Survey of improved equalization optimizer algorithm

Equilibrium optimizer (EO) is a new intelligent algorithm inspired by the dynamic mass balance of control volume. Because of its simple structure, strong search ability and easy realization, it is widely used in continuous or discrete, single objective or multi-objective problems. The EO algorithm can not be improved effectively in the exploration and local optimization stage, which leads to the stagnation of the EO algorithm. This paper systematically analyzes and summarizes the improvement ideas or methods of EO algorithm in recent two years and the progress made in various fields at home and abroad. Firstly, EO algorithms are divided into continuous and discrete ones based on the physical background, workflow and related concepts of the balanced optimizer algorithm. Secondly, the advantages and disadvantages of control parameters, hybrid algorithm and multi-objective optimization are analyzed and summarized, and then the advantages and disadvantages of various strategies in the three improvement directions are extracted. Thirdly, the application fields of the improved EO algorithm are classified. At present, the improved balance optimizer algorithm has proved its feasibility and superiority and achieved better control effect. Finally, the further research category of EO algorithm is proposed, which provides some theoretical reference value for scholars.