scholarly journals Affordance based interactive genetic algorithm (ABIGA)

2018 ◽  
Vol 4 ◽  
Author(s):  
Ivan Mata ◽  
Georges Fadel ◽  
Anthony Garland ◽  
Winfried Zanker
Keyword(s):  
Genetic Algorithm ◽  
Design Process ◽  
Web Application ◽  
Design Method ◽  
Physical Characteristics ◽  
Steering Wheel ◽  
Design Concepts ◽  
Multi Objective Genetic Algorithm ◽  
Design Variables ◽  
Parameter Values

Designers can involve users in the design process. The challenge lies in reaching multiple users and finding the best way to use their input in the design process. Affordance based design (ABD) is a design method that focuses in part on the perceived or existing interactions between the user and the artifact. The shape and physical characteristics of the product enable the user to perceive some of its affordances. The goal of this research is to use ABD, along with an optimization tool, to evolve the shape of products toward better perceived solutions using the input from users. A web application has been developed that evolves design concepts using an interactive multi-objective genetic algorithm (IGA) relying on the user assessment of product affordances. As a proof of concept, a steering wheel is designed using the application by having users rate specific affordances of solutions presented to them. The results show that the design concepts evolve toward better perceived solutions, allowing designers to explore more solutions that reflect the preferences of end users. Relationships between affordances and product design variables are also explored, revealing that specific affordances can be targeted with changes in design parameter values and highlighting the tie between physical characteristics and affordances.

Optimization Study of a Hybrid Solar Air Conditioner

2013 ◽  
Author(s):  
Ali Al-Alili ◽  
Yunho Hwang ◽  
Reinhard Radermacher
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Global Optimum ◽  
Optimized Design ◽  
Air Conditioner ◽  
Air Conditioners ◽  
Multi Objective Genetic Algorithm ◽  
Design Variables ◽  
Optimization Study ◽  
Systems Simulation

In order for the solar air conditioners (A/Cs) to become a real alternative to the conventional systems, their performance and total cost has to be optimized. In this study, an innovative hybrid solar A/C was simulated using the transient systems simulation (TRNSYS) program, which was coupled with MATLAB in order to carry out the optimization study. Two optimization problems were formulated with the following design variables: collector area, collector mass flow rate, storage tank volume, and number of batteries. The Genetic Algorithm (GA) was selected to find the global optimum design for the lowest electrical consumption. To optimize the two objective functions simultaneously, a Multi-Objective Genetic Algorithm (MOGA) was used to find the Pareto front within the design variables’ bounds while satisfying the constraints. The optimized design was also compared to a standard vapor compression cycle. The results show that coupling TRNSYS and MATLAB expands TRNSYS optimization capability in solving more complicated optimization problems.

Universal Design Considering Physical Characteristics of Diverse Users

2019 ◽  
Vol 13 (4) ◽  
pp. 517-525
Author(s):  
Masato Inoue ◽  
Wataru Suzuki ◽  
◽  
Keyword(s):  
Universal Design ◽  
Design Method ◽  
Physical Characteristics ◽  
Design Solution ◽  
User Requirements ◽  
User Characteristics ◽  
Control Factors ◽  
Design Variables ◽  
Noise Factors

To achieve a universal design that satisfies diverse user requirements associated with aging and internationalization, designers must make a decision based on diverse user requirements. Designers have generally incorporated average human physical characteristics in their designs. Thus, user limitations are critically important. Traditional design methods often regard engineering and product design as iterative processes based on point values. However, when user information is represented as a point value, the resulting product satisfies only that specific user group and does not necessarily satisfy diverse user groups. This study proposes a universal design method that obtains diversely ranged design solutions for user requirements. The proposed method defines diverse user requirements, design variables, and user characteristics as sets, which range in value. To represent user information accurately, users are classified into numerous groups using classification techniques. Design variables are divided into two types: control and noise. Control factors are designer-controllable variables that are based on design specifications. Noise factors are designer-uncontrollable variables representing user characteristics. To derive a ranged design solution set, designers clarify the relationship between performance and design variables. Ranged solutions satisfying required performance are derived for each group using all relational expressions and ranged variable values. The combinations of divided design variables that cannot satisfy the required performance are eliminated from the design proposal, and the narrowed range of design variables become ranged solutions. The ranged solutions are derived for each group, and the common range of design variables is the ranged solution for all users. This paper chooses the design problem of the strap height of a train as a case study of the proposed universal design method. In this case study, we consider diverse user requirements based on the variability of physical characteristics. This paper discusses the suitability of our proposed approach for obtaining ranged solutions that reflect the physical characteristics of diverse users.

