Multi-Objective Genetic Algorithm Implemented on a STM32F Microcontroller

Santos, Pedro Henriquede Oliveira; Soares, Gustavo Luís; Machado-Coelho, Thiago Melo; Oliveira, Bernardo AugustoGodinho de; Ekel, Petr; Ferreira, Flavia Magalhaes Freitas; Martins, Carlos AugustoPaiva da Silva

doi:10.1109/cec.2018.8477938

Cited by 2 publications

(1 citation statement)

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“…For the sake of completeness, it is worth adding that these concepts can be extended to other optimisation scenarios, such as, e.g., multi-objective problems. In this regard, we point to [20] where a Multi-Objective Genetic Algorithm (MOGA) is implemented and executed on a low-end microcontroller.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristics in the Balance: A Survey on Memory‐Saving Approaches for Platforms with Seriously Limited Resources

Khalfi,

Caraffini,

Iacca

2023

International Journal of Intelligent Systems

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In the last three decades, the field of computational intelligence has seen a profusion of population-based metaheuristics applied to a variety of problems, where they achieved state-of-the-art results. This remarkable growth has been fuelled and, to some extent, exacerbated by various sources of inspiration and working philosophies, which have been thoroughly reviewed in several recent survey papers. However, the present survey addresses an important gap in the literature. Here, we reflect on a systematic categorisation of what we call “lightweight” metaheuristics, i.e., optimisation algorithms characterised by purposely limited memory and computational requirements. We focus mainly on two classes of lightweight algorithms: single-solution metaheuristics and “compact” optimisation algorithms. Our analysis is mostly focused on single-objective continuous optimisation. We provide an updated and unified view of the most important achievements in the field of lightweight metaheuristics, background concepts, and most important applications. We then discuss the implications of these algorithms and the main open questions and suggest future research directions.

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Section: Introductionmentioning

confidence: 99%