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Genetic Programming Approaches in Design and Optimization of Mechanical Engineering Applications

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Nonlinear Approaches in Engineering Applications

Abstract

The development of modern engineering systems has introduced increasing levels of complexity and uncertainty over time. Combined with the design philosophy of engineering itself, this has given rise to many studies addressing the simple or multi-objective optimization problems present in these complex systems. Although conventional approaches can be applied to engineering optimization depends largely on the nature of problem, but they suffered to provide some quick and reasonable feedback to designers and cannot be challenging to further possible problems. Nevertheless, heuristic approaches that apply mixtures of different exploratory with or without traditional search and optimization methods are proposed to solve such complex problems. This chapter briefly provides the conventional optimizations and basic knowledge about the most widely implemented heuristic optimization techniques, as well as their application in optimization problems in mechanical engineering systems. It also presents the genetic programming that searches the space of possible computer programs which is extremely fit for solving the complex problem in truss structure design and optimization of mechanical engineering. Genetic programming employs tree structure of computer programs as individuals in its initial population, which gets evolved through generations by the algorithm operators to reach the optimum solution. To prove the ability of the genetic programming to solve complex mechanical engineering problems, a case study in design of truss with discrete design variables will be examined. In this example, genetic programming employs to find the optimum topology and discrete cross-section sizes of 10-bar truss problem which is a nonlinear problem subjected to different constraints such as the stability, maximum allowable stress and displacement in the truss nodes, and critical buckling load. As results and in comparison, with other state-of-art approaches, genetic programming finds a lighter truss structure with fewer elements because it could be constructed a tree-based expression to explore the search space.

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Authors and Affiliations

  1. School of Engineering, RMIT University, Melbourne, VIC, Australia

    Hamid Khayyam & Reza N. Jazar

  2. Faculty of Mechanical Engineering, University of Guilan, Rasht, Iran

    Ali Jamali

  3. Optimisation and Logistics, School of Computer Science, The University of Adelaide, Adelaide, Australia

    Hirad Assimi

  4. Xiamen University of Technology, Xiamen, China

    Reza N. Jazar

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  1. Hamid Khayyam
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Correspondence to Hamid Khayyam .

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  1. Xiamen University of Technology, Xiamen, China, School of Engineering, RMIT University, Bundoora, VIC, Australia

    Reza N. Jazar

  2. Xiamen University of Technology, Xiamen, China, University of Regina, Industrial Systems Engineering, REGINA, SK, Canada

    Liming Dai

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Khayyam, H., Jamali, A., Assimi, H., Jazar, R.N. (2020). Genetic Programming Approaches in Design and Optimization of Mechanical Engineering Applications. In: Jazar, R., Dai, L. (eds) Nonlinear Approaches in Engineering Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-18963-1_9

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