جستجوی مقالات مرتبط با کلیدواژه « multi-objective optimization » در نشریات گروه « مکانیک »

In recent years, reducing energy consumption and greenhouse gas emissions in the building sector has attracted a lot of attention. As in other parts of the world, the construction sector in Iran also has a large share of energy consumption. As a result, low consumption and even zero energy buildings, as an alternative solution in solving this problem, have attracted the attention of scientists, researchers and policy makers. One of the main challenges in designing a zero energy building is to find the best combination of passive solutions to reduce consumption and optimize energy performance in the building in such a way that the energy performance of the building and the thermal comfort of the residents are not negatively affected. This article presents a method for multi-objective and multi-variable optimization based on building simulation, which is carried out in three main stages: building energy consumption simulation, two-objective optimization process and multi-criteria decision making for zero energy building design using renewable energy sources. Reproducible. The studied design parameters are: direction of the building, angle and depth and length of the protrusion of the shades, insulation thickness of external walls, temperature adjustment points for cooling and heating, ratio of window to wall area, heat transfer coefficient of windows and thickness of roof insulation. In addition, the considered renewable energy sources are: photovoltaic panels and wind turbines. In order to achieve a net zero energy building, considering maintaining the thermal comfort of the residents, a multi-objective genetic algorithm with non-dominated sorting has been used. Finally, in order to determine the most optimal answer in the Pareto diagram, the elimination and selection method compatible with reality has been used. The results show that climatic conditions and the appropriate selection of architectural parameters are very important and vital in reducing building energy consumption. Based on the results, the use of energy optimization solutions in Tehran leads to a 25.3% reduction in energy consumption. In addition, there is a significant potential for the use of photovoltaic systems in Tehran to provide energy for homes.

In the current research, a renewable system based on wind, solar, and geothermal energy was analyzed with a targeted combination of fuel cell units, heat pump, proton exchange membrane electrolyzer, steam turbine and reverse osmosis. Transient modeling and simulation of the proposed system was done using TRNSYS software. This system was designed and evaluated to meet the needs of a residential complex with 80 residential units and 320 people for electrical energy, cooling, and heating. A case study was conducted for the feasibility of setting up the proposed system in the coastal city of Bandar Anzali in Iran. Optimization of system performance was done by determining two objective functions of production power and system life cycle cost with response surface method and Design Expert software. Five parameters affecting the system performance including the number of wind turbines, number of solar panels, fuel cell capacity, steam turbine capacity, and proton exchange membrane electrolyzer capacity were selected as optimization variables. In its most optimal state, the system can reach a production power of 3036105.022 kWh and a life cycle cost of 781944.254 $. The environmental results showed that by setting up the system in an optimal mode in Bandar Anzali city and producing electricity at the rate of 3036.1 MW per year, it is possible to help expand 3 hectares of green space per year. It also helped reduce carbon dioxide emissions by 619.36 tons of CO2 at a cost of 14,864.74 $.

The purpose of this article is to optimize the power supply subsystem of the satellite based on mass and power production. For this purpose, the satellite orbit is to determine the time of placement in the shadow in Satellite Tool kit (STK) software, and the design of the satellite energy supply subsystem of existing analytical equations are done, and these main design parameters have been optimized to achieve the minimum weight of system. Then, the beginning of the target satellite, the Orsted satellite, the orbital and mission operational modes of this satellite has been compiled and the statistical information of its power supply subsystem has also been determined. Then, the design of the power supply sub-system is based on the parametric analysis and processing of statistical information and also based on the simulation and orbital analysis of the satellite. Finally, in order to model the design problem, the objective functions of design and optimization, the weight of the power supply subsystem has been considered, and the main design parameters affecting the objective function are capacity, depth of discharge, efficiency and density of the battery and the area of ​​the arrays. It is worth mentioning that the type of battery itself is also very effective on the objective function, but considering the type of satellite battery considered (nickel-cadmium), the variable battery type is not considered. Finally, this problem has been optimized using genetic algorithm. The obtained results show the accuracy of the method and implementation compared to the values ​​of the existing satellite power supply subsystem.

