The Improved Equilibrium Optimization Algorithm with Averaged Candidates
Zheng-Ming Gao, Juan Zhao, Xuejun Tian · Journal of Physics Conference Series · 2020
Abstract In this paper, we proposed the improvement of the newly raised equilibrium optimization (EO) algorithm by hybridizing the grey wolf optimization (GWO) algorithm. Simulation experiments were carried out and results showed that the hybrid EO algorithm with averaged candidates would perform better than the original one. Considering the better results and the smooth convergence curve on different types of benchmark functions, we recommend to withdraw the concept of equilibrium pool in the construction of the EO algorithm henceforth, and keep the guiding equation relevant to the averaged concentration of four best candidates instead.