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

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Advances in Swarm Intelligence (ICSI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7331))

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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.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Yangzhou University, Yangzhou, 225127, China

    Bolun Chen & Ling Chen

  2. State Key Lab of Novel Software Tech, Nanjing University, Nanjing, 210093, China

    Ling Chen

Authors
  1. Bolun Chen
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  2. Ling Chen
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Editor information

Editors and Affiliations

  1. Key Laboratory of Machine Perception (MOE), Peking University, Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Peking University, 100871, Beijing, China

    Ying Tan

  2. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  3. Shenzhen City Key Laboratory of Embedded System Design, College of Computer Science and Software Engineering, Shenzhen University, 518060, Shenzhen, China

    Zhen Ji

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© 2012 Springer-Verlag Berlin Heidelberg

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Chen, B., Chen, L. (2012). A Method for Avoiding the Feedback Searching Bias in Ant Colony Optimization. In: Tan, Y., Shi, Y., Ji, Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30976-2_25

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  • DOI: https://doi.org/10.1007/978-3-642-30976-2_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30975-5

  • Online ISBN: 978-3-642-30976-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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