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Building design is a quite complex activity where a team of designers working on diverse and contradictory parameters to make the balance between them. Because of this complexity, building performance simulation tools were developed and subsequently, the use of optimization methods, generally, as a decision-making tool is started.
The current study is a review of the optimization methods and algorithms which are used in the design of the building and trying to discover the cause of their choice, practical issues, and demonstrate their capabilities and introduce key attributes. The lack of knowledge of architects about these issues and their backwardness compared to other disciplines related to design and maintain buildings double its importance.
The most important basic rules to choose optimization strategy are classification algorithms and find suitable one for a specific problem. Several research papers in this area are investigated and according to them, optimization algorithms are divided into three categories including evolutionary algorithms, direct search (derivatives free), and the hybrid. The findings show that evolutionary algorithms and especially genetic algorithm application are more popular than other algorithms. The most study objectives to optimize are the environmental impact, the cost of initial investment, operating costs, and comfort criteria. In these studies, the design variables are construction materials, form and orientation of the building, cast shadows, and HVAC. In addition, the number of research papers that have used this algorithm to optimize the design of the building, than the number of articles on optimizing building control, is very low.

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  One of the most important researches areas in food industry is setting parameters in an optimal level to improve process functions. A literature research in food science and technology databases reveals that in recent years many optimization methods have been developed to improve food processing. However, most of these methods have only dealt with a single objective problem. In many situations the quality of a manufactured product is often evaluated by several quality characteristics and multiple objectives must be optimized simultaneously. In this article we propose a method that integrates design of experiments, response surface technique and Signal-to-Noise (SN) ratio concept for optimization of a kind of yoghurt, called Whey Less, with three objectives: TS, Acidity and cost per unit. The proposed method considers both the mean and the variation of quality losses associated with several objectives, and ensures a small variation in quality losses among the objective functions, along with a small overall average loss. Moreover, this method can be used for both kinds of decision variables (discrete and continuous) and its implementation is easy.  

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Aims: The main purpose of this article is to investigate the performance and impact of the building shell on natural lighting components and radiation reception, as components affecting the quality of the interior space, through designing a shell with Voronoi algorithm.

Methods: For this purpose, using the genetic algorithm and taking into account the Useful Daylight Illumination and the Total Radiation, optimal configurations are presented from among the design options of the shell, which is based on the Voronoi algorithm. The working method has been multi-objective optimization using NSGA-II and linear modeling in the Rhino platform and Grasshopper plugin using environmental analysis tools such as Energy Plus and Radiance for numerical calculations. Also, skins are analyzed in three cases: 1-an infilled wall without the second skin, 2-using horizontal louvers, and 3- Voronoi skin as the second skin.

Findings: It has been indicated that with Voronoi skin, the amount of average Useful Daylight Illuminance has increased by 63.86% compared to the case without the second skin, and by 21.02% compared to the case using horizontal louvers the second skin. Also, by this design idea, the amount of total solar radiation decreases by 63.86% compared to the case without the second shell and decreases by 15.38% compared to the issue of using horizontal louvers as the second shell.

Conclusion: The designed shell resulting from the optimization process and the use of Voronoi geometry has a good performance in improving the Useful Daylight Illuminance and reducing the amount of sunlight.
 

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In this paper, the multi-objective optimization of twist extrusion process is carried out using the artificial neural network model and the genetic algorithm. the target purpose functions are equivalent plastic strain, strain distribution and extrusion force. the design variables are twist angle, friction factor and the loading rate. the FEM model of the process is first created and used to create training cases for the ANN, and the well-trained ANN is used as a quick and exact model of the process. Then the optimization of the design variables is conducted by an integrated genetic algorithm and the ANN model to create a set optimal solutions (pareto front).

