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Exponential Inertia Weight in Particle Swarm Optimization

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Information Systems Architecture and Technology: Proceedings of 37th International Conference on Information Systems Architecture and Technology – ISAT 2016 – Part IV

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 524))

Abstract

This paper presents an improved particle swarm optimization algorithm (EWPSO) with a novel strategy for inertia weight. In the new algorithm, nonlinear inertia weight is proposed. The new weight is an exponential function of the minimal and maximal fitness of the particles in each iteration. The set of benchmark function was used to test the new method. The results were compared with those obtained through the standard PSO with linear decreasing inertia weight (LDW-PSO) and RNW-PSO. Simulation results showed that EWPSO is more effective for the tested problems than both LDW-PSO and RNW-PSO.

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Authors and Affiliations

  1. Institute of Information Technology, Lodz University of Technology, Wólczańska 215, 90-924, Łodź, Poland

    Bożena Borowska

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  1. Bożena Borowska
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Correspondence to Bożena Borowska .

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Editors and Affiliations

  1. Department of Management Systems,Faculty of Computer Science and Management, Wrocław University of Technology, Wrocła Department of Management Systems, Wrocław, Poland

    Zofia Wilimowska

  2. Department of IT and Management, Wrocław University of Technology Department of IT and Management, Wrocław, Poland

    Leszek Borzemski

  3. Department of Computer Science, Wrocław University of Technology Department of Computer Science, Wrocław, Poland

    Adam Grzech

  4. Department of Computer Science, Wrocław University of Technology Department of Computer Science, Wrocław, Poland

    Jerzy Świątek

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Borowska, B. (2017). Exponential Inertia Weight in Particle Swarm Optimization. In: Wilimowska, Z., Borzemski, L., Grzech, A., Świątek, J. (eds) Information Systems Architecture and Technology: Proceedings of 37th International Conference on Information Systems Architecture and Technology – ISAT 2016 – Part IV. Advances in Intelligent Systems and Computing, vol 524. Springer, Cham. https://doi.org/10.1007/978-3-319-46592-0_23

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  • DOI: https://doi.org/10.1007/978-3-319-46592-0_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46591-3

  • Online ISBN: 978-3-319-46592-0

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