Enhanced Dingo Optimization Algorithm Based on Differential Evolution and Chaotic Mapping for Engineering Optimization

Aiming at the problems of lower initial population diversity and insufficient global search ability of primitive Dingo Optimization Algorithm (DOA), a dingo optimization algorithm based on differential evolution and chaotic mapping (DCDOA) is proposed. In DCDOA, differential evolution is introduced to randomly generate a new population to increase the diversity of the dingo population; Tent chaotic map can effectively faster the convergence rate and strengthen the global search ability. Taking CEC2019 as the test function set, they are performed by DCDOA and three other algorithms. Experiments show that DCDOA has superior with convergence performance and stronger robustness. Furthermore, to verify its performance in solving engineering optimization problems (pressure vessel and car side collisions design). The experimental results demonstrate that DCDOA conserves 49.85% and 4.62% in economic costs for pressure vessels and vehicle side collisions compared to DOA, respectively, verifying the practicality and superiority of DCDOA for engineering optimization problems.