{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T15:11:36Z","timestamp":1777043496556,"version":"3.51.4"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,8,21]],"date-time":"2021-08-21T00:00:00Z","timestamp":1629504000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,8,21]],"date-time":"2021-08-21T00:00:00Z","timestamp":1629504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Evolving Systems"],"published-print":{"date-parts":[[2022,8]]},"DOI":"10.1007\/s12530-021-09401-5","type":"journal-article","created":{"date-parts":[[2021,8,21]],"date-time":"2021-08-21T15:02:41Z","timestamp":1629558161000},"page":"563-575","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Battle royale optimizer for training multi-layer perceptron"],"prefix":"10.1007","volume":"13","author":[{"given":"Saeid","family":"Agahian","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4070-1058","authenticated-orcid":false,"given":"Taymaz","family":"Akan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,8,21]]},"reference":[{"issue":"1","key":"9401_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00500-016-2442-1","volume":"22","author":"I Aljarah","year":"2018","unstructured":"Aljarah I, Faris H, Mirjalili S (2018) Optimizing connection weights in neural networks using the whale optimization algorithm. Soft Computing 22(1):1\u201315","journal-title":"Soft Computing"},{"key":"9401_CR2","volume-title":"Neural networks and deep learning","author":"NG Andrew","year":"2020","unstructured":"Andrew NG, Katanforoosh K, Mourri YB (2020) Neural networks and deep learning. McGraw Hill, New York"},{"issue":"4","key":"9401_CR3","doi-asserted-by":"publisher","first-page":"1213","DOI":"10.1016\/j.cnsns.2013.08.027","volume":"19","author":"A Askarzadeh","year":"2014","unstructured":"Askarzadeh A (2014) Bird mating optimizer: an optimization algorithm inspired by bird mating strategies. Commun Nonlinear Sci Numer Simul 19(4):1213\u20131228","journal-title":"Commun Nonlinear Sci Numer Simul"},{"issue":"2","key":"9401_CR4","doi-asserted-by":"publisher","first-page":"1206","DOI":"10.1016\/j.asoc.2012.10.023","volume":"13","author":"A Askarzadeh","year":"2013","unstructured":"Askarzadeh A, Rezazadeh A (2013) Artificial neural network training using a new efficient optimization algorithm. Applied Soft Computing 13(2):1206\u20131213","journal-title":"Applied Soft Computing"},{"key":"9401_CR5","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.cogsys.2019.06.003","volume":"58","author":"K Bhattacharjee","year":"2019","unstructured":"Bhattacharjee K, Pant M (2019) Hybrid particle swarm optimization-genetic algorithm trained multi-layer perceptron for classification of human glioma from molecular brain neoplasia data. Cogn Syst Res 58:173\u2013194","journal-title":"Cogn Syst Res"},{"key":"9401_CR6","doi-asserted-by":"crossref","unstructured":"Blum C and Socha K (2005) Training feed-forward neural networks with ant colony optimization: an application to pattern classification. In Fifth International Conference on Hybrid Intelligent Systems (HIS'05), p 6.","DOI":"10.1109\/ICHIS.2005.104"},{"key":"9401_CR7","unstructured":"Braik M, Sheta A, Arieqat A (2008) A comparison between GAs and PSO in training ANN to model the TE chemical process reactor. Proceedings of the AISB 2008 symposium on swarm intelligence algorithms and applications, vol 11, pp 24\u201330."},{"issue":"8","key":"9401_CR8","doi-asserted-by":"publisher","first-page":"2005","DOI":"10.1007\/s00521-016-2190-2","volume":"28","author":"S Chatterjee","year":"2017","unstructured":"Chatterjee S, Sarkar S, Hore S, Dey N, Ashour AS, Balas VE (2017) Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings. Neural Comput Appl 28(8):2005\u20132016","journal-title":"Neural Comput Appl"},{"key":"9401_CR9","first-page":"1470","volume":"2","author":"M Dorigo","year":"1999","unstructured":"Dorigo M, Di Caro G (1999) Ant colony optimization: a new meta-heuristic. Proc Congr Evol Comput 2:1470\u20131477","journal-title":"Proc Congr Evol Comput"},{"key":"9401_CR10","unstructured":"Eberhart R and Kennedy J (1995) A new optimizer using particle swarm theory. MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39\u201343: IEEE."},{"key":"9401_CR12","volume-title":"Neural networks: a comprehensive foundation","author":"S Haykin","year":"2007","unstructured":"Haykin S (2007) Neural networks: a comprehensive foundation. Prentice-Hall, Inc., Upper Saddle River"},{"key":"9401_CR13","volume-title":"The organization of behavior: a neuropsychological theory","author":"DO Hebb","year":"1949","unstructured":"Hebb DO (1949) The organization of behavior: a neuropsychological theory. Wiley, New York"},{"key":"9401_CR14","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/1090.001.0001","volume-title":"Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence","author":"JH Holland","year":"1992","unstructured":"Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press, London"},{"issue":"1","key":"9401_CR15","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1023\/A:1022995128597","volume":"17","author":"J Ilonen","year":"2003","unstructured":"Ilonen J, Kamarainen J-K, Lampinen J (2003) Differential evolution training algorithm for feed-forward