Avoidance strategies in particle swarm optimisation

Particle swarm optimisation (PSO) is an optimisation algorithm in which particles traverse a problem space moving towards promising locations which either they or their neighbours have previously visited. This paper presents a new PSO variant with the Avoidance of Worst Locations (AWL). This variation was inspired by animal behaviour. In the wild, an animal will react to negative stimuli as well as positive, e.g. an animal looking for food will also be conscious of danger. PSO AWL enables particles to remember previous poor solutions as well as good. As a result, the particles change the way they move and avoid known bad areas. Balancing the influence of these poor locations is vital. The research in this paper found that a small influence from bad locations on the particles leads to a significant improvement on overall performance when compared to the standard PSO. When compared to previous implementations of worst location memory, PSO AWL demonstrates vast improvements.