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Present study describes the approach of applying Response Surface Methodology (RSM) with a Pareto-based multi-objective genetic algorithm to assist engineers in optimization of sheet metal forming. In many studies, Finite element analysis and optimization technique have been integrated to solve the optimal process parameters of sheet metal forming by transforming multi objective problem into a single-objective problem. This paper aims to minimize the objective functions of fracture and wrinkle simultaneously. Design variables are blank-holding force and draw-bead geometry (length and Diameter). Response surface model has been used for design of experiment and finding relationships between variables and objective functions. Forming Limit Curve (FLC) has been used to define the objective functions. Finite element analysis applied for simulating the forming process. Proposed approach has been investigated on a cross-shaped cup drawing case and it has been observed that it is more effective and accurate than traditional finite element analysis methods and the ‘trial and error’ procedure.

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In the present paper, power transmission interaction of multilayered sound isolation panels consists of porous, solid materials and air gaps using Transfer Matrix Method (TMM) has been considered. Considering the theories related to acoustical behavior of multilayered panel lined with poroelastic materials, detail explanation of Transmission Loss (TL) of a panel via TMM has been presented. Calculation of TL for a specified panel via TMM has been compared to existed experimental data in the literature and excellent agreement is observed. Next, based on this verified model, a multi-objective optimization of multilayered panel has been conducted using NSGA-II to maximize TL of panel while the panel weight is kept to a minimum. Results of two-objective optimization reveals, if the designer target is to achieve a specific average TL in the frequency band of 10 to 500 Hz, for a panel with constant width, selecting a panel with lower layers (three layers) can bring lower weight. But, if a higher average TL in the same frequency band is desired, a panel with more layers (six layers) has much better conditions in terms of weight.

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Dynamic model identification and state variables estimation from the corrupted measurement data have been attracted much research efforts during the recent years. In this way, Kalman and H-infinity filters have been increasingly used to estimate the parameters individually. In this paper, a mixed kalman-H_∞ filter is designed in an innovative approach using a multi-objective optimization method. It is desired to simultaneously employ the advantages of both filters to minimize both the root-mean squared errors and the upper bounds limit of estimation errors associated with Kalman and H-infinity filters, respectively. Some Pareto optimum design points are presented for two case studies from which trade-off optimum design points can be simply selected.

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Aims: Utilizing passive architectural elements to conserve energy and optimize natural lighting is a common solution in traditional Iranian architecture. Various factors such as building shape and orientation, window positioning, use of local materials, and shading devices are recognized as traditional architectural elements in warm and humid climates. The main objective of this research is to evaluate the efficiency and optimize architectural elements in the warm and humid climate of Bushehr city, focusing on energy consumption control and utilization of natural light.

Methods: Firstly, through documentary resources, the residential architectural patterns of Bushehr were identified. Then, using the Rhinoceros software environment and Grasshopper plugin, selected variables were parametrically modeled, and quantitative data analysis was conducted using energy tools and radiation analysis. Finally, optimal patterns were selected using a genetic algorithm, and the final response was presented with an annual performance analysis.

Findings: By optimizing passive strategies, UDI could be increased up to 96%, and energy consumption could be reduced up to 174.1 kWh/m². In hot and humid climates, paying attention to the minimum absorption of sunlight is essential, in addition to the importance of using natural ventilation.

Conclusion: Using passive architectural elements such as the use of Shenashir, the proportions of the room and the window-to-wall ratio increases the performance of the building. The conclusion emphasizes the pivotal role of the contemporization of traditional houses in resolving contemporary architectural challenges, especially high energy consumption and environmental regulation.

