Wang X.TSAN MING CHOIYue X.Zhang M.Du W.2022-05-302022-05-302020https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081722675&doi=10.1109%2fTIE.2019.2926043&partnerID=40&md5=df657b65b497f57539a7d42eec5db5c4https://scholars.lib.ntu.edu.tw/handle/123456789/612185The application mapping problem is an NP-hard combinatorial optimization problem in network-on-chip (NoC) design. Applications of size (n >30) cannot be solved optimally by an exact algorithm in reasonable time, and the evolutionary algorithms have drawn the attention of NoC researchers. In this paper, we propose a new effective optimization method based on the discrete particle swarm optimization framework, which includes the novel principles for representation, velocity computing, and position-updating of the particles. In our proposed method, particles are allowed to swing between elite and regular pools, and a simple local search procedure is applied on elite particles to exploit the promising solutions. Extensive computational studies using standard benchmark instances and task graphs for free (TGFF) random instances reveal that the proposed optimization algorithm is able to attain the best results, and thus competes very favorably with the previously proposed heuristic approaches. A stability analysis and the two-sided Wilcoxon rank sum tests are also presented to shed light on the robust behavior of the algorithm. ? 1982-2012 IEEE.Application mapping; discrete particle swarm optimization; local search; network-on-chip (NoC)Benchmarking; Combinatorial optimization; Graph algorithms; Heuristic methods; Local search (optimization); Mapping; Particle size analysis; Particle swarm optimization (PSO); Servers; Application mapping; Combinatorial optimization problems; Computational studies; Discrete particle swarm optimization; Local search; Network-on-chip(NoC); Optimization algorithms; Wilcoxon rank sum test; Network-on-chipjournal article10.1109/TIE.2019.29260432-s2.0-85081722675