Flow Path Network Design for Robust AGV Systems Against Tasks Using Competitive Coevolution

2010 ◽  
Vol 22 (4) ◽  
pp. 475-484 ◽  
Author(s):  
Ryosuke Chiba ◽  
◽  
Tamio Arai ◽  
Jun Ota ◽  
Keyword(s):  
Genetic Algorithm ◽  
Design Process ◽  
Design Method ◽  
Flow Path ◽  
Automated Guided Vehicle ◽  
Simultaneous Design ◽  
Combined Flow ◽  
Competitive Coevolution ◽  
System Effectiveness ◽  
Agv Systems

An effective and robust flow path network is desired in Automated Guided Vehicle (AGV) systems. A design process to obtain the desired flow path network in AGV systems is proposed in this paper. Our proposed method can make flow path networks robust against tasks, which include pick-up point, drop-off point and throughput and number of AGVs . It is important for this robust flow path network that the kinds of tasks be of various and non-linear to the system effectiveness. The problem is solved by the design method of various kinds of tasks that are difficult for AGV systems using Genetic Algorithm (GA). An effective flow path network is designed with GA simultaneously because the difficult tasks and number of AGVs depend on the flow path networks. Competitive coevolution is applied to the simultaneous design. AGV systems can be effective with uni/bi-directional combined flow path networks which utilize just simple routings. Results of the design are shown through simulations, and the designed flow path network makes it possible to complete various tasks with various numbers of AGVs.

Genetic algorithm-based optimization design method of the Formula SAE racing car’s rear wing

2017 ◽  
Vol 232 (7) ◽  
pp. 1255-1269 ◽  
Author(s):  
Hui Wang ◽  
Qiuyang Bai ◽  
Xufei Hao ◽  
Lin Hua ◽  
Zhenghua Meng
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Design Method ◽  
Optimal Solution ◽  
Optimization Procedure ◽  
Formula Sae ◽  
Design Variables ◽  
Rear Wing ◽  
Sample Points ◽  
Lift And Drag

The aerodynamic devices play an important role on the performance of the Formula SAE racing car. The rear wing is the most significant and popular element, which offers primary down force and optimizes the wake. In traditional rear wing optimization, the optimization variables are first selected, and separately enumerated according to the analyzing experience of the racing car’s external flow field, and thus the optimal design is chosen by comparison. This method is complicated, and even might lose some key sample points. In this paper, the attack angle of the rear wing and the relative position parameters are set as design variables; then the design variables’ combination is determined by the DOE experimental design method. The aerodynamic lift and drag of the racing car for these variables’ combinations are obtained by the computational fluid dynamics method. With these sample points, the approximation model is produced by the response surface method. For the sake of gaining the best lift to drag ( FL/ FD) ratio, i.e. maximum down force and the minimum drag force, the optimal solution is found by the genetic algorithm. The result shows that the established optimization procedure can optimize the rear wing’s aerodynamic characteristic on the racing car effectively and have application values in the practical engineering.

A Design Method of an Automotive Wheel-Bearing Unit With Discrete Design Variables Using Genetic Algorithms

2000 ◽  
Vol 123 (1) ◽  
pp. 181-187 ◽  
Author(s):  
Dong-Hoon Choi ◽  
Ki-Chan Yoon
Keyword(s):  
Genetic Algorithm ◽  
Design Problem ◽  
Design Method ◽  
Bearing Unit ◽  
Design Variables ◽  
Rolling Element ◽  
Discrete Design Variables ◽  
Ball Diameter ◽  
System Life ◽  
Wheel Bearing

In order to improve the efficiency of the design process and the quality of the resulting design, this study proposes a design method for determining design variables of an automotive wheel-bearing unit of double-row angular-contact ball bearing type by using a genetic algorithm. The desired performance of the wheel-bearing unit is to maximize system life while satisfying geometrical and operational constraints without enlarging mounting space. The design variables selected are number of balls, initial contact angle, standard ball diameter, pitch circle diameter, preload, distance between ball centers, and wheel offset, which must be selected at the preliminary design stage. The use of gradient-based optimization methods for the design of the unit is restricted because this design problem is characterized by the presence of discrete design variables such as the number of balls and standard ball diameter. Therefore, the design problem of rolling element bearings is a constrained discrete optimization problem. A genetic algorithm using real coding and dynamic mutation rate is used to efficiently find the optimum discrete design values. To effectively deal with the design constraints, a ranking method is suggested for constructing a fitness function in the genetic algorithm. A computer program is developed and applied to the design of a real wheel-bearing unit model to evaluate the proposed design method. Optimum design results demonstrate the effectiveness of the design method suggested in this study by showing that the system life of an optimally designed wheel-bearing unit is enhanced in comparison with that of the current design without any constraint violations. It is believed that the proposed methodology can be applied to other rolling element bearing design applications.