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This paper investigates the multi-objective optimization design of planar cable-driven parallel robots by using the evolutionary optimization algorithm. Since in cable-driven parallel robots, the cables should remain in tension in all configurations, the extent of the controllable workspace is considered as one of the design indices. This objective function is of utmost importance to the design of cable-driven parallel robots, since it considers the unidirectional properties of the cables in the analysis. In addition, in order for the robot to have suitable dexterity and accuracy and to be able to manipulate any arbitrary task in all the required directions, various kinematic indices such as global condition number, translational and rotational kinematic sensitivity indices are used. Through analysis of the conflict of these objectives within the workspace of the robot, it is shown that use of multi-objective optimization is an effective method to reach to a suitable trade-off. Furthermore, by applying multi-objective optimization methods such as the non-sorting genetic algorithm and the adaptive weighted particle swarm optimization algorithm, the optimal pareto front for the design parameters for the cable robot is obtained such that to draw a compromise between the robot designs.

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In this paper, a new multi-objective differential evolution with a diversity preserving mechanism called the ε-elimination algorithms is used for the Pareto optimum design of gripper mechanisms. The ε-elimination diversity is used to improve the population diversity among the obtained Pareto front. In the ε-elimination diversity approach based on a threshold value all the clones or ε-similar individuals are recognized and simply removed from the current population. It should be noted that such e-similarity must exist both in the space of objectives and in the space of the associated design variables. The proposed algorithm has been used for two different configuration of robot’s gripper. The dimensions of mechanisms are considered as design variables and optimally selected by proposed algorithm to improve the efficiency of griper mechanism. Two conflicting objectives which are the difference between maximum and minimum gripping forces and the transmission ratio of actuated and experienced gripper forces, are considered for Pareto optimization. The best configuration of gripper mechanism is suggested by comparing of trade-off design points. The comparisons of the obtained Pareto front using the method of this paper with those obtained in other references shows a significant improvement.

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Control engineers are interested in state estimation problems as one of the most interesting subject. In this way, Kalman filter, H-infinity, and Mixed Kalman/H-infinity filter are the most widely used filters for state estimation of the discrete linear dynamical system corrupted with Gaussian and white noises. These filters will be, however, suboptimal for state estimation when the process noise and/or measurement noise are color noises. In this paper, a multi-objective Pareto optimization (multi-objective genetic algorithm) approach is presented for the design of combined Kalman/H-infinity filters to estimate states corrupted with color noises. In this way, a state augmentation procedure is used to analyze the effect of the colored noises on states estimation of an inverted pendulum. Some Pareto curves are then obtained to compromise between the Kalman and H-infinity filters. It is shown that the use of such approach can evidently improve the effectiveness of the filters when the color noises are significant. Therefore, by using the proposed approach, we can employ the advantages of both Kalman and H-infinity filters simultaneously to minimize both the mean of squared errors and the upper bounds limit of estimation errors.

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Nowadays, optimization is becoming one of the most important techniques in engineering and industry to provide competing products in design and manufacturing. Therefore, it is a necessity to search for optimum designs with productibility. In aerospace industry reducing weight and improving reliability of the products are major concerns. As regards the gearbox is one of the most important parts in the helicopter propulsion system, these objects should be more considered. However, most of the existing designs consider only one object, hence, it is vital to implement optimization techniques to include different objectives to improve the existing designs and provide optimum products. In this paper, optimum design parameters including module and face width of gears for the main gearbox of Sikorsky ASH-3D helicopter have been determined (modified) using single and multi-objective mixed discrete- continuous optimization method to minimize weight of the gearbox, increase the safety factor and reduce the difference between safety factors of different gears. The results show that the weight of the gears can be reduced by 27.24% comparing with the existing gearbox. The results of the multiobjective optimization have also been presented as Pareto front diagram wich can be used by the manufacturers to satisfy the prefered requiments.

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Many researchers have been considered biomass utilization due to reduction of greenhouse gas effects and environmental impact recently. Achieving a system with the best performance for the application of this type of fuel with low calorific value is to be one of the topics of interest to researchers. This study focus on precise modeling of biomass gasification and design a trigeneration system to produce cooling, heating and electricity using this clean source of energy. In the process modeling of biomass gasification a realistic model includes tar content in syngas is developed. A parametric study of trigeneration system to find the objective functions trend and to achieve the best performance parameter is carried out. Results show that two objective functions in the reasonable range have conflict which emphasis to the multi-objective optimization. Also, with draw Pareto front curve, a suitable relation to estimate the trend of objective functions is derived.

