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A Method for Avoiding the Feedback Searching Bias in Ant Colony Optimization

  • Bolun Chen
  • Ling Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7331)

Abstract

One of the obstacles in applying ant colony optimization (ACO) to the combinatorial optimization is that the search process is sometimes biased by algorithm features such as the pheromone model and the solution construction process. Due to such searching bias, ant colony optimization cannot converge to the optimal solution for some problems. In this paper, we define a new type of searching bias in ACO named feedback bias taking the k-cardinality tree problem as the test instance. We also present a method for avoiding the feedback searching bias. Convergence analysis of our method is also given. Experimental results confirm the correctness of our analysis and show that our method can effectively avoid the searching bias and can ensure the convergence for the problem.

Keywords

ant colony optimization deceptive problems K-cardinality tree problem solution convergence 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Bolun Chen
    • 1
  • Ling Chen
    • 1
    • 2
  1. 1.Department of Computer ScienceYangzhou UniversityYangzhouChina
  2. 2.State Key Lab of Novel Software TechNanjing UniversityNanjingChina

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