Splitting for optimization

The splitting method is a well-known method for rare-event simulation, where sample paths of a Markov process are split into multiple copies during the simulation, so as to make the occurrence of a rare event more frequent. Motivated by the splitting algorithm we introduce a novel global optimization method for continuous optimization that is both very fast and accurate. Numerical experiments demonstrate that the new splitting-based method outperforms known methods such as the differential evolution and artificial bee colony algorithms for many bench mark cases. HighlightsMotivated by the splitting algorithm for rare-event simulation, we introduce a novel global optimization method for continuous optimization that is both very fast and accurate, called Splitting for Continuous Optimization (SCO).The idea is to adaptively sample a collection of particles on a sequence of level sets, such that at each level the elite set of particles is "split" into better performing offspring. The particles are generated from a multivariate normal distribution with independent components, via a Gibbs sampler.We compared the performance of SCO with that of the Differential Evolutionary (DE) and Artificial Bee colony (ABC) algorithms through two sets of numerical experiments based on a widely used suite of test functions. From the results, it can be concluded that SCO is competitive with both DE and ABC algorithm on this test suite.