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Buggy Pinball: A Novel Single-point Meta-heuristic for Global Continuous Optimization

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Artificial Intelligence and Soft Computing (ICAISC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13589))

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Abstract

In this work, we propose a fundamentally novel single-point meta-heuristic designed for continuous optimization. Our algorithm continuously improves on a solution via a trajectory-based search inspired by the pinball arcade game in an anytime optimization manner. We evaluate our algorithm against widely employed meta-heuristics on several standard test-bed functions and various dimensions. Our algorithm exhibits high precision, and superior accuracy compared to the benchmark, especially when complex configuration spaces are considered.

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Notes

  1. 1.

    The sign of the elevation angle is irrelevant as the square of its sin value is considered.

  2. 2.

    We experimented with trajectories alternating from one direction to another; however we found this took a toll in exploration. Thus, the algorithm does not behave exactly like a pinball, however, the final trajectories do resemble a pinball movement.

  3. 3.

    Crossing of the objective function means that a common point of the objective function and the current trajectory segment has been detected.

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Authors and Affiliations

  1. Technical University of Crete, Chania, Greece

    Vasileios Lymperakis

  2. California State University, Fresno, CA, USA

    Athanasios Aris Panagopoulos

Authors
  1. Vasileios Lymperakis
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  2. Athanasios Aris Panagopoulos
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Correspondence to Vasileios Lymperakis .

Editor information

Editors and Affiliations

  1. Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Lymperakis, V., Panagopoulos, A.A. (2023). Buggy Pinball: A Novel Single-point Meta-heuristic for Global Continuous Optimization. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2022. Lecture Notes in Computer Science(), vol 13589. Springer, Cham. https://doi.org/10.1007/978-3-031-23480-4_22

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  • DOI: https://doi.org/10.1007/978-3-031-23480-4_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23479-8

  • Online ISBN: 978-3-031-23480-4

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