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ABC Metaheuristic Based Optimized Adaptation Planning Logic for Decision Making Intelligent Agents in Self Adaptive Software System

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Data Mining and Big Data (DMBD 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10387))

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Abstract

The potential of machine intelligence is enormously increasing with a vision of computing systems that can act as good decision making and self managing entities. This led to the introduction of systems that are more intelligent with self* properties and are known as Self Adaptive Software Systems (SAS). Intelligent Agents which has a high adaptation capability forms the main component of such systems. These self adaptive systems are provided with the ability of self–configuring based on the run time environmental changes which guarantee the overall system functional and QoS goals. This paper proposes an optimized decentralized adaptation logic for modeling SAS which exploits the multi-agent concept. Each subsystem has an objective and uses an Artificial Bee Colony metaheuristic to achieve local optimization which in turn leads to the optimization of the whole distributed system.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, SCT College of Engineering, Thiruvananthapuram, India

    Binu Rajan

  2. Computer Centre, University of Kerala, Thiruvananthapuram, India

    Vinod Chandra

Authors
  1. Binu Rajan
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  2. Vinod Chandra
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Corresponding author

Correspondence to Binu Rajan .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Kyushu University, Fukuoka, Japan

    Hideyuki Takagi

  3. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

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Rajan, B., Chandra, V. (2017). ABC Metaheuristic Based Optimized Adaptation Planning Logic for Decision Making Intelligent Agents in Self Adaptive Software System. In: Tan, Y., Takagi, H., Shi, Y. (eds) Data Mining and Big Data. DMBD 2017. Lecture Notes in Computer Science(), vol 10387. Springer, Cham. https://doi.org/10.1007/978-3-319-61845-6_49

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  • DOI: https://doi.org/10.1007/978-3-319-61845-6_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61844-9

  • Online ISBN: 978-3-319-61845-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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