Particle Swarm Optimization Hybrids for Searching Ground States of Ising Spin Glasses

This paper compares two particle swarm optimization (PSO) hybrids on the problem of searching ground states of Ising spin glasses. The Ising model is one of the most widely used models for disordered systems in statistical physics. Finding the ground state of an Ising spin glass can be expressed as the problem of determining the minimum weighted cut in a graph. We tackle this problem with canonical PSO, a hill climber and two hybrid algorithms. The results show that both hybrids obtain better results than the classical heuristics they are built upon, one of the hybrids significantly outperforming the others. Hybrid methods created as a combination of PSO with a local search procedure provide promising results for this class of problems.