GA, which is one of the stochastic methods of optimization, has been commonly used for the optimal design machine systems. Therefore, some optimization methods such as genetic algorithm (GA) have been developed to solve complex optimization problems recently (Rao & Tiwari, 2007). If the optimization problem involves the objective function and constraints that are not stated as explicit functions of the design variables or which are too complicated to manipulate, it is hard to solve by classical optimization methods. Many practical optimum design problems are characterized by mixed continuous-discrete variables, and discontinuous and non-convex design spaces. When the number of design parameters increase, the complexity increases drastically. ![]() The main disadvantages of them are slow convergence along with local minima (or maxima) problems. They are deterministic in nature and use only a few geometric design variables due to their complexity and convergence problems (Chakraborthy, Kumar, Nair, & Tiwari, 2003). ![]() Conventional methods have been widely used in various mechanical design problems.
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