{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T22:09:14Z","timestamp":1759183754211,"version":"3.37.3"},"reference-count":38,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,2,4]],"date-time":"2021-02-04T00:00:00Z","timestamp":1612396800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71371181","71672193","BSZA2019003"],"award-info":[{"award-number":["71371181","71672193","BSZA2019003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011993","name":"Xi'an International Studies University","doi-asserted-by":"publisher","award":["71371181","71672193","BSZA2019003"],"award-info":[{"award-number":["71371181","71672193","BSZA2019003"]}],"id":[{"id":"10.13039\/501100011993","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Scientific Programming"],"published-print":{"date-parts":[[2021,2,4]]},"abstract":"<jats:p>The particle swarm optimization (PSO) is a wide used optimization algorithm, which yet suffers from trapping in local optimum and the premature convergence. Many studies have proposed the improvements to address the drawbacks above. Most of them have implemented a single strategy for one problem or a fixed neighborhood structure during the whole search process. To further improve the PSO performance, we introduced a simple but effective method, named adaptive particle swarm optimization with Gaussian perturbation and mutation (AGMPSO), consisting of three strategies. Gaussian perturbation and mutation are incorporated to promote the exploration and exploitation capability, while the adaptive strategy is introduced to ensure dynamic implement of the former two strategies, which guarantee the balance of the searching ability and accuracy. Comparison experiments of proposed AGMPSO and existing PSO variants in solving 29 benchmark functions of CEC 2017 test suites suggest that, despite the simplicity in architecture, the proposed AGMPSO obtains a high convergence accuracy and significant robustness which are proven by conducted Wilcoxon\u2019s rank sum test.<\/jats:p>","DOI":"10.1155\/2021\/6676449","type":"journal-article","created":{"date-parts":[[2021,2,5]],"date-time":"2021-02-05T04:18:45Z","timestamp":1612498725000},"page":"1-14","source":"Crossref","is-referenced-by-count":5,"title":["Adaptive Particle Swarm Optimization with Gaussian Perturbation and Mutation"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3540-1053","authenticated-orcid":true,"given":"Binbin","family":"Chen","sequence":"first","affiliation":[{"name":"Graduate School, Xi\u2019an International Studies University, Xi\u2019an 710128, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6001-3167","authenticated-orcid":true,"given":"Rui","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Zunyi Normal College, Zunyi 563002, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8136-345X","authenticated-orcid":true,"given":"Long","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Zunyi Normal College, Zunyi 563002, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1869-320X","authenticated-orcid":true,"given":"Shengjie","family":"Long","sequence":"additional","affiliation":[{"name":"School of Traffic & Transportation Engineering, Central South University, Changsha 410004, China"}]}],"member":"311","reference":[{"first-page":"1942","article-title":"Particle swarm optimization","author":"J. 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