An X-Architecture SMT Algorithm Based on Competitive Swarm Optimizer

Abstract

The X-architecture Steiner Minimum Tree (XSMT) is the best connection model of non-Manhattan multi-terminal nets in global routing, and it is an NP-hard problem. Particle Swarm Optimization (PSO), with its efficient searching ability and self-organizing ability, has become a powerful tool for constructing the XSMT. However, PSO is prone to fall into the local optimum due to its excessive exploitation intensity. To keep a smooth trade-off between exploitation and exploration capabilities of PSO, maintain the diversity of the population, and obtain a better solution, this paper proposes an XSMT algorithm based on Competitive Swarm Optimizer (called CSO-XSMT). The algorithm utilizes the methods of pairwise competition and roulette wheel selection to randomly select the learning objects of particles so as to enhance the exploration ability of the population and improve the algorithm performance. Meanwhile, to further reduce the wirelength of the Steiner tree, a refine strategy based on sharing edges is proposed, which adjusts the Steiner tree obtained by CSO to improve the quality of the final routing tree. Experimental results show that compared with other Steiner tree construction algorithms, the proposed algorithm has better wirelength optimization capability and superior stability.