An Improved Equilibrium Optimizer for Solving Optimal Power Flow Problem

With the rapid development of the economy, the quality of power systems has assumed an increasingly prominent influence on people's daily lives. In this paper, an improved equilibrium optimizer (IEO) is proposed to solve the optimal power flow (OPF) problem. The algorithm uses the chaotic equilibrium pool to enhance the information interaction between individuals. In addition, a nonlinear dynamic generation mechanism is introduced to balance the global search and local development capabilities. At the same time, the improved algorithm uses the golden sine strategy to update the individual position and enhance the ability of the algorithm to jump out of local optimums. Sixteen benchmark test functions, Wilcoxon rank sum test and 30 CEC2014 complex test function optimization results show that the improved algorithm has better global searching ability than the basic equilibrium optimizer, as well as faster convergence and a more accurate solution than other improved equilibrium optimizers and metaheuristic algorithms. Finally, the improved algorithm is applied to the standard IEEE 30-bus test systems for different objectives. The obtained results demonstrate that the improved algorithm has better solutions than other algorithms in the literature for solving the optimal power flow problem.

Automated summary

In this paper, an improved equilibrium optimizer (IEO) is proposed to solve the optimal power flow (OPF) problem. The algorithm enhances information interaction between individuals by using the chaotic equilibrium pool and introduces a nonlinear dynamic generation mechanism to balance global search and local development capabilities. The improved algorithm also uses the golden sine strategy to update individual positions and enhance the algorithm's ability to break out of local optima.