{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T09:53:43Z","timestamp":1762509223152,"version":"3.37.0"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T00:00:00Z","timestamp":1736899200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T00:00:00Z","timestamp":1736899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>This paper proposes two novel group-based frameworks that can be implemented into almost any nature-inspired optimization algorithm. The proposed Group-Based (GB) and Cross Group-Based (XGB) framework implements a strategy which modifies the attraction and movement behaviors of base nature-inspired optimization algorithms and a mechanism that creates a continuing variance within population groupings, while attempting to maintain levels of computational simplicity that have helped nature-inspired optimization algorithms gain notoriety within the field of feature selection. Through this functionality, the proposed framework seeks to increase search diversity within the population swarm to address issues such as premature convergence, and oscillations within the swarm. The proposed frameworks have shown promising results when implemented into the Bat algorithm (BA), Firefly algorithm (FA), and Particle Swarm Optimization algorithm (PSO), all of which are popular when applied to the field of feature selection, and have been shown to perform well in a variety of domains, gaining notoriety due to their powerful search capabilities.<\/jats:p>","DOI":"10.1007\/s40747-024-01763-y","type":"journal-article","created":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T08:33:41Z","timestamp":1736930021000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A novel group-based framework for nature-inspired optimization algorithms with adaptive movement behavior"],"prefix":"10.1007","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2752-3381","authenticated-orcid":false,"given":"Adam","family":"Robson","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9371-7833","authenticated-orcid":false,"given":"Kamlesh","family":"Mistry","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8698-7605","authenticated-orcid":false,"given":"Wai-Lok","family":"Woo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,15]]},"reference":[{"issue":"6","key":"1763_CR1","doi-asserted-by":"publisher","first-page":"1046","DOI":"10.3390\/sym12061046","volume":"12","author":"O Almomani","year":"2020","unstructured":"Almomani O (2020) A feature selection model for network intrusion detection system based on PSO, Gwo, FFA and GA algorithms. Symmetry 12(6):1046. https:\/\/doi.org\/10.3390\/sym12061046","journal-title":"Symmetry"},{"issue":"06","key":"1763_CR2","doi-asserted-by":"publisher","first-page":"2451","DOI":"10.4236\/ojbm.2020.86151","volume":"08","author":"A Altherwi","year":"2020","unstructured":"Altherwi A (2020) Application of the Firefly algorithm for optimal production and demand forecasting at selected Industrial Plant. Open J Bus Manage 08(06):2451\u20132459. https:\/\/doi.org\/10.4236\/ojbm.2020.86151","journal-title":"Open J Bus Manage"},{"issue":"1","key":"1763_CR3","doi-asserted-by":"publisher","first-page":"192","DOI":"10.22266\/ijies2021.0228.19","volume":"14","author":"K Alwan","year":"2021","unstructured":"Alwan K, AbuEl-Atta A, Zayed H (2021) Feature selection models based on hybrid Firefly algorithm with mutation operator for network intrusion detection. Int J Intell Eng Syst 14(1):192\u2013202. https:\/\/doi.org\/10.22266\/ijies2021.0228.19","journal-title":"Int J Intell Eng Syst"},{"key":"1763_CR4","doi-asserted-by":"publisher","first-page":"104778","DOI":"10.1016\/j.micpro.2023.104778","volume":"98","author":"N Bacanin","year":"2023","unstructured":"Bacanin N et al (2023) A novel Firefly algorithm approach for efficient feature selection with covid-19 dataset. Microprocess Microsyst 98:104778. https:\/\/doi.org\/10.1016\/j.micpro.2023.104778","journal-title":"Microprocess Microsyst"},{"issue":"9","key":"1763_CR5","doi-asserted-by":"publisher","first-page":"9795","DOI":"10.1007\/s10489-021-02766-9","volume":"52","author":"L Cao","year":"2022","unstructured":"Cao L et al (2022) Enhancing Firefly algorithm with adaptive multi-group mechanism. Appl Intell 52(9):9795\u20139815. https:\/\/doi.org\/10.1007\/s10489-021-02766-9","journal-title":"Appl Intell"},{"issue":"2","key":"1763_CR6","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1108\/ec-07-2019-0305","volume":"38","author":"S Das","year":"2020","unstructured":"Das S, Sahu TP, Janghel RR (2020) PSO-based