Optimal Designs by Means of Genetic Algorithms

2021 ◽  
pp. 344-354
Author(s):  
Lata Nautiyal ◽  
Preeti Shivach ◽  
Mangey Ram
Keyword(s):  
Genetic Algorithms ◽  
Modeling And Simulation ◽  
Engineering Design ◽  
Design Problem ◽  
High Performance ◽  
Mechanical Engineering ◽  
Design Parameters ◽  
Design Problems ◽  
Simulation Tools ◽  
Engineering Design Problems

With the advancement in contemporary computational and modeling skills, engineering design completely depends upon on variety of computer modeling and simulation tools to hasten the design cycles and decrease the overall budget. The most difficult design problem will include various design parameters along with the tables. Finding out the design space and ultimate solutions to those problems are still biggest challenges for the area of complex systems. This chapter is all about suggesting the use of Genetic Algorithms to enhance maximum engineering design problems. The chapter recommended that Genetic Algorithms are highly useful to increase the High-Performance Areas for Engineering Design. This chapter is established to use Genetic Algorithms to large number of design areas and delivered a comprehensive conversation on the use, scope and its applications in mechanical engineering.

Optimal Designs by Means of Genetic Algorithms

2018 ◽  
pp. 151-161
Author(s):  
Lata Nautiyal ◽  
Preeti Shivach ◽  
Mangey Ram
Keyword(s):  
Genetic Algorithms ◽  
Modeling And Simulation ◽  
Engineering Design ◽  
Design Problem ◽  
High Performance ◽  
Mechanical Engineering ◽  
Design Parameters ◽  
Design Problems ◽  
Simulation Tools ◽  
Engineering Design Problems

With the advancement in contemporary computational and modeling skills, engineering design completely depends upon on variety of computer modeling and simulation tools to hasten the design cycles and decrease the overall budget. The most difficult design problem will include various design parameters along with the tables. Finding out the design space and ultimate solutions to those problems are still biggest challenges for the area of complex systems. This chapter is all about suggesting the use of Genetic Algorithms to enhance maximum engineering design problems. The chapter recommended that Genetic Algorithms are highly useful to increase the High-Performance Areas for Engineering Design. This chapter is established to use Genetic Algorithms to large number of design areas and delivered a comprehensive conversation on the use, scope and its applications in mechanical engineering.

Using Q-Learning and Genetic Algorithms to Improve the Efficiency of Weight Adjustments for Optimal Control and Design Problems

2005 ◽  
Author(s):  
Kaivan Kamali ◽  
Lijun Jiang ◽  
John Yen ◽  
K. W. Wang
Keyword(s):  
Optimal Control ◽  
Genetic Algorithms ◽  
Convergence Rate ◽  
Cost Function ◽  
Design Problem ◽  
Numerical Experiments ◽  
Design Parameters ◽  
Design Problems ◽  
Q Learning ◽  
Layered Approach

In traditional optimal control and design problems, the control gains and design parameters are usually derived to minimize a cost function reflecting the system performance and control effort. One major challenge of such approaches is the selection of weighting matrices in the cost function, which are usually determined via trial and error and human intuition. While various techniques have been proposed to automate the weight selection process, they either can not address complex design problems or suffer from slow convergence rate and high computational costs. We propose a layered approach based on Q-learning, a reinforcement learning technique, on top of genetic algorithms (GA) to determine the best weightings for optimal control and design problems. The layered approach allows for reuse of knowledge. Knowledge obtained via Q-learning in a design problem can be used to speed up the convergence rate of a similar design problem. Moreover, the layered approach allows for solving optimizations that cannot be solved by GA alone. To test the proposed method, we perform numerical experiments on a sample active-passive hybrid vibration control problem, namely adaptive structures with active-passive hybrid piezoelectric networks (APPN). These numerical experiments show that the proposed Q-learning scheme is a promising approach for.

scholarly journals Optimal Solution of Structural Engineering Design Problems using Crow Search Algorithm

2019 ◽  
Vol 4 (4) ◽  
pp. 968-981
Author(s):  
Madhur Agarwal
Keyword(s):  
Engineering Design ◽  
Design Problem ◽  
Structural Engineering ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Worst Case ◽  
Design Problems ◽  
Beam Design ◽  
Engineering Design Problems ◽  
Better Than

In real world, the structural engineering design problems are large scale non-linear constrained problems. In the present study, crow search algorithm (CSA) is applied to find the optimal solution of structural engineering design problems such as pressure vessel design problem, welded beam design problem and tension/ compression string design problem. The numerical results are compared with the existing results reported in the literature including metaheuristic algorithms and it is found that the results obtained by the crow search algorithm are better than other existing algorithms. Further, the effectiveness of the algorithm is verified to be better than the existing algorithms by statistical analysis using mean, median, best case, and worst case scenarios. The present study confirms that the crow search algorithm may be easily and effectively applied to various structural design problems.

