Optimal Designs by Means of Genetic Algorithms

Optimal Designs by Means of Genetic Algorithms

Lata Nautiyal (Graphic Era University, India), Preeti Shivach (Graphic Era University, India) and Mangey Ram (Graphic Era University, India)
DOI: 10.4018/978-1-5225-3035-0.ch007
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Abstract

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.
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Introduction

Designing process of a product includes a number of optimization problems. Such as, when we design an engine controller, the fuel economy and power performance should be optimized. And both of these factors are also affected by various other parameters such as temperature, pressure etc. Therefore, controlling fuel injection and air-fuel ratio with reference to these parameters is an optimizing problem and also a complex one. There are various issues when we deal with the engineering problems. First one is to improve design competency. Industries have to develop good products with in a time bound because of competition or requirements. Second issue is to optimize the design process. The third issue is to achieve robustness requirements.

Although design problems are typically demarcated more indefinitely and have a variety of precise answers, but the methodology may require backtracking as well as iteration. Designing process is a depending process and the solution depends on unpredicted difficulties and modifications as it matures.

The Wright brothers didn’t come to know problems until they actually build and test their primary gliders. The fundamental five steps for resolving the problems associated with designing or design problems are as follows:

  • 1.

    Describe the overall problem

  • 2.

    Collect relevant information

  • 3.

    Produce several solutions

  • 4.

    Evaluate and choose a solution

  • 5.

    Assess and execute the solution

The first phase is Describe the overall problem. Problem description step mainly consists of a list of requirements of the customer and also information like function and features of the product among other things.

In the second step, appropriate material is collected for the design and functional specifications of the product. For this purpose, similar products available in the market are reviewed. When the relevant information is collected successfully then the duty of the design teams, manufacturing teams, and marketing teams is to generate large number of options to accomplish the objectives.

Evaluate and choose a solution step is all about the comprehensive analysis of the solutions and results in finding out of the absolute design that finest fits the final needs of the customer. By applying this step, a sample is developed and tested against functionality to validate and possibly revise the design. This process is depicted in Figure 1.

Figure 1.

Design Process

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Design Problems In Engineering

As contemporary computational and modeling technologies propagate all over the world, engineering design profoundly depends on computer modeling and simulation to quicken design cycles as well as to save the cost (Xiaopeng, 2007; Roosenburg & Eekeks, 1995). A complicated design problem will comprise of various design constraints. Discovering design space and finding best solutions are still major issues and the challenges for complex systems.

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