neural networks. Neural Process Lett 17(1):93\u2013105","journal-title":"Neural Process Lett"},{"key":"9401_CR16","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.asoc.2015.08.002","volume":"37","author":"NS Jaddi","year":"2015","unstructured":"Jaddi NS, Abdullah S, Hamdan AR (2015) Optimization of neural network model using modified bat-inspired algorithm. Appl Soft Comput 37:71\u201386","journal-title":"Appl Soft Comput"},{"issue":"3","key":"9401_CR17","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1007\/s10898-007-9149-x","volume":"39","author":"D Karaboga","year":"2007","unstructured":"Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 39(3):459\u2013471","journal-title":"J Global Optim"},{"key":"9401_CR18","first-page":"318","volume-title":"Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks","author":"D Karaboga","year":"2007","unstructured":"Karaboga D, Akay B, Ozturk C (2007) Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks. Springer, Berlin, pp 318\u2013329"},{"key":"9401_CR19","volume-title":"Neural networks for vision, speech and natural language","author":"R Linggard","year":"2012","unstructured":"Linggard R, Myers D, Nightingale C (2012) Neural networks for vision, speech and natural language. Springer, Berlin"},{"key":"9401_CR20","first-page":"3","volume-title":"The appeal of parallel distributed processing","author":"JL McClelland","year":"1986","unstructured":"McClelland JL, Rumelhart DE, Hinton GE (1986) The appeal of parallel distributed processing. MIT Press, Cambridge, pp 3\u201344"},{"issue":"4","key":"9401_CR21","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/BF02478259","volume":"5","author":"WS McCulloch","year":"1943","unstructured":"McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115\u2013133","journal-title":"Bull Math Biophys"},{"issue":"1","key":"9401_CR22","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1007\/s10489-014-0645-7","volume":"43","author":"S Mirjalili","year":"2015","unstructured":"Mirjalili S (2015) How effective is the Grey Wolf optimizer in training multi-layer perceptrons. Appl Intell 43(1):150\u2013161","journal-title":"Appl Intell"},{"key":"9401_CR23","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","volume":"95","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51\u201367","journal-title":"Adv Eng Softw"},{"key":"9401_CR24","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","volume":"69","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili S, Mirjalili SM, Lewis A (2014a) Grey wolf optimizer. Adv Eng Softw 69:46\u201361","journal-title":"Adv Eng Softw"},{"key":"9401_CR25","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.ins.2014.01.038","volume":"269","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili S, Mirjalili SM, Lewis A (2014) Let a biogeography-based optimizer train your multi-layer perceptron. Inform Sci 269:188\u2013209","journal-title":"Inform Sci"},{"key":"9401_CR26","first-page":"762","volume":"89","author":"DJ Montana","year":"1989","unstructured":"Montana DJ, Davis L (1989) Training feedforward neural networks using genetic algorithms. IJCAI 89:762\u2013767","journal-title":"IJCAI"},{"key":"9401_CR27","first-page":"413","volume-title":"A new back-propagation neural network optimized with cuckoo search algorithm","author":"NM Nawi","year":"2013","unstructured":"Nawi NM, Khan A, Rehman MZ (2013) A new back-propagation neural network optimized with cuckoo search algorithm. Springer, Berlin, pp 413\u2013426"},{"issue":"1","key":"9401_CR28","first-page":"197","volume":"19","author":"J Nayak","year":"2016","unstructured":"Nayak J, Naik B, Behera HS (2016) A novel nature inspired firefly algorithm with higher order neural network: performance analysis. Eng Sci Technol Int J 19(1):197\u2013211","journal-title":"Eng Sci Technol Int J"},{"key":"9401_CR29","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.engappai.2017.01.013","volume":"60","author":"VK Ojha","year":"2017","unstructured":"Ojha VK, Abraham A, Sn\u00e1\u0161el V (2017) Metaheuristic design of feedforward neural networks: a review of two decades of research,\". Engineering Applications of Artificial Intelligence 60:97\u2013116","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"9401_CR30","first-page":"84","volume":"2011","author":"C Ozturk","year":"2011","unstructured":"Ozturk C, Karaboga D (2011) Hybrid artificial bee colony algorithm for neural network training. IEEE Congr Evol Comput 2011:84\u201388","journal-title":"IEEE Congr Evol Comput"},{"key":"9401_CR31","unstructured":"Price KV (1996) Differential evolution: a fast and simple numerical optimizer. In Proceedings of North American Fuzzy Information Processing, pp. 524\u2013527: IEEE."