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Cable-Driven Parallel Robot has many advantages. However, the problems of cable collision between each other and environment, the lacks proper structure and non-positive cable tension prevent the spread of them. Therefore, connecting a serial manipulator to mobile platform improve the ability to object manipulation. This paper investigates the multi-objective optimization structure design and comparative study of spatial constrained and suspended cable-driven parallel robot. By install serial manipulators possess a full hybrid robot’s features. The workspace volume, kinematic stiffness and sensitivity are three sets of optimization criteria. The workspace volume calculated by a novel approach of combination constraints as prevent cables collisions with each other, cable collision with moving platform, uncontrollability and singularity of the robot. First, examine range of the forces and torque reaction of the serial manipulator to moving platform. Then, the evolutionary optimization genetic algorithm use for the multi-objective optimization of constrained and suspended spatial cable-driven parallel robot structure to achieve proper Pareto front confrontation. The Pareto front reconciliation of these three criteria will be discussed. The constrained and suspended optimize by same criteria will compare in the same conditions. It is verified that the constrained structure significantly reduced actuation energy for manipulate a serial robot, supply greater workspace and manipulability. The result of this study used for manufacturing and development of a prototype spatial cable-driven parallel robot (RoboCab).

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Air staging is defined as the supply of inadequate air from the primary stage to the reaction zone, and the completion of the air supply through the next stage or stages. This study is concerned with the optimization of the air staging system of a burner with two air inlets and one fuel (natural gas) inlet with the help of numerical modeling. The equivalence ratio of the primary air (with the assumption of a fixed total air mass flow rate), and the distance between the two air inlets constitute the design variables of the problem. In the previous research works, the air staging technology has been mainly employed as a method to reduce the emission of NO. However, in the current study, in addition to the emission of NO, the emissions of CO and soot, and radiative heat transfer from the flame are considered as the objective functions. The results show that increasing the level of air staging (or the equivalence ratio of the primary air) has contradictory effects on the objective functions so that, as positive influences, it increases the radiative heat transfer from the flame and decreases the emission of NO, and as negative effects, it increases the emission of both CO and soot. The results also indicate that when all the previously mentioned objectives are considered simultaneously, the optimal case, which is selected based on the Pareto front concept, is the case in which the primary air is about 20% of the theoretical air.

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In this study, the effects of geometrical parameters of 6-DOF Hexa parallel robot on kinematic, and dynamic performance indices are investigated and its structure is optimized using the intelligent multi-objective Bees Algorithm. In this way, after describing the structure and specifying the geometrical parameters of the robot, inverse kinematic relations of the robot are obtained. Jacobian matrix that maps velocity from joint space to Cartesian space is developed. Mass matrix is obtained from calculating the total kinetic energy of the manipulator in terms of the actuated joints vector. Inverse of the homogen jacobian based condition number is considered as a index to evaluate the kinematic dexterity. based on mass matrix as relation between acceleration vector of the end effecter and torque vector of actuated joints, dynamic dexterity index is presented. Using the multi-objective Bees Algorithm and considering dynamic and kinematic performance indices in a pre-determined workspace as the objective functions, structure of Hexa parallel robot is optimized. In this way, the proper geometrical constraints such as limitation of universal and spherical joins, and the constraints to singularity avoidance are considered. Pareto front of the multi objective optimization of the robot is drawn. Diagrams of the kinematic and dynamic performance indices variation in the workspace and the effects of geometrical parameters variation on them are presented.

