Chapter 10: Intelligent Optimization Algorithms

Yuehua Gao

School of Traffic & Transportation Engineering, Dalian Jiaotong University, Dalian, Liaoning, China

Peng Zhao

State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang, China

Lih-Sheng Turng

Polymer Engineering Center, Department of Mechanical Engineering, University of Wisconsin–Madison, Madison, Wisconsin, USA

Huamin Zhou

State Key Laboratory of Materials Processing and Die & Mould Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China

In the last chapter, noniterative optimization technologies were studied. However, these methods are unable to provide true optimal design in a given design space; that is, numerical optimization is not applied. Conventional numerical optimization methods need gradient information, which is rather complicated and difficult for optimization when using black box commercial software. Hence, in this chapter, intelligent optimization algorithms will be introduced.

Intelligent optimization algorithms simulate some natural rules and can find the global optimum in the design space. Of these algorithms, which avoid sensitivity analysis and are easy to use, genetic algorithms (GAs) are applied most widely. The primary disadvantage of these algorithms is that they need numerous objective function evaluations for convergence. Therefore, it is expensive and impractical if the techniques involve a time-consuming simulation for each function ...

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