group-oriented crow search algorithm (PGCSA). Eng Comput 38(2):545\u2013571. https:\/\/doi.org\/10.1108\/ec-07-2019-0305","journal-title":"Eng Comput"},{"key":"1763_CR7","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.comnet.2018.02.028","volume":"136","author":"V Hajisalem","year":"2018","unstructured":"Hajisalem V, Babaie S (2018) A hybrid intrusion detection system based on ABC-AFS algorithm for misuse and anomaly detection. Comput Netw 136:37\u201350. https:\/\/doi.org\/10.1016\/j.comnet.2018.02.028","journal-title":"Comput Netw"},{"issue":"2","key":"1763_CR8","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1016\/j.asej.2019.10.003","volume":"11","author":"AM Hemeida","year":"2020","unstructured":"Hemeida AM et al (2020) Implementation of nature-inspired optimization algorithms in some data mining tasks. Ain Shams Eng J 11(2):309\u2013318. https:\/\/doi.org\/10.1016\/j.asej.2019.10.003","journal-title":"Ain Shams Eng J"},{"issue":"10","key":"1763_CR9","doi-asserted-by":"publisher","first-page":"1821","DOI":"10.3390\/math8101821","volume":"8","author":"AG Hussien","year":"2020","unstructured":"Hussien AG et al (2020) Binary whale optimization algorithm for dimensionality reduction. Mathematics 8(10):1821. https:\/\/doi.org\/10.3390\/math8101821","journal-title":"Mathematics"},{"key":"1763_CR10","doi-asserted-by":"publisher","unstructured":"Jain A, Sharma S, Sharma S (2021) \u2018Firefly algorithm\u2019, nature-inspired algorithms applications, pp 157\u2013180. https:\/\/doi.org\/10.1002\/9781119681984.ch6","DOI":"10.1002\/9781119681984.ch6"},{"key":"1763_CR11","doi-asserted-by":"publisher","first-page":"106894","DOI":"10.1016\/j.knosys.2021.106894","volume":"219","author":"F K\u0131l\u0131\u00e7","year":"2021","unstructured":"K\u0131l\u0131\u00e7 F, Kaya Y, Yildirim S (2021) A novel multi population based particle swarm optimization for feature selection. Knowl Based Syst 219:106894. https:\/\/doi.org\/10.1016\/j.knosys.2021.106894","journal-title":"Knowl Based Syst"},{"issue":"6","key":"1763_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3136625","volume":"50","author":"J Li","year":"2017","unstructured":"Li J et al (2017) Feature selection. ACM-CSUR 50(6):1\u201345. https:\/\/doi.org\/10.1145\/3136625","journal-title":"ACM-CSUR"},{"key":"1763_CR13","doi-asserted-by":"publisher","first-page":"107149","DOI":"10.1016\/j.compbiolchem.2019.107149","volume":"83","author":"X Lin","year":"2019","unstructured":"Lin X et al (2019) A new feature selection method based on symmetrical uncertainty and interaction gain. Comput Biol Chem 83:107149. https:\/\/doi.org\/10.1016\/j.compbiolchem.2019.107149","journal-title":"Comput Biol Chem"},{"issue":"2","key":"1763_CR14","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1515\/jisys-2017-0127","volume":"28","author":"P Malhotra","year":"2019","unstructured":"Malhotra P, Kumar D (2019) An optimized face recognition system using cuckoo search. J Intell Syst 28(2):321\u2013332. https:\/\/doi.org\/10.1515\/jisys-2017-0127","journal-title":"J Intell Syst"},{"key":"1763_CR15","doi-asserted-by":"publisher","unstructured":"Mohy-eddine M et al (2023) An intrusion detection model using election-based feature selection and K-NN\u2019, microprocessors and microsystems, p 104966. https:\/\/doi.org\/10.1016\/j.micpro.2023.104966","DOI":"10.1016\/j.micpro.2023.104966"},{"key":"1763_CR16","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.asoc.2016.01.044","volume":"43","author":"P Moradi","year":"2016","unstructured":"Moradi P, Gholampour M (2016) A hybrid particle swarm optimization for feature subset selection by integrating a novel local search strategy. Appl Soft Comput 43:117\u2013130. https:\/\/doi.org\/10.1016\/j.asoc.2016.01.044","journal-title":"Appl Soft Comput"},{"key":"1763_CR17","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.knosys.2018.01.002","volume":"145","author":"S Nakariyakul","year":"2018","unstructured":"Nakariyakul S (2018) High-dimensional hybrid feature selection using interaction information-guided search. Knowl Based Syst 145:59\u201366. https:\/\/doi.org\/10.1016\/j.knosys.2018.01.002","journal-title":"Knowl Based Syst"},{"key":"1763_CR18","doi-asserted-by":"publisher","DOI":"10.5220\/0011672200003393","author":"A Robson","year":"2023","unstructured":"Robson A, Mistry K, Woo W (2023) A novel group-based Firefly algorithm with adaptive intensity behaviour. Proc 15th Int Conf Agents Artif Intell [Preprint]. https:\/\/doi.org\/10.5220\/0011672200003393","journal-title":"Proc 15th Int