The Improved Equilibrium Optimization Algorithm with Best Candidates

The best candidates play the important role during the exploration and exploitation of individuals in almost all of the swarm-based algorithms. More best candidates were involved in such procedure of the grey wolf optimization algorithm and the newly raised equilibrium optimization (EO) algorithm we called here. The EO algorithm introduced four best candidates besides their average and constructs an equilibrium pool. However, the best candidates would still perform the guiding role and a novel improvement was introduced. Experiments on some classical benchmark functions were carried out and results show the better performance than the original one. Consequently, the EO algorithm should be improved and focused more on the best candidates furthermore.