Chaotic immune differential evolution algorithm

A novel chaotic immune differential evolution algorithm (CEDE) is presented. In CEDE, weighted difference is added to the best individual. Using randomness and space ergodicity of chaotic mapping, the best individual is processed by chaotic immune clone operation; In each iteration process, the weighting factor is changed dynamically based on the current aggregation degree and the number of stopping generations; the crossover factor is changed dynamically based on the current evolution speed. Introduce keeping diversity operation, the problem on premature convergence has been solved. DE and CEDE are tested with three well-known benchmark functions. The numerical experiments indicate that the convergence speed of CEDE is considerably superior to DE, has high efficiency and convergence accuracy.