{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:06:37Z","timestamp":1777705597433,"version":"3.51.4"},"reference-count":46,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2022,2,2]]},"abstract":"<jats:p>Autonomous groups of particles swarm optimization (AGPSO), inspired by individual diversity in biological swarms such as insects or birds, is a modified particle swarm optimization (PSO) variant. The AGPSO method is simple to understand and easy to implement on a computer. It has achieved an impressive performance on high-dimensional optimization tasks. However, AGPSO also struggles with premature convergence, low solution accuracy and easily falls into local optimum solutions. To overcome these drawbacks, random-walk autonomous group particle swarm optimization (RW-AGPSO) is proposed. In the RW-AGPSO algorithm, Levy flights and dynamically changing weight strategies are introduced to balance exploration and exploitation. The search accuracy and optimization performance of the RW-AGPSO algorithm are verified on 23 well-known benchmark test functions. The experimental results reveal that, for almost all low- and high-dimensional unimodal and multimodal functions, the RW-AGPSO technique has superior optimization performance when compared with three AGPSO variants, four PSO approaches and other recently proposed algorithms. In addition, the performance of the RW-AGPSO has also been tested on the CEC\u201914 test suite and three real-world engineering problems. The results show that the RW-AGPSO is effective for solving high complexity problems.<\/jats:p>","DOI":"10.3233\/jifs-210867","type":"journal-article","created":{"date-parts":[[2021,12,21]],"date-time":"2021-12-21T12:38:17Z","timestamp":1640090297000},"page":"1519-1545","source":"Crossref","is-referenced-by-count":7,"title":["Random walk autonomous groups of particles for particle swarm optimization"],"prefix":"10.1177","volume":"42","author":[{"given":"Xinliang","family":"Xu","sequence":"first","affiliation":[{"name":"College of Economics and Management, Northeast Agricultural University, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fu","family":"Yan","sequence":"additional","affiliation":[{"name":"Guizhou Provincial Key Laboratory of Public Big Data, Guizhou University, Guiyang, China"},{"name":"Guizhou Province Big Data Industry Development and Application Research Institute, Guiyang, 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