Summary
The particle swarm optimization (PSO) algorithm has gained increasing popularity in the last few years mainly because of its relative simplicity and its good overall performance, particularly in continuous optimization problems. As PSO is adopted in more types of application domains, it becomes more important to have well-established methodologies to assess its performance. For that purpose, several test problems have been proposed. In this chapter, we review several state-of-the-art test function generators that have been used for assessing the performance of PSO variants. As we will see, such test problems sometimes have regularities which can be easily exploited by PSO (or any other algorithm for that sake) resulting in an outstanding performance. In order to avoid such regularities, we describe here several basic design principles that should be followed when creating a test function generator for single-objective continuous optimization.
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Barrera, J., Coello Coello, C.A. (2011). Test Function Generators for Assessing the Performance of PSO Algorithms in Multimodal Optimization. In: Panigrahi, B.K., Shi, Y., Lim, MH. (eds) Handbook of Swarm Intelligence. Adaptation, Learning, and Optimization, vol 8. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17390-5_4
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