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Thin-walled structures are frequently used as energy absorbers in automotive, railway and aviation industries. This paper deals with the collapse and energy absorption behavior of thin-walled structures under dynamic axial loading Numerical modeling was performed using finite element code LS-DYNA. In order to validate the results of finite element analyses, a square tube was collapsed using universal test machine. This tube was then simulated in LS-DYNA, and the results with those of experiments were compared. There was a good consistency between the numerical and experimental results. The tubes with different cross-sections namely square, hexagonal and octagonal shapes reinforced with inside ribs as well as with different scales (ratio of sectional side length of the inner tube to that of outer tube) 0, 0.25, 0.5, 0.75 and 1 were simulated in LS-DYNA. To determine the suitable cross-section in terms of crashworthiness, multi-criteria decision making method known as Technique of Order Preference by Similarity to Ideal Solution (TOPSIS) was employed. The results demonstrated that the double walled tube with octagonal cross-section possessing the scale between 0.25 and 0.5 had the best crashworthiness behavior. To find the optimum values of scale and wall-thickness, response surface method (RSM) and D-optimal criterion using design of experiments (DOE) were utilized Moreover, the effect of number of inside ribs (4 and 8) on the capability of absorbing energy was also investigated and the octagonal tube with 4 inside ribs was selected as an optimal tube with lower maximum impact force.

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Prediction of the behavior of T-shaped chambers due to its high complexity has always been of great interest researchers. In this article, based on experimental data and genetic programming, the optimal model was presented for mixing process response. To get system’s behavioral equations, first, by using the experimental results and by changing the input variables System, input – output data is extracted. In order to predict the behavior of the system, the equation of input – output data, is derived using genetic programming. To design the structure of genetic programming trees, multi-objective optimization with two objective functions are taken into consideration: model inaccuracy and complexity of structure. By minimizing the objective function at the same time, we are looking for simple equations (minimizing the complexity of the structure) and increasing the accuracy of modeling (minimizing the error). In order to achieve a less complex equation, depth of the generated trees in structure of genetic programming will be minimal. By using multi-objective optimization, optimum set of points have presented. Comparing the results obtained from the models and real data represents a very good match.

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One of the organizations’ fundamental issues is supply chain network design. Optimization of this network can lead to effective management of the whole supply chain. Network design specifies the position, capacity, number and type of network facilities, and transportation network of materials and products from the supplier to the customer and vice versa. This research proposes new solution procedure based on Multi-objective Genetic Algorithm (MOGA) and Non-dominated Sorting Genetic algorithm-II (NSGAII) to find the set of Pareto optimal solutions that empowers the decision-makers by alternative solutions. Considering that in this study the level of service is very important, so this modeling was based on satisfying all customer demands. Objectives for network optimization are minimization of total cost and maximization of capacity utilization balance for network facilities that lead to the reduction of customers’ service time (increase service levels). Nine problems were designed from small to large. In order to compare the quality of the obtained Pareto solutions of both algorithms, seven criteria (for multi-objective problems) were used in this study. The results indicated that the solutions produced by NSGAII algorithm have higher quality.

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Today, with increasing consumption of non-renewable energy sources, scientists are looking for an alternative for these resources. The Stirling engine is one of the ideas that have recently attracted engineers' attention. The purpose of this study is to optimize the output power and stability of a beta type free-piston Stirling engine. In this regard, at first by deriving the thermodynamic and dynamic equations of the system and combining them, the governing equations are obtained including the nonlinear function of the pressure loss in heat exchangers. The governing nonlinear equations are solved and for the purpose of validation, simulation results obtained in this study are compared with experimental and simulation results presented in the literature. In free-piston Stirling engines, increasing the output power by keeping their stability is very important. Therefore, by performing parametric study, the parameters with more effects on the output power and stability are determined and considered as optimization variables. In order to perform multi-objective optimization of output power and stability of the free-piston Stirling engine, a proper objective function is selected and one of the methods in genetic algorithm is employed using optimization software Modefrontier. Finally, values of variables, before and after optimization and also, percentage of improvements in output power and stability of the free-piston Stirling engine are presented.