ArticleBiomimetics (Basel, Switzerland)2023
Binary Restructuring Particle Swarm Optimization and Its Application.
Article in Biomimetics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- An improved multi-objective particle swarm optimization algorithm for the design of foundation pit of rail transit upper cover project.Scientific reports · 2025Article
- An Enhanced Misinformation Detection Model Based on an Improved Beluga Whale Optimization Algorithm and Cross-Modal Feature Fusion.Biomimetics (Basel, Switzerland) · 2025Article
- Adaptive Bi-Operator Evolution for Multitasking Optimization Problems.Biomimetics (Basel, Switzerland) · 2024Article
- Automated collective variable discovery for MFSD2A transporter from molecular dynamics simulations.Biophysical journal · 2024Article
- An Advanced Bio-Inspired Mantis Search Algorithm for Characterization of PV Panel and Global Optimization of Its Model Parameters.Biomimetics (Basel, Switzerland) · 2023Article
- A Subtraction-Average-Based Optimizer for Solving Engineering Problems with Applications on TCSC Allocation in Power Systems.Biomimetics (Basel, Switzerland) · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
Restructuring Particle Swarm Optimization (RPSO) algorithm has been developed as an intelligent approach based on the linear system theory of particle swarm optimization (PSO). It streamlines the flow of the PSO algorithm, specifically targeting continuous optimization problems. In order to adapt RPSO for solving discrete optimization problems, this paper proposes the binary Restructuring Particle Swarm Optimization (BRPSO) algorithm. Unlike other binary metaheuristic algorithms, BRPSO does not utilize the transfer function. The particle updating process in BRPSO relies solely on comparison results between values derived from the position updating formula and a random number. Additionally, a novel perturbation term is incorporated into the position updating formula of BRPSO. Notably, BRPSO requires fewer parameters and exhibits high exploration capability during the early stages. To evaluate the efficacy of BRPSO, comprehensive experiments are conducted by comparing it against four peer algorithms in the context of feature selection problems. The experimental results highlight the competitive nature of BRPSO in terms of both classification accuracy and the number of selected features.
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