Parallel computing enables whole-trip train dynamics optimizations

Due to the high computing demand of whole-trip train dynamicssimulations and the iterative nature of optimizations, whole-triptrain dynamics optimizations using sequential computing schemesare practically impossible. This paper reports advancements inwhole-trip train dynamics optimizations enabled by using the parallelcomputing technique. A parallel computing scheme forwhole-trip train dynamics optimizations is presented and discussed.Two case studies using parallel multiobjective particleswarm optimization (pMOPSO) and parallel multiobjectivegenetic algorithm (pMOGA), respectively, were performed to optimizea friction draft gear design. Linear speed-up was achievedby using parallel computing to cut down the computing time from18 months to just 11 days. Optimized results using pMOPSO andpMOGA were in agreement with each other; Pareto fronts wereidentified to provide technical evidence for railway manufacturersand operators.