An improved whale algorithm (DEOBWOA) based on differential evolution and elite opposition-based learning is proposed to solve the problem that the intelligent optimization algorithm is easy to fall into the local optimum and the convergence precision in dealing with the nonlinear optimization problem is poor. The algorithm uses the opposing search initialization

elite opposition-based learning and combines with differential evolution

which can improve the convergence precision and convergence speed of the whale optimization (WOA) algorithm effectively and improve the ability to jump out of local optimum. 8 standard test functions are used to do simulation experiment. The results show that DEOBWOA algorithm has a better performance than WOA

heterogeneous comprehensive learning particle swarm optimization (HCLPSO) and differential evolution (DE). Finally

the kinetic model of residue hydrogenation was established

but there are many typical nonlinear constraints in the process of residue hydrogenation. So DEOBWOA was used to optimize the kinetic model parameters of residue hydrogenation in a refinery residue

which indicates the algorithm can deal with the practical engineering optimization problem.