Chunking and cooperation in particle swarm optimization for feature selection

Abstract

Bio-inspired optimization aims at adapting observed natural behavioral patterns and social phenomena towards efficiently solving complex optimization problems, and is nowadays gaining much attention. However, researchers recently highlighted an inconsistency between the need in the field and the actual trend. Indeed, while nowadays it is important to design innovative contributions, an actual trend in bio-inspired optimization is to re-iterate the existing knowledge in a different form. The aim of this paper is to fill this gap. More precisely, we start first by highlighting new examples for this problem by considering and describing the concepts of chunking and cooperative learning. Second, by considering particle swarm optimization (PSO), we present a novel bridge between these two notions adapted to the problem of feature selection. In the experiments, we investigate the practical importance of our approach while exploring both its strength and limitations. The results indicate that the approach is mainly suitable for large datasets, and that further research is needed to improve the computational efficiency of the approach and to ensure the independence of the sub-problems defined using chunking.

Bio-Inspired Feature Selection Algorithms With Their Applications: A Systematic Literature Review

“…However, conventional PSO algorithms may have certain drawbacks, such as a lack of exploration, which leads to the possibility of falling into local optima [40]. Several enhancement approaches for PSO observed in the reviewed literature include adapting Lóvy flights function to improve local search [40]; hybridization with other BIAs to increase population diversity [40], [53]; cooperative learning within swarm intelligence [63]. Lóvy flight is a random walk method that follows a heavy-tailed distribution.…”

“…Martarelli and Nagano [53] hybridized WHA with an improved version of PSO called Adaptive PSO [64], which uses additional adaptive parameters to improve the algorithm's convergence speed and achieve a balance between exploitation and exploration of the search space. Sarhani and Voß [63] followed a unique approach, which is worth noting as the authors claimed that an actual trend in bio-inspired optimization is to re-iterate the existing knowledge in a different form, so they aim to fill this gap. Instead of having one swarm (of n particles) trying to find the optimal d-dimensional vector, the vector is split into clusters of features that we can consider independent of the others.…”

Enhanced Anomaly Detection System for IoT Based on Improved Dynamic SBPSO

“…Therefore, there are two main goals in feature selection: to improve classification performance and reduce the number of selected features. In [34], the aggregate fitness function is used to select best features with no change in accuracy, which can be shown in (12).…”

Initialization of metaheuristics: comprehensive review, critical analysis, and research directions

“…A classical multipopulation strategy is problem-oriented in the sense that each subpopulation independently optimizes a part of the problem (e.g., Sarhani and Voß, 2022). Other advanced multipopulation optimization methods are used to improve the search diversity by splitting the entire population into groups, in which each one has a specific role.…”