Application of Improved Equilibrium Optimizer Algorithm to Constrained Optimization Problems

Abstract

Aiming at the problems of the equilibrium optimizer algorithm, such as difficult balance between population exploration and exploitation, insufficient information of particle evolution and prematurity, an improved equilibrium optimizer algorithm is proposed. Firstly, in the iterative stage optimized by the algorithm, the sinusoidal pool strategy is used to balance the exploration and development capabilities dynamically. In the early stage of iteration, a large range of global exploration is carried out through the sinusoidal decrease of fixed angular frequency to expand the algorithm to explore unknown areas in the search space and enhance the ability of discovering potential high-quality particles. At the end of iteration, local exploitation is carried out by sinusoidal increase of changing angular frequency to balance exploration and exploitation adaptively and improve the optimization accuracy of the algorithm. Secondly, the adaptive priority gravity strategy introduces the current optimal particle information to overcome the lack of evolution information, enriches the evolution information of the population particles by incorporating the uniform distribution and beta distribution together, improves the information exchange rate between particles, enhances the escape of the particles from the local area, and achieves the goal of guiding the population to converge rapidly towards the global optimum. Finally, 16 benchmark functions, CEC2017 functions, Friedman test, Wilcoxon rank sum test and two real-world engineering constraint optimization problems are used to test the optimization ability of the proposed algorithm. Experimental results show that the proposed algorithm has higher optimization accuracy and faster convergence speed compared with other new proposed intelligent algorithms.

Keywords

• equilibrium optimizer algorithm; exploration and exploitation; constrained engineering optimization problem