Neighborhood Re-structuring in Particle Swarm Optimization
This paper considers the use of randomly generated directed graphs as neighborhoods for particle swarm optimizers (PSO) using fully informed particles (FIPS), together with dynamic changes to the graph during an algorithm run as a diversity-preserving measure. Different graph sizes, constructed with a uniform out-degree were studied with regard to their effect on the performance of the PSO on optimization problems. Comparisons were made with a static random method, as well as with several canonical PSO and FIPS methods. The results indicate that under appropriate parameter settings, the use of random directed graphs with a probabilistic disruptive re-structuring of the graph produces the best results on the test functions considered.
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