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

Abstract

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.

Comparing Metaheuristic Search Techniques in Addressing the Effectiveness of Clustering-Based DDoS Attack Detection Methods

“…Applying vigorous feature selection processes in intrusion detection methods is essential. Parameter-tuning of metaheuristic search techniques is one critical element in improving the field [18]. In this study, we applied three metaheuristic techniques of CSA, FPA, and FSA for both the wrapper method and the hybrid approach.…”

“…Current methodologies use a switching approximation factor that, according to Palaniswamy and Kandhasamy [12], identifies the upper and lower approximations that range from 0 to 1, and the upper approximation could be between 0.7 and 0.9 and lower approximations could be between 0.1 and 0.3. Adjusting the parameters of the metaheuristic search algorithms has a major impact on their effectiveness [18]. Analyzing the values of some parameters for the enhancement of these algorithms in the optimization process of network traffic features is critical.…”