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A New Multi-objective Particle Swarm Optimization Algorithm Using Clustering Applied to Automated Docking

  • Stefan Janson
  • Daniel Merkle
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3636)

Abstract

In this paper we introduce the new hybrid Particle Swarm Optimization algorithm for multi-objective optimization ClustMPSO. We combined the PSO algorithm with clustering techniques to divide all particles into several subswarms. Strategies for updating the personal best position of a particle, for selection of the neighbourhood best and for swarm dominance are proposed. The algorithm is analyzed on both artificial optimization functions and on an important real world problem from biochemistry. The molecule docking problem is to predict the three dimensional structure and the affinity of a binding of a target receptor and a ligand. ClustMPSO clearly outperforms a well-known Lamarckian Genetic Algorithm for the problem.

Keywords

Multiobjective Optimization Particle Swarm Optimization Algorithm Objective Space Docking Energy Lamarckian Genetic Algorithm 
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 2005

Authors and Affiliations

  • Stefan Janson
    • 1
  • Daniel Merkle
    • 1
  1. 1.Department of Computer ScienceUniversity of LeipzigLeipzigGermany

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