In　this　paper　we　present　an　algorithm　for　outlier　detection　in　high-dimensional　spaces　based　on　constrained　particle　swarm　optimization　techniques.　The　concept　of　outliers　is　defined　as　sparsely　populated　patterns　in　lower　dimensional　subspaces.　The　search　for　best　abnormally　sparse　subspaces　is　done　by　an　innovative　use　of　particle　swarm　optimization　methods　with　a　specifically　designed　particle　coding　and　conversion　strategy　as　well　as　some　dimensionality-preserving　updating　techniques.　Experimental　results　show　that　the　proposed　algorithm　is　feasible　and　effective　for　high-dimensional　outlier　detection　problems.