This article addresses the bi-level multi-objective optimization problems raised in reliability-based robust design optimization of mechanism synthesis through establishing a state-of-the-art game theoretic scenario. A novel bi-level decentralized decision-making approach is proposed using the synergy of reliability-based robust design optimization (RBRDO), Stackelberg/cooperative game theory, Monte Carlo simulation (MCS) and genetic programming (GP). The application of the proposed approach is elaborated in a case-study of multi-objective robust synthesis of high-speed path generating four-bar. The four performance criteria, namely, accuracy (), robustness ( and ), reliability () at upper level and quality of motion () at lower level are assigned to four players so that each of whom is in charge of one objective criterion being optimized. The peak input driving torque as the dynamic constraint is associated with the upper-level problem. The genetic programming (GP) metamodel is used to capture the Stackelberg protocol between two levels i.e., constructing the follower’s rational reaction set (RRS) and the Nash arbitration scheme is hired to model the cooperative behaviors in upper level. By this way, the four-objective optimization problem of four-bar linkage is reduced into a single-objective robust design problem. The obtained results show a considerable enhancement in reliability and robust behavior of mechanism, whilst the deterministic criteria of accuracy and quality of motion is preserved.

In this paper, a multi-objective differential evolution with fuzzified mutation factor using the combination of non-dominated sorting and crowding distance criterion (MODE-FM) is used for Pareto optimization (in bi- and 7-objectve spaces) of a 5-degree of freedom active and linear suspension vehicle model considering the seven conflicting functions simultaneously, under stationary random road profile. The significant conflicting objective functions that have been utilized here for assessing the applicability of the aforesaid suspension system reaching the compromise between ride comfort and road holding capability are, namely, vertical seat acceleration, vertical forward tire velocity, vertical rear tire velocity, relative displacement between sprung mass and forward tire, relative displacement between sprung mass and rear tire, forward control and rear control force. Further, the design variables include the coefficients of springs and dampers of suspension system and seat and coefficients of control force along with the seat position in relation to the center of mass of sprung mass in which the suggested design can be achieved. It should be noted that the road roughness used here is in class C based on the ISO 8608 standard. Comparison of the attained results of this work with those in the literature has proved the superiority of the results of this work.

Mathematical modeling of biological systems is one of the most desirable methods for studying the behavior of these biological phenomena. The development of mathematical models has always been important for simulating, controlling, and predicting phenomena. One of the advantages of mathematical models is their application in optimization. Solving cancer therapy as an optimal control problem aims to reduce the concentration of cancer cells in the treatment period. In this solution, an important aspect that was not considered in previous studies was the optimum drug concentration that significantly affected patients' clinical health. Due to this, the present study was carried out to obtain an optimal drug-prescribing protocol to minimize the concentration of cancer cells and the drug concentration. This Multi-objective problem has been solved, for the first time, using the NSWOA algorithm, and the results were compared with the NSGA-II algorithm. Pareto front curve obtained from both algorithms offers a set of the most optimal solutions. According to the criteria of treatment choice, one of these points is chosen as the drug administration protocol.

Near-Earth Asteroids are attractive targets in terms of the mineral resources they can provide us and the fast technological development and growing human needs for rare metals and minerals. In this paper, Ryugu, Itokawa, and Bennu asteroids are selected from near-Earth asteroids due to their proper distance from Earth to design the optimal round-trip trajectory. Multi-objective optimization along with the ephemeris of these asteroids is used to design outbound and inbound trajectories. The objective functions include the total delta-velocity and the total duration of the mission, these variables are minimized with the help of the Non-dominated Sorting Genetic Algorithm for the trajectories between the Earth and the sample asteroids. In the following, the results are presented in the form of Pareto diagrams. which indicates the suitability of the conditions of Bennu asteroid compared to other sample asteroids. To validate these results, the Pareto diagram answers that had the least delta-velocity were compared with their counterparts from the available information on the NASA JPL data center.