Searching Ground States of Ising Spin Glasses with Genetic Algorithms and Binary Particle Swarm Optimization

Abstract

This paper compares the performance of two evolutionary computation paradigms, genetic algorithms (GAs) and particle swarm optimization (PSO), on the problem of finding ground states of Ising spin glasses. The algorithms are tested on various configurations of J = ±1 Ising spin glasses on 2D and 3D lattices with nearest neighbor interactions and periodic boundaries. For configurations on 2D lattices, the performance of GAs and PSO was studied in the absence, as well as in the presence of an external magnetic field. For configurations on 3D lattices, the performance was also studied for different values of the coupling constant J. Results indicate that PSO outperforms GAs with respect to the quality of the solutions.KeywordsGenetic AlgorithmParticle Swarm OptimizationProblem InstanceIsing ModelSpin GlassThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.