Particle swarm optimization with adaptive linkage learning

In many problems, the quality of solutions and computational effort required by optimization algorithms can be improved by exploiting knowledge found in the linkages or interrelations between problem dimensions or components. These linkages are sometimes known a priori from the nature of the itself; in other cases linkages can be learned by sampling the data space prior to the application of the optimization algorithm. This paper presents a new version of the particle swarm optimization algorithm (PSO) that utilizes linkages between components, performing more frequent simultaneous updates on subsets of particle position components that are strongly linked. Prior to application of this linkage-sensitive PSO algorithm, problem specific linkages can be learned by examining a randomly chosen collection of points in the search space to determine the correlations in fitness changes resulting from perturbations in pairs of components of particle positions. The resulting algorithm, adaptive-linkage PSO (ALiPSO) has performed significantly better than the classical PSO, in simulations conducted so far on several test problems.