{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-novel-artificial-fish-swarm-algorithm-for","title":"A Novel Artificial Fish Swarm Algorithm for Pattern Recognition with Convex Optimization","arxiv_id":"1612.00125","date":"2016-12-01","proceeding":null,"authors":["Lei Shi","Rui Guo","Yuchen Ma"],"abstract":"Image pattern recognition is an important area in digital image processing.\nAn efficient pattern recognition algorithm should be able to provide correct\nrecognition at a reduced computational time. Off late amongst the machine\nlearning pattern recognition algorithms, Artificial fish swarm algorithm is one\nof the swarm intelligence optimization algorithms that works based on\npopulation and stochastic search. In order to achieve acceptable result, there\nare many parameters needs to be adjusted in AFSA. Among these parameters,\nvisual and step are very significant in view of the fact that artificial fish\nbasically move based on these parameters. In standard AFSA, these two\nparameters remain constant until the algorithm termination. Large values of\nthese parameters increase the capability of algorithm in global search, while\nsmall values improve the local search ability of the algorithm. In this paper,\nwe empirically study the performance of the AFSA and different approaches to\nbalance between local and global exploration have been tested based on the\nadaptive modification of visual and step during algorithm execution. The\nproposed approaches have been evaluated based on the four well-known benchmark\nfunctions. Experimental results show considerable positive impact on the\nperformance of AFSA. A Convex optimization has been integrated into the\nproposed work to have an ideal segmentation of the input image which is a MR\nbrain image.","url_abs":"http://arxiv.org/abs/1612.00125v2","url_pdf":"http://arxiv.org/pdf/1612.00125v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-novel-artificial-fish-swarm-algorithm-for","repo_url":"https://github.com/nafiuny/AFSA-algorithm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}