At University College, Dublin, I took the excellent course "Natural Programming" by Dr. Michael O'Neill. A part of it was to write your own paper and  let the other students review it.

As a topic, I chose Particle Swarm Optimization. I was interested in defining the neighbourhood of particles geographically, which is how swarms in nature act. The standard algorithms mostly use fixed neighbourhood relations.

It was received very well and so I present it here for you to view/download.

I'll leave you with the abstract:
 

In Particle Swarm Optimization (PSO) with local neighbourhood, the social part of change in the particle's velocity is computed considering the performance of a set of neighbours. Almost all of the literature uses neighbourhood relations of a fixed topology. This paper introduces a method that computes a local optimum based on geographical, non- fixed neighbourhood in Euclidian space. It compares the two approaches in performance and geographical behaviour. The results show that swarms with geographical neighbourhood perform worse in terms of fitness. Furthermore, the results indicate that swarms with fixed topologies start by exploring the search space due to initial random distribution and then turn to exploitation because of emerged geographical neighbourhood.

I also published the code I used for the simulations.