Optimizing hybrid energy systems for locomotives based on improved grey lag goose algorithm

This work presents an Improved Grey Lag Goose Optimization (IGLGO) algorithm for minimizing the total cost of a hybrid locomotive energy system integrating polymer electrolyte membrane (PEM) fuel cells with lithium-ion batteries. The IGLGO employs a dynamic grouping mechanism and fractional calculus to ensure that a near-optimal exploration-exploitation balance is achieved so that local optima can be avoided. IGLGO delivered a more cost-effective design as compared to standard Grey Lag Goose Optimization (GLGO) and other metaheuristics. For a 2% track slope, the optimally sized IGLGO system, at $3.78 million, was found to be cheaper than GLGO ($4.41 million) and the Dwarf Mongoose Optimizer ($4.84 million). These results prove that IGLGO offers a very solid yet economical optimization framework for sustainable railway propulsion systems.