Abstract: To address the problems of low convergence accuracy and easy local optima trapping in the standard Equilibrium Optimizer (EO) algorithm, this paper proposes an Improved Opposition-based learning Equilibrium Optimizer Algorithm based on Neighborhood Searching (IOLEONS) that combines neighborhood topology search. Firstly, the hyperbolic tangent adaptive operator is used to modify the average concentration value in the balance pool to improve the convergence accuracy of the algorithm. Then, the Euclidean distance between particles is calculated, and a neighborhood search mechanism is introduced to further enhance the algorithm's local development ability, better balancing the algorithm's development and exploration stages. Finally, the dynamic symmetric opposite learning strategy with Chebyshev mapping is used to enhance the population's disturbance ability, improve the diversity of the population, and help the population escape from local optima. The convergence of the improved algorithm is analyzed, and eight benchmark test functions are selected in the simulation experiments. The results of Wilcoxon signed-rank test and Friedman rank test show that the improved algorithm has better optimization performance.

Key words: hyperbolic tangent operator, neighborhood searching, Chebyshev mapping；dynamic symmetric opposite learning；equilibrium optimizer