A novel parallel chaos differential evolution algorithm(PCDE)is presented

where differential evolution(DE)and chaos searching run concurrently

and in each iteration process the weighting factor is changed dynamically according to the current aggregation degree and number of stopping generations

the crossover factor is changed dynamically according to the current evolution rate. The randomness and space ergodicity of chaos mapping are considered to enlarge the searching range. The catastrophic factor is introduced to avoid premature convergence. DE and PCDE are tested with three well-known benchmark functions. The experiments show that PCDE is significantly superior to DE with higher efficiency to solve complicated optimization problems.