Application of Pathfinder, Honey Badger, Red Fox and Horse Herd algorithms to phase equilibria and stability problems

Thermodynamic problems are usually formulated as optimization problems but are often nonlinear and nonconvex and require robust optimization techniques to find the global solution. This work studied four newly developed swarm-based stochastic optimization algorithms, which are Honey badger algorithms (HBA), Pathfinder algorithms (PFA), Horse herd optimization algorithms (HOA) and Red fox optimization (RFO). They were applied to solving several phase stability and equilibrium problems with different degrees of complexity. The strengths and weaknesses of all the algorithms were compared. The stochastic algorithms solved the problems and obtained the global minimum value with a high success rate. HBA and HOA performed very well in all the problems considered, though the performance of HBA was slightly better than that of HOA. The performance of PFA closely followed while RFO was poor and had the worst performance among the four algorithms, but with the inclusion of a local optimizer, there was a significant improvement. Therefore, HBA and HOA are robust and reliable for solving phase stability and equilibrium problems.