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Novel Binary Biogeography-Based Optimization Algorithm for the Knapsack Problem

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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

Mathematical models of biogeography inspired the development of the biogeography-based optimization algorithm. In this article we propose a binary version of biogeography-based optimization (BBO) for the Knapsack Problem. Two new mutation operators are proposed to extend the biogeography-based optimization algorithm to binary optimization problems. We also demonstrate the performance of the resulting new binary Biogeography-based optimization algorithm in solving four Knapsack problems and compare it with that of the standard Genetic Algorithm. The simulation results show that our new method is effective and efficient for the Knapsack problem.

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Author information

Authors and Affiliations

  1. School of Business, Jiujiang University, Jiujiang, 332005, China

    BiBingyan Zhao

  2. School of Information Science and Technology, Jiujiang University, Jiujiang, 332005, China

    Changshou Deng, Yanling Yang & Hu Peng

Authors
  1. BiBingyan Zhao
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  2. Changshou Deng
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  3. Yanling Yang
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  4. Hu Peng
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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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Zhao, B., Deng, C., Yang, Y., Peng, H. (2012). Novel Binary Biogeography-Based Optimization Algorithm for the Knapsack Problem. 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_26

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  • DOI: https://doi.org/10.1007/978-3-642-30976-2_26

  • 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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