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In order to address high-dimensional feature selection problems with greater effectiveness and efficiency, this paper proposes a particle swarm optimizer variant named PSO-BEE that allows the swarm to take full advantage of exploration and exploitation at due evolutionary stages. At the early stage, a large size of candidate exemplar group is formed for diversity enhancement, attempting to find as many new combinations of features as possible. At the later stage, a tiny size of candidate exemplar group is constructed, allowing updated particles to learn from the few best elite exemplars to continuously refine feature selection solutions with a lower classification error rate. Three PSO-BEE variants with distinct preferences for exploration and exploitation are proposed based on different parameter adjustment strategies. Experiments are executed on 500, 1000, and 2000-dimensional benchmark suites presented by CEC and real-world feature selection datasets from the Machine Learning Repository. Experimental results demonstrate the competitive performance of PSO-BEE in high-dimensional global optimization and feature selection when compared with several state-of-the-art approaches.<\/jats:p>","DOI":"10.1142\/s2301385025500840","type":"journal-article","created":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T23:03:32Z","timestamp":1729638212000},"page":"95-117","source":"Crossref","is-referenced-by-count":1,"title":["A Particle Swarm Optimizer with Biased Exploration and Exploitation for High-Dimensional Optimization and Feature Selection"],"prefix":"10.1142","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5678-4651","authenticated-orcid":false,"given":"Minchong","family":"Chen","sequence":"first","affiliation":[{"name":"College of Electronic and Information Engineering, Tongji University, 4800 Cao\u2019an Road, Shanghai, 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