Multi-population-based Algorithms with Different Migration Topologies and Their Improvement by Population Re-initialization

Łapa, Krystian

doi:10.1007/978-3-031-42505-9_34

Cited by 1 publication

(2 citation statements)

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“…It is also worth mentioning the adaptive mechanisms of re-initializing the population (or part of it), which allow one to search subsequent areas of search space and thus to improve the results (see e.g. [29]). Adaptive mechanisms can be focused also on the complexity of the optimization -e.g.…”

Section: Adaptive Mechanismsmentioning

confidence: 99%

“…In the [28], individual island training plans (both population algorithms and their parameters) are changed based on the optimization progress, which brought a significant improvement. In the paper [29], when stagnation was detected, various mechanisms were used to re-initialize those populations that performed the worst. In this case, the best solution turned out to be re-initialization involving the creation of new individuals based on the mechanisms of differential evolution and the use for re-initialization the individuals from other populations.…”

Section: Adaptive Mechanismsmentioning

confidence: 99%

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

Łapa,

Rutkowska,

Byrski

et al. 2023

Journal of Artificial Intelligence and Soft Computing Research

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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.

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Section: Adaptive Mechanismsmentioning

confidence: 99%

Section: Adaptive Mechanismsmentioning

confidence: 99%