A territory defining multiobjective evolutionary algorithms and preference incorporation


Karahan I., Köksalan M.

IEEE Transactions on Evolutionary Computation, vol.14, no.4, pp.636-664, 2010 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 14 Issue: 4
  • Publication Date: 2010
  • Doi Number: 10.1109/tevc.2009.2033586
  • Journal Name: IEEE Transactions on Evolutionary Computation
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.636-664
  • Keywords: Crowding prevention, evolutionary algorithms, guidance, multiobjective optimization, preference incorporation
  • Middle East Technical University Affiliated: Yes

Abstract

We have developed a steady-state elitist evolutionary algorithm to approximate the Pareto-optimal frontiers of multiobjective decision making problems. The algorithms define a territory around each individual to prevent crowding in any region. This maintains diversity while facilitating the fast execution of the algorithm. We conducted extensive experiments on a variety of test problems and demonstrated that our algorithm performs well against the leading multiobjective evolutionary algorithms. We also developed a mechanism to incorporate preference information in order to focus on the regions that are appealing to the decision maker. Our experiments show that the algorithm approximates the Pareto-optimal solutions in the desired region very well when we incorporate the preference information. © 2010 IEEE.