{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T18:24:13Z","timestamp":1772303053466,"version":"3.50.1"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"28-29","license":[{"start":{"date-parts":[[2021,1,9]],"date-time":"2021-01-09T00:00:00Z","timestamp":1610150400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,9]],"date-time":"2021-01-09T00:00:00Z","timestamp":1610150400000},"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":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,11]]},"DOI":"10.1007\/s11042-020-10304-x","type":"journal-article","created":{"date-parts":[[2021,1,9]],"date-time":"2021-01-09T20:08:02Z","timestamp":1610222882000},"page":"35415-35439","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Oppositional salp swarm algorithm with mutation operator for global optimization and application in training higher order neural networks"],"prefix":"10.1007","volume":"80","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3051-7487","authenticated-orcid":false,"given":"Nibedan","family":"Panda","sequence":"first","affiliation":[]},{"given":"Santosh Kumar","family":"Majhi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,1,9]]},"reference":[{"key":"10304_CR1","doi-asserted-by":"crossref","unstructured":"Abusnaina AA, Ahmad S, Jarrar R, Mafarja M (2018) Training neural networks using salp swarm algorithm for pattern classification. In Proceedings of the 2nd International Conference on Future Networks and Distributed Systems (p. 17). ACM","DOI":"10.1145\/3231053.3231070"},{"issue":"2","key":"10304_CR2","doi-asserted-by":"publisher","first-page":"183","DOI":"10.7763\/IJMLC.2014.V4.409","volume":"4","author":"I Ahmed","year":"2014","unstructured":"Ahmed I, Guan D, Chung TC (2014) Sms classification based on naive bayes classifier and apriori algorithm frequent itemset. Int J mach Learn comput 4(2):183","journal-title":"Int J mach Learn comput"},{"key":"10304_CR3","doi-asserted-by":"crossref","unstructured":"Ahmed S, Mafarja M, Faris H, Aljarah I (2018) Feature selection using salp swarm algorithm with chaos. In Proceedings of the 2nd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence (pp. 65-69). ACM","DOI":"10.1145\/3206185.3206198"},{"issue":"5","key":"10304_CR4","doi-asserted-by":"publisher","first-page":"054702","DOI":"10.1063\/1.5020999","volume":"89","author":"S Asaithambi","year":"2018","unstructured":"Asaithambi S, Rajappa M (2018) Swarm intelligence-based approach for optimal design of CMOS differential amplifier and comparator circuit using a hybrid salp swarm algorithm. Review of Scientific Instruments 89(5):054702","journal-title":"Review of Scientific Instruments"},{"key":"10304_CR5","unstructured":"Bache K, Lichman M (2013) UCI machine learning repository [http:\/\/archive.ics.uci.edu\/ml]. Irvine, CA: University of California. School of information and computer science, 28"},{"issue":"3","key":"10304_CR6","first-page":"94","volume":"1","author":"U Can","year":"2015","unstructured":"Can U, Alatas B (2015) Physics based metaheuristic algorithms for global optimization. American Journal of Information Science and Computer Engineering 1(3):94\u2013106","journal-title":"American Journal of Information Science and Computer Engineering"},{"key":"10304_CR7","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/j.eswa.2016.10.050","volume":"69","author":"A Chakri","year":"2017","unstructured":"Chakri A, Khelif R, Benouaret M, Yang XS (2017) New directional bat algorithm for continuous optimization problems. Expert Syst Appl 69:159\u2013175","journal-title":"Expert Syst Appl"},{"key":"10304_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.asoc.2017.12.043","volume":"65","author":"A da Silva Ferreira","year":"2018","unstructured":"da Silva Ferreira A, da Silva Santos CH, Gon\u00e7alves MS, Figueroa HEH (2018) Towards an integrated evolutionary strategy and artificial neural network computational tool for designing photonic coupler devices. Appl Soft Comput 65:1\u201311","journal-title":"Appl Soft Comput"},{"key":"10304_CR9","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.advengsoft.2017.05.014","volume":"114","author":"G Dhiman","year":"2017","unstructured":"Dhiman G, Kumar V (2017) Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Adv Eng Softw 114:48\u201370","journal-title":"Adv Eng Softw"},{"key":"10304_CR10","first-page":"473","volume":"66","author":"SK Dinkar","year":"2017","unstructured":"Dinkar SK, Deep K (2017) Opposition based Laplacian ant lion optimizer. Journal of computational science, 23, pp.71-90.Pappula, L. and Ghosh, D., 2018. Cat swarm optimization with normal mutation for fast convergence of multimodal functions. Appl Soft Comput 