{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T01:41:32Z","timestamp":1768441292179,"version":"3.49.0"},"reference-count":50,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T00:00:00Z","timestamp":1664323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>The African Vulture Optimization Algorithm (AVOA) is inspired by African vultures\u2019 feeding and orienting behaviors. It comprises powerful operators while maintaining the balance of exploration and efficiency in solving optimization problems. To be used in discrete applications, this algorithm needs to be discretized. This paper introduces two versions based on the S-shaped and V-shaped transfer functions of AVOA and BAOVAH. Moreover, the increase in computational complexity is avoided. Disruption operator and Bitwise strategy have also been used to maximize this model\u2019s performance. A multi-strategy version of the AVOA called BAVOA-v1 is presented. In the proposed approach, i.e., BAVOA-v1, different strategies such as IPRS, mutation neighborhood search strategy (MNSS) (balance between exploration and exploitation), multi-parent crossover (increasing exploitation), and Bitwise (increasing diversity and exploration) are used to provide solutions with greater variety and to assure the quality of solutions. The proposed methods are evaluated on 30 UCI datasets with different dimensions. The simulation results showed that the proposed BAOVAH algorithm performed better than other binary meta-heuristic algorithms. So that the proposed BAOVAH algorithm set is the most accurate in 67% of the data set, and 93% of the data set is the best value of the fitness functions. In terms of feature selection, it has shown high performance. Finally, the proposed method in a case study to determine the number of neurons and the activator function to improve deep learning results was used in the sentiment analysis of movie viewers. In this paper, the CNNEM model is designed. The results of experiments on three datasets of sentiment analysis\u2014IMDB, Amazon, and Yelp\u2014show that the BAOVAH algorithm increases the accuracy of the CNNEM network in the IMDB dataset by 6%, the Amazon dataset by 33%, and the Yelp dataset by 30%.<\/jats:p>","DOI":"10.3390\/bdcc6040104","type":"journal-article","created":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T20:58:47Z","timestamp":1664398727000},"page":"104","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["An Improved African Vulture Optimization Algorithm for Feature Selection Problems and Its Application of Sentiment Analysis on Movie Reviews"],"prefix":"10.3390","volume":"6","author":[{"given":"Aitak","family":"Shaddeli","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia 5756151818, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1588-1659","authenticated-orcid":false,"given":"Farhad","family":"Soleimanian Gharehchopogh","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia 5756151818, Iran"}]},{"given":"Mohammad","family":"Masdari","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia 5756151818, Iran"}]},{"given":"Vahid","family":"Solouk","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia 5756151818, Iran"},{"name":"Faculty of Information Technology and Computer Engineering, Urmia University of Technology, Urmia 5756151818, Iran"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4519","DOI":"10.1007\/s10462-019-09800-w","article-title":"A survey on feature selection approaches for clustering","volume":"53","author":"Hancer","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Nadimi-Shahraki, M.H., Banaie-Dezfouli, M., Zamani, H., Taghian, S., and Mirjalili, S. (2021). B-MFO: A binary moth-flame optimization for feature selection from medical datasets. Computers, 10.","DOI":"10.3390\/computers10110136"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1845","DOI":"10.1007\/s00366-021-01369-9","article-title":"A multi-objective optimization algorithm for feature selection problems","volume":"38","author":"Abdollahzadeh","year":"2021","journal-title":"Eng. Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"105858","DOI":"10.1016\/j.compbiomed.2022.105858","article-title":"Enhanced whale optimization algorithm for medical feature selection: A COVID-19 case study","volume":"148","author":"Zamani","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Shaddeli, A., Gharehchopogh, F.S., Masdari, M., and Solouk, V. (2022). BFRA: A New Binary Hyper-Heuristics Feature Ranks Algorithm for Feature Selection in High-Dimensional Classification Data. Int. J. Inf. Technol. Decis. Mak., 1\u201366.","DOI":"10.1142\/S0219622022500432"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hosseini, F., Gharehchopogh, F.S., and Masdari, M. (2022). A Botnet Detection in IoT Using a Hybrid Multi-objective Optimization Algorithm. New Gener. Comput., 1\u201335.","DOI":"10.1007\/s00354-022-00188-w"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Gharehchopogh, F.S. (2022). Advances in tree seed algorithm: A comprehensive survey. Arch. Comput. Methods Eng., 1\u201324.","DOI":"10.1007\/s11831-022-09804-w"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"de Carvalho, V.R., \u00d6zcan, E., and Sichman, J.S. (2021). Comparative Analysis of Selection Hyper-Heuristics for Real-World Multi-Objective Optimization Problems. Appl. Sci., 11.","DOI":"10.3390\/app11199153"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"15091","DOI":"10.1007\/s00521-021-06406-8","article-title":"A systematic review of emerging feature selection optimization methods for optimal text classification: The present state and prospective opportunities","volume":"33","author":"Abiodun","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"953","DOI":"10.3233\/IDA-160840","article-title":"HHFS: Hyper-heuristic feature selection","volume":"20","author":"Montazeri","year":"2016","journal-title":"Intell. Data Anal."