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A Multi-population-Based Algorithm with Different Ways of Subpopulations Cooperation

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Artificial Intelligence and Soft Computing (ICAISC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13588))

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Abstract

Metaheuristic methods are designed to solve continuous and discrete problems. Such methods include population based algorithms (PBAs). They are distinguished by the flexibility of defining the fitness function, therefore they are a good alternative to gradient methods. However, creating new variants of PBAs that work similarly and differ in detail might be problematic. Therefore, it is interesting to combine existing PBAs in order to increase their effectiveness. One of the hybrid methods is the Multi-population Nature-Inspired Algorithm (MNIA), which uses search operators from different PBAs. The formula of MNIA’s operation is based on the appropriate cooperation of its subpopulations. That is why in this paper we focus on expanding MNIAs with various schemes of such cooperation. In particular, we analyze various combinations of migration models, intervals, and topologies. The proposed solutions were tested and compared using generally known benchmark functions. The obtained results showed an advantage of certain patterns of cooperation of the - subpopulations, which confirmed the validity of the adopted assumptions.

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Acknowledgment

This paper was financed under the program of the Minister of Science and Higher Education under the name ‘Regional Initiative of Excellence’ in the years 2019-2022, project number 020/RID/2018/19 with the amount of financing PLN 12 000 000.

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  1. Department of Computational Intelligence, Częstochowa University of Technology, Częstochowa, Poland

    Krzysztof Cpałka, Krystian Łapa & Leszek Rutkowski

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  1. Krzysztof Cpałka
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  2. Krystian Łapa
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Correspondence to Krystian Łapa .

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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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Cpałka, K., Łapa, K., Rutkowski, L. (2023). A Multi-population-Based Algorithm with Different Ways of Subpopulations Cooperation. 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 13588. Springer, Cham. https://doi.org/10.1007/978-3-031-23492-7_18

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