66:473\u2013491","journal-title":"Appl Soft Comput"},{"key":"10304_CR11","doi-asserted-by":"crossref","unstructured":"Dinkar SK, Deep K (2018) Accelerated opposition-based Antlion optimizer with application to order reduction of linear time-invariant systems. Arabian Journal for Science and Engineering, pp.1-29","DOI":"10.1007\/s13369-018-3370-4"},{"key":"10304_CR12","doi-asserted-by":"crossref","unstructured":"Ekinci S, Hekimoglu B (2018) Parameter optimization of power system stabilizer via Salp swarm algorithm. In 2018 5th International Conference on Electrical and Electronic Engineering (ICEEE) (pp. 143-147). IEEE","DOI":"10.1109\/ICEEE2.2018.8391318"},{"key":"10304_CR13","unstructured":"F Distribution Table (2018) Retrieved from http:\/\/www.socr.ucla.edu\/applets.dir\/f_table.html."},{"key":"10304_CR14","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.eswa.2015.12.004","volume":"49","author":"AS Ghareb","year":"2016","unstructured":"Ghareb AS, Bakar AA, Hamdan AR (2016) Hybrid feature selection based on enhanced genetic algorithm for text categorization. Expert Syst Appl 49:31\u201347","journal-title":"Expert Syst Appl"},{"issue":"1","key":"10304_CR15","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1016\/j.eswa.2011.07.046","volume":"39","author":"RC Green II","year":"2012","unstructured":"Green RC II, Wang L, Alam M (2012) Training neural networks using central force optimization and particle swarm optimization: insights and comparisons. Expert Syst Appl 39(1):555\u2013563","journal-title":"Expert Syst Appl"},{"key":"10304_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.engappai.2016.11.003","volume":"61","author":"X Han","year":"2017","unstructured":"Han X, Quan L, Xiong X, Almeter M, Xiang J, Lan Y (2017) A novel data clustering algorithm based on modified gravitational search algorithm. Eng Appl Artif Intell 61:1\u20137","journal-title":"Eng Appl Artif Intell"},{"key":"10304_CR17","unstructured":"Hegazy AE, Makhlouf MA, El-Tawel GS (2018) Improved salp swarm algorithm for feature selection. Journal of King Saud University-Computer and Information Sciences"},{"key":"10304_CR18","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/j.asoc.2017.10.002","volume":"62","author":"L Hong","year":"2018","unstructured":"Hong L, Drake JH, Woodward JR, \u00d6zcan E (2018) A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming. Appl Soft Comput 62:162\u2013175","journal-title":"Appl Soft Comput"},{"issue":"12","key":"10304_CR19","first-page":"13","volume":"17","author":"HT Ibrahim","year":"2017","unstructured":"Ibrahim HT, Mazher WJ, Ucan ON, Bayat O (2017) Feature selection using Salp swarm algorithm for real biomedical datasets. IJCSNS 17(12):13","journal-title":"IJCSNS"},{"issue":"6","key":"10304_CR20","doi-asserted-by":"publisher","first-page":"1329","DOI":"10.1007\/s00521-014-1613-1","volume":"25","author":"AR Jordehi","year":"2014","unstructured":"Jordehi AR (2014) A chaotic-based big bang\u2013big crunch algorithm for solving global optimisation problems. Neural Comput & Applic 25(6):1329\u20131335","journal-title":"Neural Comput & Applic"},{"key":"10304_CR21","doi-asserted-by":"crossref","unstructured":"Karaboga D, Basturk B (2007) Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. In International fuzzy systems association world congress (pp. 789-798). Springer, Berlin, Heidelberg","DOI":"10.1007\/978-3-540-72950-1_77"},{"key":"10304_CR22","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1016\/j.neucom.2017.04.053","volume":"260","author":"MM Mafarja","year":"2017","unstructured":"Mafarja MM, Mirjalili S (2017) Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 260:302\u2013312","journal-title":"Neurocomputing"},{"key":"10304_CR23","doi-asserted-by":"crossref","unstructured":"Meraihi Y, Ramdane-Cherif A, Mahseur M, Achelia D (2018) A chaotic binary Salp swarm algorithm for solving the graph coloring problem. In International Symposium on Modelling and Implementation of Complex Systems (pp. 106-118). Springer, Cham.","DOI":"10.1007\/978-3-030-05481-6_8"},{"key":"10304_CR24","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.knosys.2015.07.006","volume":"89","author":"S Mirjalili","year":"2015","unstructured":"Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228\u2013249","journal-title":"Knowl-Based Syst"},{"key":"10304_CR25","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.advengsoft.2015.01.010","volume":"83","author":"S Mirjalili","year":"2015","unstructured":"Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80\u201398","journal-title":"Adv Eng Softw"},{"key":"10304_CR26","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.knosys.2015.12.022","volume":"96","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120\u2013133","journal-title":"Knowl-Based