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Learning-Based Multi-agent System for Solving Combinatorial Optimization Problems: A New Architecture

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Hybrid Artificial Intelligent Systems (HAIS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9121))

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Abstract

Solving combinatorial optimization problems is an important challenge in all engineering applications. Researchers have been extensively solving these problems using evolutionary computations. This paper introduces a novel learning-based multi-agent system (LBMAS) in which all agents cooperate by acting on a common population and a two-stage archive containing promising fitness-based and positional-based solutions found so far. Metaheuristics as agents perform their own method individually and then share their outcomes. This way, even though individual performance may be low, collaboration of metaheuristics leads the system to reach high performance. In this system, solutions are modified by all running metaheuristics and the system learns gradually how promising metaheuristics are, in order to apply them based on their effectiveness. Finally, the performance of LBMAS is experimentally evaluated on Multiprocessor Scheduling Problem (MSP) which is an outstanding combinatorial optimization problem. Obtained results in comparison to well-known competitors show that our multi-agent system achieves better results in reasonable running times.

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Authors and Affiliations

  1. Computer Engineering Department, Eastern Mediterranean University, Famagusta, North Cyprus, Cyprus

    Nasser Lotfi & Adnan Acan

Authors
  1. Nasser Lotfi
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  2. Adnan Acan
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Correspondence to Nasser Lotfi .

Editor information

Editors and Affiliations

  1. University of Deusto, Bilbao, Spain

    Enrique Onieva

  2. University of Deusto, Bilbao, Spain

    Igor Santos

  3. University of Deusto, Bilbao, Spain

    Eneko Osaba

  4. Universidad de Salamanca, Salamanca, Spain

    Héctor Quintián

  5. Universidad de Salamanca, Salamanca, Spain

    Emilio Corchado

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Lotfi, N., Acan, A. (2015). Learning-Based Multi-agent System for Solving Combinatorial Optimization Problems: A New Architecture. In: Onieva, E., Santos, I., Osaba, E., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2015. Lecture Notes in Computer Science(), vol 9121. Springer, Cham. https://doi.org/10.1007/978-3-319-19644-2_27

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  • DOI: https://doi.org/10.1007/978-3-319-19644-2_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19643-5

  • Online ISBN: 978-3-319-19644-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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