World Scientific
  • Search
Skip main navigation

Cookies Notification

We use cookies on this site to enhance your user experience. By continuing to browse the site, you consent to the use of our cookies. Learn More
×
Our website is made possible by displaying certain online content using javascript.
In order to view the full content, please disable your ad blocker or whitelist our website www.worldscientific.com.

System Upgrade on Tue, Oct 25th, 2022 at 2am (EDT)

Existing users will be able to log into the site and access content. However, E-commerce and registration of new users may not be available for up to 12 hours.
For online purchase, please visit us again. Contact us at [email protected] for any enquiries.

An Evolutionary Algorithm for Finding Efficient Solutions in Multi-Attribute Auctions

    There is a growing interest in electronic auctions. Many researchers consider a single-attribute, although auctions are multi-attribute in nature in practice. Addressing multiple attributes increases the difficulty of the problem substantially. We develop an evolutionary algorithm (EA) for multi-attribute multi-item reverse auctions. We try to generate the whole Pareto front using the EA. We also develop heuristic procedures to find several good initial solutions and insert those in the initial population of the EA. We test the EA on a number of randomly generated problems and report our findings.

    References

    • G. Hohneret al., Interfaces 33(1), 23 (2003). Crossref, ISIGoogle Scholar
    • T. Mettyet al., Interfaces 35(1), 7 (2005). Crossref, ISIGoogle Scholar
    • T. Sandholmet al., Interfaces 36(1), 55 (2006). Crossref, ISIGoogle Scholar
    • J. Catalánet al., Computers & Operations Research 36(10), 2752 (2009). CrossrefGoogle Scholar
    • T. Sandholm, Artificial Intelligence 135, 1 (2002). Crossref, ISIGoogle Scholar
    • T. Sandholm and S. Suri, Artificial Intelligence 145, 33 (2003). Crossref, ISIGoogle Scholar
    • T. Sandholmet al., Management Science 51(3), 374 (2005). Crossref, ISIGoogle Scholar
    • D.   Lehmann , R.   Mueller and T.   Sandholm , Combinatorial Auctions , eds. P.   Cramton , Y.   Shoham and R.   Steinberg ( MIT Press , Cambridge , 2006 ) . Google Scholar
    • M. H. Rothkopf and S. Park, Interfaces 31(6), 83 (2001). Crossref, ISIGoogle Scholar
    • M. J. Bellostaet al., A multi-criteria model for electronic auctions, Proc. ACM Symp. Applied Computing (2004) pp. 759–765. Google Scholar
    • M. Bichler and J. Kalagnanam, European Journal of Operational Research 160(2), 380 (2005). Crossref, ISIGoogle Scholar
    • J. E. Teichet al., European Journal of Operational Research 175, 90 (2006). Crossref, ISIGoogle Scholar
    • S. Talluri, R. Narasimhan and S. Viswanathan, International Journal of Production Research 45(11), 2615 (2007). Crossref, ISIGoogle Scholar
    • G. Karakaya and M. Köksalan, Decision Support Systems 51, 299 (2011). Crossref, ISIGoogle Scholar
    • C. M. Fonseca and P. J. Fleming, Evolutionary Computation 3(1), 1 (1995). Crossref, ISIGoogle Scholar
    • K. Debet al., IEEE Transactions on Evolutionary Computation 6(2), 182 (2002). Crossref, ISIGoogle Scholar
    • E. Zitzler, M. Laumanns and L. Thiele, SPEA2: Improving the strength pareto evolutionary algorithm, TIK-Report, 103, Swiss Federal Institute of Technology, Switzerland (2001) . Google Scholar
    • B. Soylu and M. Köksalan, IEEE Transactions on Evolutionary Computation 14(2), 191 (2010). Crossref, ISIGoogle Scholar
    • C. W. Richter and G. B. Sheblé, IEEE Transactions on Power Systems 13(1), 256 (1998). Crossref, ISIGoogle Scholar
    • A. Byde, Applying evolutionary game theory to auction mechanism design, IEEE Int. Conf. E-Commerce (CEC) (2003) pp. 347–354. Google Scholar
    • D. Cliff, Genetic optimization of adaptive trading agents for double-auction markets, Proc. IEEE/IAFE/INFORMS Conf. Computational Intelligence for Financial Engineering (CIFEr) (1998) pp. 252–258. Google Scholar
    • J. Gaytánet al., An interactive method for improving the applicability of electronic reverse auctions, Proc 14th IPSERA Conf. (2005) pp. 20–23. Google Scholar
    • J. M.   Perloff , Microeconomies with Calculus , 2nd edn. ( Pearson Addison Wesley , Boston , 2011 ) . Google Scholar
    • E. Zitzler and S. Künzli, Indicator-based Selection in Multiobjective Search, Parallel Problem Solving from Nature – PPSN VIII3242 (Springer-Verla, 2004) pp. 832–842. Google Scholar
    • İ. Karahan and M. Köksalan, IEEE Transactions on Evolutionary Computation 14(4), 636 (2010). Crossref, ISIGoogle Scholar
    • K.   Deb , Multi-Objective Optimization Using Evolutionary Algorithms ( Wiley , Chichester, UK , 2001 ) . Google Scholar
    • Y. Sheffi, Interfaces 34(3), 245 (2004). Crossref, ISIGoogle Scholar
    • C.   Caplice and Y.   Sheffi , Combinatorial Auctions , eds. P.   Cramton , Y.   Shoham and R.   Steinberg ( MIT Press , 2006 ) . Google Scholar
    • Y. Haimes, L. Lasdon and D. Wismer, IEEE Transactions on Systems, Man, and Cybernetics (1) 296 (1971). Google Scholar
    • Y. P. Aneja and K. P. Nair, Management Science 25(1), 73 (1979). Crossref, ISIGoogle Scholar
    • E. Zitzler and L. Thiele, Multiobjective optimization using evolutionary algorithms — A comparative case study, Parallel Problem Solving from Nature, PPSN V1498, Lecture Notes in Computer Science, eds. A. E. Albenet al. (1998) pp. 292–301. Google Scholar
    • D. A. Van Veldhuizen and G. B. Lamont, On measuring multiobjective evolutionary algorithm performance, Proc. 2000 Congress on Evolutionary Computation, IEEE1 (2000) pp. 204–211. Google Scholar