Abstract
In Particle Swarm Optimization, the behavior of particles depends on the parameters of movement formulas. In our research, we identify types of particles based on their movement trajectories. Then, we propose new rules of particle classification based on the two attributes of the measure representing the minimum number of steps necessary for the expected particle location to obtain its stable state. The new classification clarifies the division into types of particles based on the observation of different shapes of their movement trajectories.
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Wójcik, K., Kulpa, T., Trojanowski, K. (2020). Particle Classification Based on Movement Behavior in IPSO Stochastic Model. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2020. Lecture Notes in Computer Science(), vol 12415. Springer, Cham. https://doi.org/10.1007/978-3-030-61401-0_53
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DOI: https://doi.org/10.1007/978-3-030-61401-0_53
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