Evolutionary Computing in Engineering Design

Evolutionary Computing in Engineering Design

Rajkumar Roy (Cranfield University, UK), Ashutosh Tiwari (Cranfield University, UK), Yoseph Tafasse Azene (Cranfield University, UK) and Gokop Goteng (King Abdullah University of Science and Technology, Saudi Arabia)
DOI: 10.4018/978-1-59904-582-5.ch009
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This chapter presents an overview of the application of evolutionary computing for engineering design. An optimal design may be defined as the one that most economically meets its performance requirements. Optimisation and search methods can assist the designer at all stages of the design process. The past decade has seen a rapid growth of interest in stochastic search algorithms, particularly those inspired by natural processes in physics and biology. Impressive results have been demonstrated on complex practical optimisation of several schools of evolutionary computation. Evolutionary computing unlike conventional technique, have the robustness for producing variety of optimal solutions in a single simulation run, giving wider options for engineering design practitioners to choose from. Despite limitations, the act of finding the optimal solution for optimisation problems has shown a substantial improvement in terms of reducing optimisation process time and cost as well as increasing accuracy. The chapter aims to provide an overview of the application of evolutionary computing techniques for engineering design optimisation and the rational behind why industries and researchers are in favor of using it. It also presents the techniques application trend rise in the past decade.

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