ArticleScientific reports2025
Optimizing hybrid energy systems for locomotives based on improved grey lag goose algorithm.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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.
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