Affiliation:
1. Swarmotics LLC, USA
2. University of Arizona, USA
Abstract
The most common versions of particle swarm optimization (PSO) algorithms are rotationally variant. It has also been pointed out that PSO algorithms can concentrate particles along paths parallel to the coordinate axes. In this paper, the authors explicitly connect these two observations by showing that the rotational variance is related to the concentration along lines parallel to the coordinate axes. Based on this explicit connection, the authors create fitness functions that are easy or hard for PSO to solve, depending on the rotation of the function.
Subject
Artificial Intelligence,Computational Theory and Mathematics,Computer Science Applications
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