Daniel Looi Jun JieLee Wai KongLiew Soung YueChee Siang Wong2026-10-142026-10-142026-03-06979833156997610.1109/IMED68921.2026.11484321https://dspace-cris.utar.edu.my/handle/123456789/11741This paper presents a parallel implementation of a genetic algorithm (GA) to solve the Travelling Salesman Problem (TSP) using NVIDIA's Compute Unified Device Architecture (CUDA) SDK on a GPU. The proposed approach exploits the massive parallelism of modern GPUs and the highly optimized primitives of Thrust library to accelerate sorting and elitism selection on GA. The Thrust-based implementation demonstrates significant improvements in computation time and scalability compared to sequential and multi-threaded CPU implementations. Results obtained on multiple TSPLIB datasets show up to 21x speedup with comparable or better solution quality, validating the efficiency of GPU-accelerated metaheuristics implementation using CUDA and Thrust library. © 2026 IEEE.en-USCUDAGenetic AlgorithmGPU ComputingParallel Meta-heuristic AlgorithmsThrust LibraryTravelling Salesman ProblemGraphics processing unitHeuristic algorithmsParallel algorithmsParallel architecturesProblem solvingProgram processorsCompute unified device architectureDevice architecturesGPU computingMeta-heuristics algorithmsParallel genetic algorithmsParallel implementationsParallel meta-heuristic algorithmSalesman problemThrust libraryTravelling salesmanGenetic algorithmstext::conference output::conference proceedings::conference paper