Feature sequencing in the rapid design system using a genetic algorithm

1996 ◽  
Vol 7 (1) ◽  
pp. 55-67 ◽  
Author(s):  
Hilmi N. Kamhawi ◽  
Steven R. Leclair ◽  
C. L. Philip Chen
Keyword(s):  
Genetic Algorithm ◽  
Design System ◽  
Rapid Design

Genetic Algorithm-Based Searching Method for Piping Path Routing in Coal-Fired Boiler Buildings

2011 ◽  
Author(s):  
Naohiro Kusumi ◽  
David E. Goldberg ◽  
Noriyuki Ichinose
Keyword(s):  
Genetic Algorithm ◽  
Computer Aided Design ◽  
Steel Structures ◽  
Search Space ◽  
Design System ◽  
Dynamic Programming Method ◽  
Plant Design ◽  
Technical Specifications ◽  
The Many ◽  
Searching Method

Power plant design using digital engineering based on 3-D computer-aided design has become a mainstream technology because of possessing higher speed and improvement in design accuracy. To take a coal-fired boiler building as an example, it has many complex structures with several million parts including the boiler itself, large fans, steel structures, and piping in varying sizes. Therefore, it is not easy to maintain integrity of the whole design throughout all the many engineering processes. We have developed a smart design system for coal-fired boiler buildings to solve the integrity problem. This system is capable of creating and allocating 3-D models automatically in accordance with various technical specifications and engineering rules. Lately, however, there has been a growing demand for more effectiveness of the developed system. We have begun to look into the feasibility of further improvements of the system function. The first point to note, when considering effectiveness, is the piping path routing process in the coal-fired boiler building. The quantity of piping is large, and it has a considerable impact on performance of the whole plant because hot steam is fed into the steam turbine and cold steam is taken from it through the piping. A considerable number of studies have been made on automatic searching methods of piping path routing. Although, the decision of piping path routing by using the Dynamic Programming method is most commonly, a previously decided routing becomes an interference object because of the single searching method. Therefore, basically, the later the order of the routing becomes, the longer the length of the routing becomes. To overcome this problem, in this paper we have proposed a new searching method based on the Genetic Algorithm (GA). The GA is a multipoint searching algorithm based on the mechanics of natural selection and natural genetics. Virtual prohibited cells are introduced into the proposed search method as a new idea. The virtual prohibited cells are located in a search space. The different paths are generated by avoiding the virtual prohibited cells while searching for the piping path routing. The optimum locations of the prohibited cells which are expressed in a genotype are obtained by using the GA in order to get a lot of paths independent of the order of the routing. The proposed method was evaluated using a simple searching problem. The results showed that many effective paths are generated by making the virtual prohibited cells.

Genetic Algorithm Based Design for Gear Trains

2000 ◽  
Author(s):  
Tae Hyong Chong ◽  
Joung Sang Lee
Keyword(s):  
Genetic Algorithm ◽  
Optimum Design ◽  
Initial Guess ◽  
Gear Train ◽  
Design System ◽  
Contact Ratio ◽  
Design Factor ◽  
Integer Variables ◽  
Gear Trains ◽  
Volume Size

Abstract The design of gear train is a kind of mixed problems which have to determine various types of design variables; i.e., continuous, discrete, and integer variables. Therefore, the most common practice of optimum design using the derivative of objective function has difficulty in solving those kinds of problems and the optimum solution also depends on initial guess because there are many sophisticated constraints. In this study, the Genetic Algorithm is introduced for the optimum design of gear trains to solve such problems and we propose a genetic algorithm based gear design system. This system is applied for the geometrical volume (size) minimization problem of the two-stage gear train and the simple planetary gear train to show that genetic algorithm is better than the conventional algorithms for solving the problems that have continuous, discrete, and integer variables. In this system, each design factor such as strength, durability, interference, contact ratio, etc. is considered on the basis of AGMA standards to satisfy the required design specification and the performance with minimizing the geometrical volume (size) of gear trains.