Conf Agents Artif Intell"},{"key":"1763_CR19","doi-asserted-by":"publisher","first-page":"29637","DOI":"10.1109\/access.2018.2843443","volume":"6","author":"SB Sakri","year":"2018","unstructured":"Sakri SB, Rashid A, N.B. and, Muhammad Zain Z (2018) Particle swarm optimization feature selection for breast cancer recurrence prediction. IEEE Access 6:29637\u201329647. https:\/\/doi.org\/10.1109\/access.2018.2843443","journal-title":"IEEE Access"},{"key":"1763_CR20","doi-asserted-by":"publisher","unstructured":"Seetharaman A, Sundersingh AC (2021) Gene selection and classification using correlation feature selection based binary BAT algorithm with greedy crossover. Concurrency Computation Pract Experience 34(5). https:\/\/doi.org\/10.1002\/cpe.6718","DOI":"10.1002\/cpe.6718"},{"key":"1763_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2013\/325973","volume":"2013","author":"AM Taha","year":"2013","unstructured":"Taha AM, Mustapha A, Chen S-D (2013) Naive Bayes-guided Bat Algorithm for feature selection. Sci World J 2013:1\u20139. https:\/\/doi.org\/10.1155\/2013\/325973","journal-title":"Sci World J"},{"key":"1763_CR22","doi-asserted-by":"publisher","unstructured":"Tong N et al (2017) A multi-group Firefly algorithm for numerical optimization. J Phys Conf Ser 887(1):012060. https:\/\/doi.org\/10.1088\/1742-6596\/887\/1\/012060","DOI":"10.1088\/1742-6596\/887\/1\/012060"},{"key":"1763_CR23","doi-asserted-by":"publisher","unstructured":"Tripathi, D. et al. (2021) Bat algorithm based feature selection: Application in credit scoring. J Intell Fuzzy Syst 41(5):5561\u20135570. https:\/\/doi.org\/10.3233\/jifs-189876","DOI":"10.3233\/jifs-189876"},{"key":"1763_CR24","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.ijar.2018.12.013","volume":"106","author":"C Wang","year":"2019","unstructured":"Wang C et al (2019) Attribute reduction based on K-Nearest neighborhood rough sets. Int J Approximate Reasoning 106:18\u201331. https:\/\/doi.org\/10.1016\/j.ijar.2018.12.013","journal-title":"Int J Approximate Reasoning"},{"key":"1763_CR25","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1016\/j.asoc.2015.01.043","volume":"31","author":"J Xiang","year":"2015","unstructured":"Xiang J et al (2015) A novel hybrid system for feature selection based on an improved gravitational search algorithm and K-NN Method. Appl Soft Comput 31:293\u2013307. https:\/\/doi.org\/10.1016\/j.asoc.2015.01.043","journal-title":"Appl Soft Comput"},{"issue":"5","key":"1763_CR26","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1504\/ijbic.2011.042259","volume":"3","author":"XS Yang","year":"2011","unstructured":"Yang XS (2011) Bat algorithm for multi-objective optimisation. Int J Bio-Inspired Comput 3(5):267. https:\/\/doi.org\/10.1504\/ijbic.2011.042259","journal-title":"Int J Bio-Inspired Comput"},{"issue":"1","key":"1763_CR27","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1504\/ijsi.2013.055801","volume":"1","author":"XS Yang","year":"2013","unstructured":"Yang XS, He X (2013) Firefly Algorithm: recent advances and applications. Int J Swarm Intell 1(1):36. https:\/\/doi.org\/10.1504\/ijsi.2013.055801","journal-title":"Int J Swarm Intell"},{"key":"1763_CR28","doi-asserted-by":"publisher","first-page":"117562","DOI":"10.1016\/j.eswa.2022.117562","volume":"204","author":"X Zhao","year":"2022","unstructured":"Zhao X et al (2022) Multi-swarm improved moth\u2013flame optimization algorithm with chaotic grouping and gaussian mutation for solving engineering optimization problems. Expert Syst Appl 204:117562. https:\/\/doi.org\/10.1016\/j.eswa.2022.117562","journal-title":"Expert Syst Appl"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01763-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-024-01763-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01763-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T16:33:17Z","timestamp":1738945997000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-024-01763-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,15]]},"references-count":28,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["1763"],"URL":"https:\/\/doi.org\/10.1007\/s40747-024-01763-y","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"type":"print","value":"2199-4536"},{"type":"electronic","value":"2198-6053"}],"subject":[],"published":{"date-parts":[[2025,1,15]]},"assertion":[{"value":"24 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"On behalf of all authors, the corresponding author states that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"141"}}