Fuzzy Optimization on V-belt Drives of Automobile Powertrain in Hybrid Genetic Algorithm

As belt drives play an important part in absorbing shock loads and in damping out and isolating the effects of vibration, may be used for long center distance, and slipping may occur when an overload is applied to the system, which protects other elements against damage, therefore it is very important to adopt optimization methods to design the V-belt drive. Considering the random character of the design parameters and load-bearing capacity, the fuzzy optimization mathematic model is established to minimize the number of V-belt in conveying drive. Global algorithms are known for their slower convergence to the true global optimum once the optimum region is found, This drawback of the genetic algorithm can be overcome by combining it with local gradient-based algorithms, A hybrid algorithm combining the genetic algorithm and local algorithm is developed in this paper. The genetic algorithm can be combined with fuzzy logic to efficiently find a more accurate solution.