The improved equilibrium optimization algorithm with multiple updating discipline

Abstract

In this paper, a hybridization algorithm of the equilibrium optimization (EO) algorithm and the slime mould (SM) algorithm was proposed. The better performance of the SM algorithm in optimization both benchmark functions and real engineering problems were all encouraging. And consequently, the multiple updating discipline for individuals in swarms was introduced to the EO algorithm. Simulation experiments were carried out and the faster convergence, lower residual errors, higher accuracy verified the improved EO algorithm would perform better than the original one.