Syst"},{"key":"10304_CR27","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":"10304_CR28","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 (2014) Grey wolf optimizer. Adv Eng Softw 69:46\u201361","journal-title":"Adv Eng Softw"},{"key":"10304_CR29","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.advengsoft.2017.07.002","volume":"114","author":"S Mirjalili","year":"2017","unstructured":"Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163\u2013191","journal-title":"Adv Eng Softw"},{"key":"10304_CR30","doi-asserted-by":"crossref","unstructured":"Misra BB, Dehuri S (2007) Functional link artificial neural network for classification task in data mining","DOI":"10.1109\/CEC.2007.4424542"},{"issue":"2","key":"10304_CR31","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1109\/TCYB.2015.2404806","volume":"46","author":"K Nag","year":"2016","unstructured":"Nag K, Pal NR (2016) A multiobjective genetic programming-based ensemble for simultaneous feature selection and classification. IEEE transactions on cybernetics 46(2):499\u2013510","journal-title":"IEEE transactions on cybernetics"},{"key":"10304_CR32","unstructured":"Normal Distribution Table. (n.d.) Retrieved from http:\/\/math.arizona.edu\/~rsims\/ma464\/standardnormaltable.pdf."},{"key":"10304_CR33","doi-asserted-by":"crossref","unstructured":"Panda N, Majhi SK (2019) How effective is spotted hyena optimizer for training multilayer Perceptrons. Int J Recent Technol Eng, 4915-4927.","DOI":"10.35940\/ijrte.B3736.078219"},{"key":"10304_CR34","doi-asserted-by":"crossref","unstructured":"Panda N, Majhi SK (2019) Improved Salp swarm algorithm with space transformation search for training neural network. Arabian Journal for Science and Engineering, pp. 1-19","DOI":"10.1007\/s13369-019-04132-x"},{"key":"10304_CR35","doi-asserted-by":"crossref","unstructured":"Panda N, Majhi SK (2020) How effective is the Salp swarm algorithm in data classification. In Computational Intelligence in Pattern Recognition (pp. 579-588). Springer, Singapore","DOI":"10.1007\/978-981-13-9042-5_49"},{"key":"10304_CR36","unstructured":"Panda N, Majhi SK (n.d.) Improved spotted hyena optimizer with space transformational search for training pi-sigma higher order neural network. Comput Intell"},{"key":"10304_CR37","doi-asserted-by":"crossref","unstructured":"Panda N, Majhi SK, Singh S, Khanna A (2020) Oppositional spotted hyena optimizer with mutation operator for global optimization and application in training wavelet neural network. Journal of Intelligent & Fuzzy Systems, (preprint), pp.1-14.","DOI":"10.3233\/JIFS-179746"},{"key":"10304_CR38","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1016\/j.asoc.2018.02.012","volume":"66","author":"L Pappula","year":"2018","unstructured":"Pappula L, Ghosh D (2018) Cat swarm optimization with normal mutation for fast convergence of multimodal functions. Appl Soft Comput 66:473\u2013491","journal-title":"Appl Soft Comput"},{"issue":"12","key":"10304_CR39","first-page":"12707","volume":"119","author":"N Patnana","year":"2018","unstructured":"Patnana N, Pattnaik S, Singh VP (2018) Salp swarm optimization based PID controller tuning for Doha reverse osmosis desalination plant. Int J Pure Appl Math 119(12):12707\u201312720","journal-title":"Int J Pure Appl Math"},{"key":"10304_CR40","doi-asserted-by":"crossref","unstructured":"Rizk-Allah RM, Hassanien AE, Elhoseny M, Gunasekaran M (2018) A new binary salp swarm algorithm: development and application for optimization tasks. Neural Comput Appl, 1\u201323","DOI":"10.1007\/s00521-018-3613-z"},{"issue":"2","key":"10304_CR41","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1007\/s00366-015-0415-0","volume":"32","author":"A Saghatforoush","year":"2016","unstructured":"Saghatforoush A, Monjezi M, Faradonbeh RS, Armaghani DJ (2016) Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting. Eng Comput 32(2):255\u2013266","journal-title":"Eng Comput"},{"issue":"1","key":"10304_CR42","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1006\/jcph.2000.6568","volume":"164","author":"DP Schmidt","year":"2000","unstructured":"Schmidt DP, Rutland CJ (2000) A new droplet collision algorithm. J Comput Phys 164(1):62\u201380","journal-title":"J Comput Phys"},{"key":"10304_CR43","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1016\/j.cam.2015.03.050","volume":"291","author":"JA Sicilia","year":"2016","unstructured":"Sicilia JA, Quemada C, Royo B, Escu\u00edn D (2016) An optimization algorithm for solving the rich vehicle routing problem based on variable neighborhood search and Tabu search metaheuristics. J