A PSO-based algorithm for reserve price optimization in online ad auctions

Jason Rhuggenaath, Alp Akcay, Yingqian Zhang, Uzay Kaymak

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

6 Citations (Scopus)
365 Downloads (Pure)


One of the main mechanisms that online publishers use in online advertising in order to sell their advertisement space is the real-time bidding (RTB) mechanism. In RTB the publisher sells advertisement space via a second-price auction. Publishers can set a reserve price for their inventory in the second-price auction. In this paper we consider an online publisher that sells advertisement space and propose a method for learning optimal reserve prices in second-price auctions. We study a limited information setting where the values of the bids are not revealed and no historical information about the values of the bids is available. Our proposed method leverages the dynamics of particles in particle swarm optimization (PSO) to set reserve prices and is suitable for non-stationary environments. We also show that, taking the gap between the winning bid and second highest bid into account leads to better decisions for the reserve prices. Experiments using real-life ad auction data show that the proposed method outperforms popular bandit algorithms.
Original languageEnglish
Title of host publication2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Number of pages9
ISBN (Electronic)9781728121536
Publication statusPublished - Jun 2019
Event2019 IEEE Congress on Evolutionary Computation - Wellington, New Zealand
Duration: 10 Jun 201913 Jun 2019


Conference2019 IEEE Congress on Evolutionary Computation
Country/TerritoryNew Zealand
Internet address


Dive into the research topics of 'A PSO-based algorithm for reserve price optimization in online ad auctions'. Together they form a unique fingerprint.

Cite this