Optimized Spatial Partitioning via Minimal Swarm Intelligence

TL;DR

This paper introduces simple, efficient spatial partitioning algorithms based on neighbor locations, improving flexibility over traditional CVT methods, and demonstrates their application in sensor networks and optimization tasks.

Contribution

The paper presents novel neighbor-based partitioning schemes that are easier to implement and extend to high dimensions, with qualitative assessment techniques and applications in sensor networks and particle swarm optimization.

Findings

Partitioning schemes effectively incorporate weighted regions.

Algorithms are simpler and more adaptable than CVT.

Improved particle swarm optimizer results with partitioned initial positions.

Abstract

Optimized spatial partitioning algorithms are the corner stone of many successful experimental designs and statistical methods. Of these algorithms, the Centroidal Voronoi Tessellation (CVT) is the most widely utilized. CVT based methods require global knowledge of spatial boundaries, do not readily allow for weighted regions, have challenging implementations, and are inefficiently extended to high dimensional spaces. We describe two simple partitioning schemes based on nearest and next nearest neighbor locations which easily incorporate these features at the slight expense of optimal placement. Several novel qualitative techniques which assess these partitioning schemes are also included. The feasibility of autonomous uninformed sensor networks utilizing these algorithms are considered. Some improvements in particle swarm optimizer results on multimodal test functions from partitioned initial positions in two space are also illustrated. Pseudo code for all of the novel algorithms depicted here-in is available in the supplementary information of this manuscript.