Development of new generation hybrid techniques for innovative product design in automotive industry

In this dissertation, design optimization of an automotive suspension arm taken from automotive industry is carried out considering the fatigue behavior of the component. Examination of the fatigue behaviour of the component is important due to the function of suspension system. Firstly, an initial 3-D design is developed. Fatigue analysis of the initial model is carried out in ANSYS Workbench. Latin hypercube sampling method and radial basis function method are used for obtaining objective and constraint functions respectively. In this dissertation, a novel optimization method based on charged system search optimization algorithm and Nelder- Mead local search algorithm is developed. The developed new hybrid optimization method is used to solve engineering test problems taken from the literature and to optimize the automotive suspension arm. The optimum suspension arm model is lighter as intial design considering fatigue life constraints. The results of the single-objective benchmark problems taken from the literature and the case study for optimum design of the automotive suspension arm have demonstrated the superiority of the developed hybrid optimization method over pure charged system search and the other optimization techniques that are representative of the state-of-the-art in the evolutionary optimization literature in terms of solution quality and convergence rates to the global optimum.