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RHW10
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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
thomas.ritscher@googlemail.com, {sabine.helwig,rwanka}@informatik.uni-erlangen.de

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


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  Impressum Stand: 30 October 2010.   R.W.