- Author
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Ziyaei, Pegah, Khorasanchi, Mahdi, Sayyaadi, Hassan, and Sadollah, Ali
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OFFSHORE wind power plants , *WIND power , *TURBINES , *ARTIFICIAL neural networks , *FARM produce , *WIND turbines , *GENETIC algorithms - Abstract
Minimum cost of energy is the main goal of a wind farm layout optimization. This is achieved by maximizing the total energy while minimizing the total costs of the farm. In this study, two sizes of commercial turbines were considered to investigate the effect of a non-homogenous farm on the layout optimization process. A cost model consisting of turbines, cable, transformers, foundation, and service vehicle routes was developed. Using Genetic Algorithm and Artificial Neural Network, first the superiority of the new algorithm in turbines and cable layout was verified versus previous studies. Next, two cases were investigated, i.e. (1) a farm populated with identical turbines and (2) a farm with a random mixture of both sizes of turbines. The layouts of both cases were optimized by both single and multi-objective optimizations. In the single objective optimization, only the larger turbines remained in the optimal layout of the second case and reduced the Levelized Cost of Energy (LCOE) into half of the first case. Multi-objective optimization clarified the reason for selecting larger size turbines in the layouts when the goal of the optimization was to minimize the cost of energy. As reported in literature, non-homogenous farms produce higher output. However, they impose a higher LCOE which makes them less appealing to developers. • Single objective (cost) and multi-objective (cost & power output) optimization result in different layout. • Non-homogenous farms produce higher power output. • Non-homogenous farms impose a higher LCOE which makes them less appealing to developers. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
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