Aiming at the shortcomings of Equilibrium Optimizer （EO） such as low optimization accuracy， slow convergence and being easy to fall into local optimum， a new EO in consideration with distance factor and Elite Evolutionary Strategy （EES） named E-SFDBEO was proposed. Firstly， the distance factor was introduced to select the candidate solutions of the equilibrium pool， and the adaptive weight was used to balance the fitness value and distance， thereby adjusting the exploration and development capabilities of the algorithm in different iterations. Secondly， the EES was introduced to improve the convergence speed and accuracy of the algorithm by both elite natural evolution and elite random mutation. Finally， the adaptive t-distribution mutation strategy was used to perturb some individuals， and the individuals were retained with greedy strategy， so that the algorithm was able to jump out of the local optimum effectively. In the simulation experiment， the proposed algorithm was compared with 4 basic algorithms and 2 improved algorithms based on 10 benchmark test functions and Wilcoxon rank sum test was performed to the algorithms. The results show that the proposed algorithm has better convergence and higher solution accuracy.