Slpso-based x-architecture steiner minimum tree construction

Abstract

The X-architecture Steiner Minimum Tree (XSMT) is the best connection model for multi-terminal nets in global routing algorithms under non-Manhattan structures, and it is an NP-hard problem. And the successful application of Particle Swarm Optimization (PSO) technique in this field also reflects its extraordinary optimization ability. Therefore, based on Social Learning Particle Swarm Optimization (SLPSO), this paper proposes an XSMT construction algorithm (called SLPSO-XSMT) that can effectively balance exploration and exploitation capabilities. In order to expand the learning range of particles, a novel SLPSO approach based on the learning mechanism of example pool is proposed, which is conductive to break through local extrema. Then the proposed mutation operator is integrated into the inertia component of SLPSO to enhance the exploration ability of the algorithm. At the same time, in order to maintain the exploitation ability, the proposed crossover operator is integrated into the individual cognition and social cognition of SLPSO. Experimental results show that compared with other Steiner tree construction algorithms, the proposed SLPSO-XSMT algorithm has better wirelength optimization capability and superior stability.