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Department Informatik  >  Informatik 12  >  Personal  >  Rolf Wanka  >  Veröffentlichungen  >  RHW10

Design and Experimental Evaluation of Multiple Adaptation Layers
in Self-optimizing Particle Swarm Optimization

Thomas Ritscher, Sabine Helwig and Rolf Wanka

Department of Computer Science
University of Erlangen-Nuremberg, Germany, {sabine.helwig,rwanka}

Abstract. Particle swarm optimization (PSO) is a nature-inspired technique for solving continuous optimization problems. For a fixed optimization problem, the quality of the found solution depends significantly on the choice of the algorithmic PSO parameters such as the inertia weight and the acceleration coefficients. It is a challenging task to choose appropriate values for these parameters by hand or mathematically. In this paper, a novel self-optimizing particle swarm optimizer with multiple adaptation layers is introduced. In the new algorithm, adaptation takes place on both particle and subswarm level . The new idea of using virtual parameter swarms which hold modifiable parameter configurations each is introduced. The algorithmic PSO parameters can be mutated by using, for instance, well-known techniques from the field of evolutionary algori thms, in order to allow fine-granular parameter adaptation to the problem at hand. The new algorithm is experimentally evaluated, and compared to a standard PSO and the TRIBES algorithm. The experimental study shows that our new algorithm is highly competitive to previously suggested approaches.

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Copyright Notice: © 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Published in: Proc. IEEE Congress on Evolutionary Computation (CEC); 2010. [doi:10.1109/C EC.2010.5586255]

BibTex entry

  Impressum Stand: 30 October 2010.   R.W.