A novel honey badger algorithm with golden sinusoidal survival rate selection for solving optimal power flow problem

The original honey badger algorithm (HBA), as one of the newest meta-heuristic techniques, has a better convergence speed. However, HBA has the potential disadvantages of poor convergence accuracy, insufficient balance between exploration and exploitation, and the tendency to slip into local optimization. In this paper, a novel golden sinusoidal survival honey badger algorithm is proposed. Firstly, tent chaotic opposition learning is applied to the initial individual generation so that they can be distributed throughout the entire search area, which improves the precision of initial populations. Secondly, in the position update phase, we use a nonlinear convergence strategy to balance the weight of prey in the next walk and to increase the global search ability. Then, the quality of honey badger is evaluated by the golden sinusoidal survival rate strategy and precocious individuals are updated by Levy flight, which can avoid the premature convergence of the algorithm. Finally, 23 benchmark functions, CEC2019 tests functions and optimal power flow problems are used to evaluate the effectiveness of the improved algorithm. Test results indicate that the algorithm's ability to evolve, to extract the local optimal and to detect the global optimal placements are improved.