A Ship Design Tool Using Genetic Algorithms

2007 ◽  
Author(s):  
S Vasudevan ◽  
◽  
S C Rusling ◽  
Keyword(s):  
Genetic Algorithms ◽  
Ship Design ◽  
Design Tool

Evaluating a digital ship design tool prototype: Designers’ perceptions of novel ergonomics software

2017 ◽  
Vol 59 ◽  
pp. 19-26 ◽  
Author(s):  
Steven C. Mallam ◽  
Monica Lundh ◽  
Scott N. MacKinnon
Keyword(s):  
Ship Design ◽  
Design Tool

scholarly journals Genetic algorithms in computer aided inductor design

2004 ◽  
Vol 23 (3) ◽  
pp. 68-78
Author(s):  
Jean Fivaz ◽  
Willem A. Cronjé
Keyword(s):  
Genetic Algorithms ◽  
Power Electronics ◽  
Computer Aided Design ◽  
Design Tool ◽  
Design Problems ◽  
Characteristic Data ◽  
Computer Aided ◽  
Aided Design

The goal of this investigation is to determine the advantages of using genetic algorithms in computer-aided design as applied to inductors.  These advantages are exploited in design problems with a number of specifications and constraints, as encountered in power electronics during practical inductor design. The design tool should be able to select components, such as cores and wires, from databases of available components, and evaluate these choices based on the components’ characteristic data read from a database of manufacturers’ data-sheets.  The proposed design must always be practically realizable, as close to the desired specifications as possible and within any specified constraints.

scholarly journals Design of low-power CMOS VLSI circuits using multi-objective optimization in genetic algorithms

2021 ◽  
Vol 12 (1) ◽  
pp. 215-224
Author(s):  
Mahnoor Maghroori ◽  
Mehdi Dolatshahi
Keyword(s):  
Genetic Algorithms ◽  
Integrated Circuits ◽  
Circuit Simulation ◽  
Cmos Technology ◽  
Vlsi Circuits ◽  
Design Tool ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Cmos Vlsi

This paper presents a design CAD tool for automated design of digital CMOS VLSI circuits. In order to fit the circuit performance into desired specifications, a multi-objective optimization approach based on genetic algorithms (GA) is proposed and the transistor sizes are calculated based on the analytical equations describing the behavior of the circuit. The optimization algorithm is developed in MATLAB and the performance of the designed circuit is verified using HSPICE simulations based on 0.18µm CMOS technology parameters. Different digital integrated circuits were successfully designed and verified using the proposed design tool. It is also shown in this paper that, the design results obtained from the proposed algorithm in MATLAB, have a very good agreement with the obtained circuit simulation results in HSPICE.

Producibility as a Design Factor in Naval Ships

1986 ◽  
Vol 2 (02) ◽  
pp. 90-100
Author(s):  
Michael L. Bosworth ◽  
Clark Graham
Keyword(s):  
Design Process ◽  
Ship Design ◽  
Design Tool ◽  
Evaluation Procedure ◽  
Design Factor ◽  
Rigorous Evaluation ◽  
Tradeoff Analysis ◽  
Technical Risk ◽  
The Cost ◽  
Ship Performance

There are many producibility concepts which affect the characteristics of a naval ship. These concepts must be addressed during the early phases of the ship design process while the ship is still flexible. Since these producibility concepts may affect ship performance and technical risk, as well as ship characteristics and cost, a rigorous tradeoff analysis is required. This paper provides examples of producibility concepts which should be addressed during the ship design process. An evaluation procedure is presented to assist in the gathering and the organizing of information required for an objective tradeoff analysis. The ship synthesis model "ASSET" is utilized as the principal design tool to determine ship impact and the cost of producibility concepts. One of the primary recommendations of the authors is that the Navy increase the visibility of producibility as a design factor in naval ships by developing rigorous evaluation tools, cataloging producibility concepts for considerations in future designs, and establishing an advocate for ship producibility within the design organization.

scholarly journals Using pruning algorithms and genetic algorithms to optimise network architectures and forecasting inputs in a neural network rainfall-runoff model

1999 ◽  
Vol 1 (2) ◽  
pp. 103-114 ◽  
Author(s):  
Robert J. Abrahart ◽  
Linda See ◽  
Pauline E. Kneale
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Design Tool ◽  
Forecasting Model ◽  
Network Architectures ◽  
Rainfall Runoff ◽  
Small Catchment ◽  
Pruning Algorithms ◽  
Rainfall Runoff Model ◽  
Runoff Model

Four design tool procedures are examined to create improved neural network architectures for forecasting runoff from a small catchment. Different algorithms are used to remove nodes and connections so as to produce an optimised forecasting model, thereby reducing computational expense without loss in performance. The results also highlight issues in selecting analytical methods to compare outputs from different forecasting procedures.

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