The Gradient Free Directed Search Method as Local Search within Multi-Objective Evolutionary Algorithms

  • Adriana Lara
  • Sergio Alvarado
  • Shaul Salomon
  • Gideon Avigad
  • Carlos A. Coello Coello
  • Oliver Schütze
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 175)


Recently, the Directed Search Method has been proposed as a point-wise iterative search procedure that allows to steer the search, in any direction given in objective space, of a multi-objective optimization problem. While the original version requires the objectives’ gradients, we consider here a possible modification that allows to realize the method without gradient information. This makes the novel algorithm in particular interesting for hybridization with set oriented search procedures, such as multi-objective evolutionary algorithms.

In this paper, we propose the DDS, a gradient free Directed Search method, and make a first attempt to demonstrate its benefit, as a local search procedure within a memetic strategy, by integrating the DDS into the well-known algorithmMOEA/D. Numerical results on some benchmark models indicate the advantage of the resulting hybrid.


Local Search Pareto Front Evolutionary Computation Multiobjective Optimization Objective Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Adriana Lara
    • 1
  • Sergio Alvarado
    • 2
  • Shaul Salomon
    • 3
  • Gideon Avigad
    • 3
  • Carlos A. Coello Coello
    • 2
  • Oliver Schütze
    • 2
  1. 1.Mathematics DepartmentESFM-IPN, Edif. 9 UPALMMexico CityMexico
  2. 2.Computer Science DepartmentCINVESTAV-IPNMexico CityMexico
  3. 3.School of EngineeringORT BraudeKarmielIsrael

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