},{"key":"9401_CR33","doi-asserted-by":"publisher","first-page":"1139","DOI":"10.1007\/s00521-020-05004-4","volume":"33","author":"T Rahkar Farshi","year":"2020","unstructured":"Rahkar Farshi T (2020) Battle royale optimization algorithm. Neural Comput Appl 33:1139\u20131157","journal-title":"Neural Comput Appl"},{"issue":"13","key":"9401_CR34","doi-asserted-by":"publisher","first-page":"2232","DOI":"10.1016\/j.ins.2009.03.004","volume":"179","author":"E Rashedi","year":"2009","unstructured":"Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: A Gravitational search algorithm. Inform Sci 179(13):2232\u20132248","journal-title":"Inform Sci"},{"key":"9401_CR35","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85\u2013117","journal-title":"Neural Netw"},{"issue":"2","key":"9401_CR36","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/BF01876146","volume":"1","author":"H-P Schwefel","year":"1984","unstructured":"Schwefel H-P (1984) Evolution strategies: a family of non-linear optimization techniques based on imitating some principles of organic evolution. Ann Oper Res 1(2):165\u2013167","journal-title":"Ann Oper Res"},{"issue":"6","key":"9401_CR37","doi-asserted-by":"publisher","first-page":"702","DOI":"10.1109\/TEVC.2008.919004","volume":"12","author":"D Simon","year":"2008","unstructured":"Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702\u2013713","journal-title":"IEEE Trans Evol Comput"},{"issue":"4","key":"9401_CR38","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1023\/A:1008202821328","volume":"11","author":"R Storn","year":"1997","unstructured":"Storn R, Price K (1997) Differential evolution\u2013a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341\u2013359","journal-title":"J Global Optim"},{"key":"9401_CR11","unstructured":"Wdaa ASI and Sttar A (2008) Differential evolution for neural networks learning enhancement. Universiti Teknologi Malaysia"},{"key":"9401_CR39","doi-asserted-by":"crossref","unstructured":"Werbos P (1989) Back-propagation and neurocontrol: a review and prospectus. In: IEEE Proceedings of the International Joint Conference on Neural Networks (IJCNN'89), pp. 1, I209-I216.","DOI":"10.1109\/IJCNN.1989.118583"},{"key":"9401_CR40","first-page":"490","volume-title":"Minimizing the system error in feedforward neural networks with evolution strategy","author":"W Wienholt","year":"1993","unstructured":"Wienholt W (1993) Minimizing the system error in feedforward neural networks with evolution strategy. Springer, London, pp 490\u2013493"},{"issue":"1","key":"9401_CR41","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/4235.585893","volume":"1","author":"DH Wolpert","year":"1997","unstructured":"Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67\u201382","journal-title":"IEEE Trans Evol Comput"},{"key":"9401_CR32","doi-asserted-by":"crossref","unstructured":"Yadav RK and Anubhav (2020) PSO-GA based hybrid with adam optimization for ANN training with application in medical diagnosis. Cogn Syst Res 64:191\u2013199","DOI":"10.1016\/j.cogsys.2020.08.011"},{"key":"9401_CR42","first-page":"169","volume-title":"Firefly algorithms for multimodal optimization. International symposium on stochastic algorithms","author":"X-S Yang","year":"2009","unstructured":"Yang X-S (2009) Firefly algorithms for multimodal optimization. International symposium on stochastic algorithms. Springer, Berlin, pp 169\u2013178"},{"key":"9401_CR43","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/978-3-642-12538-6_6","volume-title":"Nature Inspired Cooperative Strategies for Optimization (NICSO 2010)","author":"X-S Yang","year":"2010","unstructured":"Yang X-S (2010) A new metaheuristic bat-inspired algorithm. In: Gonz\u00e1lez JR, Pelta DA, Cruz C, Terrazas G, Krasnogor N (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). Springer, Berlin, pp 65\u201374"},{"key":"9401_CR44","doi-asserted-by":"crossref","unstructured":"Yang X-S and Deb S (2009) Cuckoo search via L\u00e9vy flights. In 2009 World congress on nature & biologically inspired computing (NaBIC), pp 210\u2013214","DOI":"10.1109\/NABIC.2009.5393690"},{"issue":"2","key":"9401_CR45","first-page":"1026","volume":"185","author":"J-R Zhang","year":"2007","unstructured":"Zhang J-R, Zhang J, Lok T-M, Lyu MR (2007) A hybrid particle swarm optimization\u2013back-propagation algorithm for feedforward neural network training. Appl Math Comput 185(2):1026\u20131037","journal-title":"Appl Math Comput"},{"key":"9401_CR46","volume-title":"Introduction to artificial neural systems","author":"JM Zurada","year":"1992","unstructured":"Zurada JM (1992) Introduction to artificial neural systems. West St. Paul, New York"}],"container-title":["Evolving Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12530-021-09401-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12530-021-09401-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12530-021-09401-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,9]],"date-time":"2022-07-09T05:39:02Z","timestamp":1657345142000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12530-021-09401-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,21]]},"references-count":46,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["9401"],"URL":"https:\/\/doi.org\/10.1007\/s12530-021-09401-5","relation":{},"ISSN":["1868-6478","1868-6486"],"issn-type":[{"value":"1868-6478","type":"print"},{"value":"1868-6486","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,21]]},"assertion":[{"value":"29 January 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 August 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 August 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Authors declares that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}