A Favorable Weight-Based Evolutionary Algorithm for Multiple Criteria Problems

SOYLU B., Koksalan M.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, vol.14, no.2, pp.191-205, 2010 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 14 Issue: 2
  • Publication Date: 2010
  • Doi Number: 10.1109/tevc.2009.2027357
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.191-205
  • Keywords: Evolutionary algorithm, multiple criteria, Tchebycheff scalarization, MULTIOBJECTIVE OPTIMIZATION, SELECTION


In this paper, we present a favorable weight-based evolutionary algorithm for multiple criteria problems. The algorithm tries to both approximate the Pareto frontier and evenly distribute the solutions over the frontier. These two goals are common for many multiobjective evolutionary algorithms. To achieve these goals in our algorithm, each member selects its own weights for a weighted Tchebycheff distance function to define its fitness score. The fitness scores favor solutions that are closer to the Pareto frontier and that are located at underrepresented regions. We compare the performance of the algorithm with two leading evolutionary algorithms on various continuous test problems having different number of criteria.