Multi-Objective Optimal Design of Automotive Engine Using Genetic Algorithm

1998 ◽  
Author(s):  
Kikuo Fujita ◽  
Noriyasu Hirokawa ◽  
Shinsuke Akagi ◽  
Shinji Kitamura ◽  
Hideaki Yokohata
Keyword(s):  
Genetic Algorithm ◽  
Engineering Design ◽  
Design Problem ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Total Power ◽  
Design Problems ◽  
Automotive Engine ◽  
Multi Objective ◽  
Engineering Design Problems

Abstract A genetic algorithm based optimization method is proposed for a multi-objective design problem of an automotive engine, that includes several difficulties in practical engineering optimization problems. While various optimization techniques have been applied to engineering design problems, a class of realistic engineering design problems face on a mixture of different optimization difficulties, such as the rugged nature of system response, the numbers of design variables and objectives, etc. In order to overcome such a situation, this paper proposes a genetic algorithm based multi-objective optimization method, that introduces Pareto-optimality based fitness function, similarity based selection and direct real number crossover. This optimization method is also applied to the design problem of an automotive engine with the design criteria on a total power train. The computational examples show the ability of the proposed method for finding a relevant set of Pareto optima.

Enhancing Design Problem Formulation Skills for Engineering Design Students

2014 ◽  
Author(s):  
Mahmoud Dinar ◽  
Jami J. Shah
Keyword(s):  
Graduate Students ◽  
Engineering Design ◽  
Design Problem ◽  
Mechanical Engineering ◽  
Problem Formulation ◽  
Assessment Methods ◽  
Web Tool ◽  
Design Problems ◽  
Course Work ◽  
Design Students

Problem formulation is an essential design skill for which assessment methods have been less commonly developed. In order to evaluate the progress of a group of graduate students in mechanical engineering design in regard with the problem formulation skill, they were asked to work on three design problems using the Problem Formulator web tool during their course work. Changes in a set of measures elicited from this data were examined in addition to sketches, simulations, and working prototypes. Inventories of requirements and issues, as well as concepts derived from morphological charts were created to assess designers’ skills and outcomes.

scholarly journals ASSESSMENT OF EVIDENCE FOR THE EFFECTIVENESS OF DIFFERENT IDEATION PRACTICES

2012 ◽  
Author(s):  
Antony J Hodgson ◽  
HF Machiel Van Der Loos
Keyword(s):  
Engineering Design ◽  
Design Process ◽  
Design Problem ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Assessment Of Evidence ◽  
Engineering Schools

While most engineering schools substantially agree on the general form of the design process that should be used to address engineering design problems, surprisingly little is known about the actual practical effectiveness of many recommended techniques. In this paper and presentation, we review some of the recent evidence concerning the effectiveness of several well- known practices related to ideation - i.e., generating alternative potential solutions to a design problem.

Cognitive Heuristics in Defining Engineering Design Problems

2016 ◽  
Author(s):  
Jaryn A. Studer ◽  
Seda Yilmaz ◽  
Shanna R. Daly ◽  
Colleen M. Seifert
Keyword(s):  
Engineering Design ◽  
Design Problem ◽  
Empirical Studies ◽  
Cognitive Strategies ◽  
Unique Problem ◽  
Design Problems ◽  
Creative Solutions ◽  
Engineering Design Problems ◽  
Systematic Methodology ◽  
Problem Description

This paper explores “problem exploration heuristics,” or cognitive strategies used to identify and reframe design problem descriptions. The way a design problem is structured influences the types of ideas a designer generates; in particular, some framings may lead to more creative solutions and using multiple framings can support diverse solutions. Most existing problem exploration strategies have not been derived from empirical studies of engineering design practice. Thus, in our work, we drew upon a sample of engineering design problems and analyzed how the problem descriptions evolved during design. Examining iterations on the problem description allowed us to identify heuristics evident in designers’ recrafting of problem descriptions. Heuristics were defined based on the elements in each problem description and their perceived role in transforming the problem. We present a systematic methodology for identifying problem exploration heuristics, and describe five unique Problem Exploration Heuristics commonly observed in structuring design briefs.

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