Comparison of Differential Evolution and Genetic Algorithm in the design of permanent magnet Generators

The inherent complex structure of electrical machines makes an optimum design a challenging task. The Genetic Algorithm (GA) is a benchmark in machine design optimization due to its gradient-free nature and its ability to efficiently find global optima. The Differential Evolution (DE) Algorithm is a population-based, combinatorial algorithm and like the GA, is able to find the global minimum of non differentiable, discontinuous and non-linear functions. This paper uses the design of a Radial Flux Permanent Magnet Generator (RFPMG), with the analytical model as a benchmark and compares the performance of the GA and the DE in terms of their accuracy, their robustness to the population size, the number of generations and computational efficiency. Experimental results show that the DE has advantages over the GA and can effectively improve the convergence speed and optimal quality, hence showing excellent characteristics in the optimum design of electrical machines.