The cloud-based framework for ant colony optimization

ABSTRACT

How to keep the balance between exploration in search space regions and exploitation of the search experience gathered so far is one of the most important issues in Ant Colony Optimization (ACO). By using a variety of effective exploitation mechanisms and elite strategies, researchers proposed many sophisticated ACO algorithms, and obtains better results in experiments. In this paper, a new framework for implementing ACO algorithms called the cloud-based framework for ACO is proposed, which uses cloud model as the fuzzy membership function and constructs a self-adaptive mechanism with cloud model. By using the self-adaptive mechanism and the pheromone updating rule of suboptimal solutions which is determined by the membership function uncertainly, the cloud-based framework can make ACO algorithm explorer search space more effectively. Theoretical analysis on the cloud-based framework for ACO indicate that the framework is convergent, and the simulation results show that the framework can improve the ACO algorithms evidently.

REFERENCES

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.8 Problem Solving, Control Methods, and Search

General Terms:
Algorithms, Theory

Keywords:
ant colony optimization, ant colony system, cloud-model