particle swarm optimization, machine learning, dynamic pricing
Dynamic pricing is a real-time machine learning problem with scarce prior data and a concrete learning cost. While the Kalman Filter can be employed to track hidden demand parameters and extensions to it can facilitate exploration for faster learning, the exploratory nature of Particle Swarm Optimization makes it a natural choice for the dynamic pricing problem. We compare both the Kalman Filter and existing particle swarm adaptations for dynamic and/or noisy environments with a novel approach that time-decays each particle's previous best value; this new strategy provides more graceful and effective transitions between exploitation and exploration, a necessity in the dynamic and noisy environments inherent to the dynamic pricing problem.
Original Publication Citation
Patrick Mullen, Christopher Monson, and Kevin Seppi. "Particle Swarm Optimization in Dynamic Pricing." In Proceedings of the IEEE Congress on Evolutionary Computation (CEC 26), pp. 4375-4382,Vancouver, B.C.
BYU ScholarsArchive Citation
Monson, Christopher K.; Mullen, Patrick B.; Seppi, Kevin; and Warnick, Sean C., "Particle Swarm Optimization in Dynamic Pricing" (2006). All Faculty Publications. 983.
Physical and Mathematical Sciences
© 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Copyright Use Information