Application study on a novel differential evolution algorithm
一种新型的差分演化算法及其应用研究

A novel algorithm based on simple diversity rules and Simple Improved Differential Evolution (SIDE) algorithm was proposed in this paper. It is characterized with the following new features: 1) introducing a hybrid self-adaptive crossover-mutation operator, which can enhance the search ability and exploit the optimum offspring; 2) using a new constraint-handling technique to maintain the diversity of the population; 3) simplifying the scaling factor F of the Original Differential Evolution (ODE) algorithm, which can reduce the parameters of the algorithm and make it easy to use for engineers. Our algorithm was tested on 13 benchmark optimization problems with linear or/and nonlinear constraints and compared with other state-of-the-art evolutionary algorithms. The experimental results demonstrate that the performance of SIDE outperforms other evolutionary algorithms in terms of the quality of the final solution and the stability; and its computational cost (measured by the average number of fitness function evaluations) is lower than the cost required by the other techniques compared.