Geometric Size Optimization of Annular Step Fin Using Multi-Objective Genetic Algorithm

2017 ◽  
Vol 9 (2) ◽  
Author(s):  
Abhijit Deka ◽  
Dilip Datta
Keyword(s):  
Heat Transfer ◽  
Genetic Algorithm ◽  
Heat Transfer Rate ◽  
Transfer Rate ◽  
Pareto Optimal ◽  
Objective Functions ◽  
Cross Sectional ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Design Variables

Although an annular stepped fin can produce better cooling effect in comparison to an annular disk fin, it is yet to be studied in detail. In the present work, one-dimensional heat transfer in a two-stepped rectangular cross-sectional annular fin with constant base temperature and variable thermal conductivity is modeled as a multi-objective optimization problem. Taking cross-sectional half-thicknesses and outer radii of the two fin steps as design variables, an attempt is made to obtain the efficient fin geometry primarily by simultaneously maximizing the heat transfer rate and minimizing the fin volume. For further assessment of the fin performance, three more objective functions are studied, which are minimization of the fin surface area and maximization of the fin efficiency and effectiveness. Evaluating the heat transfer rate through the hybrid spline difference method, the well-known multi-objective genetic algorithm, namely, nondominated sorting genetic algorithm II (NSGA-II), is employed for approximating the Pareto-optimal front containing a set of tradeoff solutions in terms of different combinations of the considered five objective functions. The Pareto-optimal sensitivity is also analyzed for studying the influences of the design variables on the objective functions. As an outcome, it can be concluded that the proposed procedure would give an open choice to designers to lead to a practical stepped fin configuration.

Decomposition Templates and Joint Morphing Operators for Genetic Algorithm Optimization of Multicomponent Structural Topology

2013 ◽  
Vol 136 (2) ◽  
Author(s):  
Zebin Zhou ◽  
Karim Hamza ◽  
Kazuhiro Saitou
Keyword(s):  
Genetic Algorithm ◽  
Search Space ◽  
Structural Topology ◽  
Algorithm Optimization ◽  
Two Stage ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Design Variables ◽  
Loaded Structure ◽  
Monolithic Structure

This paper presents a continuum-based approach for multi-objective topology optimization of multicomponent structures. Objectives include minimization of compliance, weight, and cost of assembly and manufacturing. Design variables are partitioned into two main groups: those pertaining to material allocation within a design domain (base topology problem), and those pertaining to decomposition of a monolithic structure into multiple components (joint allocation problem). Generally speaking, the two problems are coupled in the sense that the decomposition of an optimal monolithic structure is not always guaranteed to produce an optimal multicomponent structure. However, for spot-welded sheet-metal structures (such as those often found in automotive applications), certain assumptions can be made about the performance of a monolithic structure that favor the adoption of a two-stage approach that decouples the base topology and joint allocation problems. A multi-objective genetic algorithm (GA) is used throughout the studies in this paper. While the problem decoupling in two-stage approaches significantly reduces the size of the search space and allows better performance of the GA, the size of the search space can still be quite enormous in the second stage. To further improve the performance, a new mutation operator based on decomposition templates and localized joints morphing is proposed. A cantilever-loaded structure is used to study and compare various setups of single and two-stage GA approaches and establish the merit of the proposed GA operators. The approach is then applied to a simplified model of an automotive vehicle floor subject to global bending loading condition.

Design of a parallel gripper based on topology synthesis and evolutionary optimization

2021 ◽  
pp. 1-18
Author(s):  
I-Ting Chi ◽  
Teeranoot Chanthasopeephan ◽  
Dung-An Wang
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Design Process ◽  
Compliant Mechanisms ◽  
Synthesis Process ◽  
Dimensional Synthesis ◽  
Multi Objective Genetic Algorithm ◽  
Constraint Model ◽  
Force Displacement ◽  
Synthesis Approach

Abstract A compliant gripper with nearly parallel gripping motion is developed by a topology synthesis and a dimensional synthesis approach. The topology synthesis process can generate linkage type compliant mechanisms. Suitable boundary conditions of the topology synthesis process are selected to achieve the desired functions of the device. The dimensional synthesis is based on an evolutionary optimal design process. In order to meet various design goals, a nondominated multi-objective genetic algorithm is selected for the optimal design process. A kinetostaic model based on the chained beam constraint model is developed for force-displacement analysis of the designs. Efficiency and accuracy of the design approach are proved by experiments. Appropriate linkage types of compliant mechanisms may be discovered by the topology optimization process before moving on to dimensional synthesis to obtain final designs.

Optimization Design of Cantilever Supporting Structure

2013 ◽  
Vol 357-360 ◽  
pp. 2410-2413
Author(s):  
Wei Xu ◽  
Jian Sheng Feng ◽  
Fei Fei Feng
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Design Method ◽  
Optimal Solution ◽  
Optimization Method ◽  
Original Design ◽  
Supporting Structure ◽  
Primary Object ◽  
Design Variables ◽  
Optimum Solution

The primary object of this fundamental research is to reveal the application of genetic algorithm improved on the optimization design of cantilever supporting structure. In order to meet the strength of pile body and pile top displacement as well as design variables subjected to constraint, an algorithm is carried on to seek the optimum solution and relevant examples by means of comprehensively considering the effects on center-to-center spacing between piles,pile diameter and quantity of distributed steel, which is taken the lowest engineering cost as objective function. Through the comparison of the optimized scheme and original design, this fruitful work provides explanation to the effectiveness of genetic algorithm in optimization design. These findings of the research lead to the conclusion that the shortcomings of traditional design method is easy to fall into local optimal solution. The new optimization method can overcome this drawback.

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