An Improved Equilibrium Optimizer with a Decreasing Equilibrium Pool

Big Data is impacting and changing the way we live, its core lies in use of machine learning to extract valuable information from huge amounts data. Optimization problems are a common problem many steps learning. In face complex optimization problems, evolutionary computation has shown advantages over traditional methods. Therefore, researchers working on improving performance algorithms for solving various The equilibrium optimizer (EO) member inspired by mass balance model environmental engineering. Using particles their concentrations as search agents, it simulates process finding states optimization. this paper, propose an improved (IEO) based decreasing pool. IEO provides more sources particle updates maintains higher population diversity. It can discard some exploration later stages enhance exploitation, thus achieving better balance. verified using 29 benchmark functions IEEE CEC2017, dynamic economic dispatch problem, spacecraft trajectory artificial neural network training problem. addition, changes diversity computational complexity brought proposed method analyzed.