Particle algorithms for optimization on binary spaces
Computation
2012-04-09 v2 Optimization and Control
Abstract
We discuss a unified approach to stochastic optimization of pseudo-Boolean objective functions based on particle methods, including the cross-entropy method and simulated annealing as special cases. We point out the need for auxiliary sampling distributions, that is parametric families on binary spaces, which are able to reproduce complex dependency structures, and illustrate their usefulness in our numerical experiments. We provide numerical evidence that particle-driven optimization algorithms based on parametric families yield superior results on strongly multi-modal optimization problems while local search heuristics outperform them on easier problems.
Cite
@article{arxiv.1111.0574,
title = {Particle algorithms for optimization on binary spaces},
author = {Christian Schäfer},
journal= {arXiv preprint arXiv:1111.0574},
year = {2012}
}