Strategy Research on Rapid Design of Complex Product Based on Case Reuse Technology

2010 ◽  
Vol 139-141 ◽  
pp. 2685-2690
Author(s):  
Yong Ling He ◽  
Shang Ping Li ◽  
Dong Hong Fan ◽  
Hong Liang Nong ◽  
Yan Luo
Keyword(s):  
Virtual Manufacturing ◽  
Design System ◽  
Dump Truck ◽  
Case Based Reasoning ◽  
Design Reuse ◽  
Strategy Research ◽  
Complex Product ◽  
Complex Products ◽  
Rapid Design ◽  
Virtual Manufacturing Environment

This paper proposes the design reuse model of complex product which is integrated with CBR (Case-Based Reasoning) and is established under virtual manufacturing environment. This model is aimed at the issue how to rapidly extract reuse design knowledge and select matching reuse design model to ensure that complex product can quickly respond to customers’ diverse and personalized demands with low costs during designing and manufacturing complex products. This model makes reasonable planning for the existing case design and considers the usability and economy during product design comprehensively. And on this basis, this paper researches the reuse design strategy ensuring the rapid response of complex product and the method of reuse process, develops the design system basing on case reuse technology and achieves the rapid design and analysis of product. Besides, the proposed design reuse approach is going to be tested by integrating with the farm dump truck produced by one certain machinery Co., Ltd.

A Genetic Algorithm Based Preliminary Design System

1993 ◽  
Vol 207 (2) ◽  
pp. 127-133 ◽  
Author(s):  
D T Pham ◽  
Y Yang
Keyword(s):  
Genetic Algorithm ◽  
Design Stage ◽  
Design System ◽  
Prototype System ◽  
Preliminary Design ◽  
Design Concepts ◽  
Computer Aided ◽  
Preliminary Design Stage

The different stages in design are briefly discussed. Examples of previous research into automating the preliminary design stage are described. An architecture for a computer aided preliminary design system is proposed. A prototype system for generating design concepts for transmission devices is presented.

scholarly journals A COMPARISON OF SIMULATED ANNEALING, GENETIC ALGORITHM AND PARTICLE SWARM OPTIMIZATION IN OPTIMAL FIRST-ORDER DESIGN OF INDOOR TLS NETWORKS

2017 ◽  
Vol IV-2/W4 ◽  
pp. 75-82 ◽  
Author(s):  
F. Jia ◽  
D. Lichti
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Network Design Problem ◽  
Design System ◽  
Swarm Optimization ◽  
First Order

The optimal network design problem has been well addressed in geodesy and photogrammetry but has not received the same attention for terrestrial laser scanner (TLS) networks. The goal of this research is to develop a complete design system that can automatically provide an optimal plan for high-accuracy, large-volume scanning networks. The aim in this paper is to use three heuristic optimization methods, simulated annealing (SA), genetic algorithm (GA) and particle swarm optimization (PSO), to solve the first-order design (FOD) problem for a small-volume indoor network and make a comparison of their performances. The room is simplified as discretized wall segments and possible viewpoints. Each possible viewpoint is evaluated with a score table representing the wall segments visible from each viewpoint based on scanning geometry constraints. The goal is to find a minimum number of viewpoints that can obtain complete coverage of all wall segments with a minimal sum of incidence angles. The different methods have been implemented and compared in terms of the quality of the solutions, runtime and repeatability. The experiment environment was simulated from a room located on University of Calgary campus where multiple scans are required due to occlusions from interior walls. The results obtained in this research show that PSO and GA provide similar solutions while SA doesn’t guarantee an optimal solution within limited iterations. Overall, GA is considered as the best choice for this problem based on its capability of providing an optimal solution and fewer parameters to tune.

User’s Preference Aggregation Based on Parallel Interactive Genetic Algorithms

2010 ◽  
Vol 34-35 ◽  
pp. 1159-1164 ◽  
Author(s):  
Yi Nan Guo ◽  
Yong Lin ◽  
Mei Yang ◽  
Shu Guo Zhang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimal Solution ◽  
Small Population ◽  
Population Diversity ◽  
Design System ◽  
Preference Aggregation ◽  
Ip Multicast ◽  
Interactive Genetic Algorithm ◽  
Interactive Genetic Algorithms

In traditional interactive genetic algorithms, high-quality optimal solution is hard to be obtained due to small population size and limited evolutional generations. Aming at above problems, a parallel interactive genetic algorithm based on knowledge migration is proposed. During the evolution, the number of the populations is more than one. Evolution information can be exchanged between every two populations so as to guide themselves evolution. In order to realize the freedom communication, IP multicast is adopted as the transfer protocol to find out the similar users instead of traditional TCP/IP communication mode. Taken the fashion evolutionary design system as test platform, the results indicate that the IP multicast-based parallel interactive genetic algorithm has better population diversity. It also can alleviate user fatigue and speed up the convergence.

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