Geographical Neighbourhood in Particle Swarm Optimization
نویسنده
چکیده
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, nonfixed 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.
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