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SWAF: Swarm Algorithm Framework for Numerical Optimization

  • Xiao-Feng Xie
  • Wen-Jun Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3102)

Abstract

A swarm algorithm framework (SWAF), realized by agent-based modeling, is presented to solve numerical optimization problems. Each agent is a bare bones cognitive architecture, which learns knowledge by appropriately deploying a set of simple rules in fast and frugal heuristics. Two essential categories of rules, the generate-and-test and the problem-formulation rules, are implemented, and both of the macro rules by simple combination and subsymbolic deploying of multiple rules among them are also studied. Experimental results on benchmark problems are presented, and performance comparison between SWAF and other existing algorithms indicates that it is efficiently.

Keywords

Particle Swarm Optimization Simulated Annealing Differential Evolution Taboo Search Numerical Optimization 
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 2004

Authors and Affiliations

  • Xiao-Feng Xie
    • 1
  • Wen-Jun Zhang
    • 1
  1. 1.Institute of MicroelectronicsTsinghua UniversityBeijingChina

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