Real-world dynamic optimization using an adaptive-mutation compact genetic algorithm

Uzor, Chigozirim J.; Góngora, Mario; Coupland, Simon; Passow, Benjamin N.

doi:10.1109/cidue.2014.7007862

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…Instead, the compact adaptive mutation genetic algorithm (amcGA) presented in [342] is based on an adaptive mechanism where the mutation scheme is directly linked to a change detection scheme so that the change detection scheme regulates the mutation rate (i.e., the degree of change determines the probability of mutation). Tis method was tested in [343] using a real-world dynamic optimisation problem that includes designing and optimising a PID controller for a torsional massspring-damper system in a dynamic environment.…”

Section: Future Research Directionsmentioning

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: Future Research Directionsmentioning

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 (Uzor et al, 2014b), the amcGA was evaluated using a real-world dynamic optimization control problem with some preliminary results. In this paper, variants of the amcGA are presented and further investigated to improve the algorithms adaptability in a dynamic environment.…”

Section: Introductionmentioning

confidence: 99%

Adaptive-mutation compact genetic algorithm for dynamic environments

et al. 2016

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In recent years, the interest in studying nature inspired optimization algorithms for dynamic optimization problems (DOPs) has been increasing constantly due to its importance in real-world applications. Several techniques such as hyper-selection, change prediction, hypermutation and many more have been developed to address DOPs. Among these techniques, the hypermutation scheme has proved beneficial for addressing DOPs but requires that the mutation factors be picked a priori and this is one of the limitation of the hypermutation scheme.This paper investigates variants of the recently proposed adaptive-mutation compact genetic algorithm (amcGA). The amcGA is made up of a change detection scheme and mutation schemes, where the degree of change regulates the probability of mutation (i.e. the probability of mutation is directly proportional to the degree of change). This paper also presents a change trend scheme for the amcGA so as to boost its performance whenever a change occurs. Experimental results shows that the change trend and mutation schemes has an

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