Comput Appl Math 291:468\u2013477","journal-title":"J Comput Appl Math"},{"issue":"1","key":"10304_CR44","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/s12293-016-0220-3","volume":"10","author":"CK Ting","year":"2018","unstructured":"Ting CK, Liaw RT, Wang TC, Hong TP (2018) Mining fuzzy association rules using a memetic algorithm based on structure representation. Memetic Computing 10(1):15\u201328","journal-title":"Memetic Computing"},{"issue":"1","key":"10304_CR45","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1007\/s10994-006-6889-7","volume":"65","author":"I Tsamardinos","year":"2006","unstructured":"Tsamardinos I, Brown LE, Aliferis CF (2006) The max-min hill-climbing Bayesian network structure learning algorithm. Mach Learn 65(1):31\u201378","journal-title":"Mach Learn"},{"key":"10304_CR46","doi-asserted-by":"crossref","unstructured":"Wang H, Wu Z, Liu Y, Wang J, Jiang D, Chen L (2009) Space transformation search: a new evolutionary technique. In Proceedings of the first ACM\/SIGEVO Summit on Genetic and Evolutionary Computation (pp. 537-544). ACM","DOI":"10.1145\/1543834.1543907"},{"issue":"2","key":"10304_CR47","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1007\/s00500-016-2474-6","volume":"22","author":"D Wang","year":"2018","unstructured":"Wang D, Tan D, Liu L (2018) Particle swarm optimization algorithm: an overview. Soft Comput 22(2):387\u2013408","journal-title":"Soft Comput"},{"issue":"6","key":"10304_CR48","doi-asserted-by":"publisher","first-page":"1561","DOI":"10.3390\/en11061561","volume":"11","author":"J Wang","year":"2018","unstructured":"Wang J, Gao Y, Chen X (2018) A novel hybrid interval prediction approach based on modified lower upper bound estimation in combination with multi-objective salp swarm algorithm for short-term load forecasting. Energies 11(6):1561","journal-title":"Energies"},{"issue":"1","key":"10304_CR49","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":"10304_CR50","unstructured":"Wu G, Mallipeddi R, Suganthan PN (2017) Problem definitions and evaluation criteria for the cec 2017 competition on constrained real-parameter optimization. National University of Defense Technology, Changsha, Hunan, PR China and Kyungpook National University, Daegu, South Korea and Nanyang Technological University, Singapore, Technical Report"},{"key":"10304_CR51","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1016\/j.ins.2017.09.053","volume":"423","author":"G Wu","year":"2018","unstructured":"Wu G, Shen X, Li H, Chen H, Lin A, Suganthan PN (2018) Ensemble of differential evolution variants. Inf Sci 423:172\u2013186","journal-title":"Inf Sci"},{"key":"10304_CR52","doi-asserted-by":"crossref","unstructured":"Yang XS (2012) Flower pollination algorithm for global optimization. In International conference on unconventional computing and natural computation (pp. 240-249). Springer, Berlin, Heidelberg.","DOI":"10.1007\/978-3-642-32894-7_27"},{"issue":"2","key":"10304_CR53","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1007\/s00366-012-0254-1","volume":"29","author":"XS Yang","year":"2013","unstructured":"Yang XS (2013) Multiobjective firefly algorithm for continuous optimization. Eng Comput 29(2):175\u2013184","journal-title":"Eng Comput"},{"key":"10304_CR54","doi-asserted-by":"crossref","unstructured":"Yang XS, Deb S (2010) Engineering optimisation by cuckoo search. arXiv preprint arXiv:1005.2908","DOI":"10.1504\/IJMMNO.2010.035430"},{"issue":"4","key":"10304_CR55","doi-asserted-by":"publisher","first-page":"925","DOI":"10.1109\/72.774263","volume":"10","author":"L Zhang","year":"1999","unstructured":"Zhang L, Zhang B (1999) A geometrical representation of McCulloch-Pitts neural model and its applications. IEEE Trans Neural Netw 10(4):925\u2013929","journal-title":"IEEE Trans Neural Netw"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-10304-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-020-10304-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-10304-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,30]],"date-time":"2021-11-30T17:29:11Z","timestamp":1638293351000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-020-10304-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,9]]},"references-count":55,"journal-issue":{"issue":"28-29","published-print":{"date-parts":[[2021,11]]}},"alternative-id":["10304"],"URL":"https:\/\/doi.org\/10.1007\/s11042-020-10304-x","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,9]]},"assertion":[{"value":"5 April 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 September 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 December 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}