<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>In this paper, a new mechanism for detecting population stagnation based on the analysis of the local improvement of the evaluation function and the infinite impulse response filter is proposed. The purpose of this mechanism is to improve the population stagnation detection capability for various optimization scenarios, and thus to improve multi-population-based algorithms (MPBAs) performance. In addition, various other approaches have been proposed to eliminate stagnation, including approaches aimed at both improving performance and reducing the complexity of the algorithms. The developed methods were tested, among the others, for various migration topologies and various MPBAs, including the MNIA algorithm, which allows the use of many different base algorithms and thus eliminates the need to select the population-based algorithm for a given simulation problem. The simulations were performed for typical benchmark functions and control problems. The obtained results confirm the validity of the developed method.</p>
</abstract>

In this paper, a new mechanism for detecting population stagnation based on the analysis of the local improvement of the evaluation function and the infinite impulse response filter is proposed. The purpose of this mechanism is to improve the population stagnation detection capability for various optimization scenarios, and thus to improve multi-population-based algorithms (MPBAs) performance. In addition, various other approaches have been proposed to eliminate stagnation, including approaches aimed at both improving performance and reducing the complexity of the algorithms. The developed methods were tested, among the others, for various migration topologies and various MPBAs, including the MNIA algorithm, which allows the use of many different base algorithms and thus eliminates the need to select the population-based algorithm for a given simulation problem. The simulations were performed for typical benchmark functions and control problems. The obtained results confirm the validity of the developed method.

A New Approach to Detecting and Preventing Populations Stagnation Through Dynamic Changes in Multi-Population-Based Algorithms

Christian Napoli Sapienza University of Rome

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

In this paper, a new mechanism for detecting population stagnation based on the analysis of the local improvement of the evaluation function and the infinite...

A New Approach to Detecting and Preventing Populations Stagnation Through Dynamic Changes in Multi-Population-Based Algorithms

Online veröffentlicht: 30. Okt. 2023

Seitenbereich: 289 - 306

Eingereicht: 06. Mai 2023

Akzeptiert: 11. Okt. 2023

DOI: https://doi.org/10.2478/jaiscr-2023-0020

Schlüsselwörter
multi-population-based algorithms, migration topologies, population stagnation

© 2023 Krystian Łapa et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A New Approach to Detecting and Preventing Populations Stagnation Through Dynamic Changes in Multi-Population-Based Algorithms

Online veröffentlicht: 30. Okt. 2023

Seitenbereich: 289 - 306

Eingereicht: 06. Mai 2023

Akzeptiert: 11. Okt. 2023

DOI: https://doi.org/10.2478/jaiscr-2023-0020

Schlüsselwörtermulti-population-based algorithms, migration topologies, population stagnation

© 2023 Krystian Łapa et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
multi-population-based algorithms, migration topologies, population stagnation