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Gharehchopogh, F.S., Nadimi-Shahraki, M.H., Barshandeh, S., Abdollahzadeh, B., and Zamani, H. (2022). CQFFA: A Chaotic Quasi-oppositional Farmland Fertility Algorithm for Solving Engineering Optimization Problems. J. Bionic Eng., 1\u201326.","DOI":"10.1007\/s42235-022-00255-4"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Nadimi-Shahraki, M.H., Fatahi, A., Zamani, H., and Mirjalili, S. (2022). Binary Approaches of Quantum-Based Avian Navigation Optimizer to Select Effective Features from High-Dimensional Medical Data. Mathematics, 10.","DOI":"10.3390\/math10152770"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Gharehchopogh, F.S. (2022). An Improved Tunicate Swarm Algorithm with Best-random Mutation Strategy for Global Optimization Problems. J. Bionic Eng., 1\u201326.","DOI":"10.1007\/s42235-022-00185-1"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.swevo.2012.09.002","article-title":"S-shaped versus V-shaped transfer functions for binary particle swarm optimization","volume":"9","author":"Mirjalili","year":"2013","journal-title":"Swarm Evol. Comput."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.neucom.2015.06.083","article-title":"Binary grey wolf optimization approaches for feature selection","volume":"172","author":"Emary","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.neucom.2016.03.101","article-title":"Binary ant lion approaches for feature selection","volume":"213","author":"Emary","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"De Souza, R.C.T., dos Santos Coelho, L., De Macedo, C.A., and Pierezan, J. (2018, January 8\u201313). A V-shaped binary crow search algorithm for feature selection. Proceedings of the 2018 IEEE congress on evolutionary computation (CEC), Rio de Janeiro, Brazil.","DOI":"10.1109\/CEC.2018.8477975"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.knosys.2018.08.003","article-title":"Binary dragonfly optimization for feature selection using time-varying transfer functions","volume":"161","author":"Mafarja","year":"2018","journal-title":"Knowl. Based Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.knosys.2018.05.009","article-title":"An efficient binary salp swarm algorithm with crossover scheme for feature selection problems","volume":"154","author":"Faris","year":"2018","journal-title":"Knowl. Based Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s00521-017-2988-6","article-title":"Feature selection via a novel chaotic crow search algorithm","volume":"31","author":"Sayed","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.eswa.2018.08.051","article-title":"Binary butterfly optimization approaches for feature selection","volume":"116","author":"Arora","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"113122","DOI":"10.1016\/j.eswa.2019.113122","article-title":"Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection","volume":"145","author":"Tubishat","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"107470","DOI":"10.1016\/j.patcog.2020.107470","article-title":"Binary coyote optimization algorithm for feature selection","volume":"107","author":"Pierezan","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"112824","DOI":"10.1016\/j.eswa.2019.112824","article-title":"A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection","volume":"139","author":"Mirjalili","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"121127","DOI":"10.1109\/ACCESS.2020.3006473","article-title":"Improved harris hawks optimization using elite opposition-based learning and novel search mechanism for feature selection","volume":"8","author":"Sihwail","year":"2020","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1080\/0305215X.2019.1624740","article-title":"New binary whale optimization algorithm for discrete optimization problems","volume":"52","author":"Hussien","year":"2020","journal-title":"Eng. Optim."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"9102","DOI":"10.1007\/s11227-021-03626-6","article-title":"An efficient binary chaotic symbiotic organisms search algorithm approaches for feature selection problems","volume":"77","author":"Mohmmadzadeh","year":"2021","journal-title":"J. Supercomput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"106560","DOI":"10.1016\/j.knosys.2020.106560","article-title":"BEPO: A novel binary emperor penguin optimizer for automatic feature selection","volume":"211","author":"Dhiman","year":"2021","journal-title":"Knowl. Based Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4824","DOI":"10.1007\/s10489-020-02038-y","article-title":"A novel binary farmland fertility algorithm for feature selection in analysis of the text psychology","volume":"51","author":"Hosseinalipour","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"107408","DOI":"10.1016\/j.cie.2021.107408","article-title":"African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems","volume":"158","author":"Abdollahzadeh","year":"2021","journal-title":"Comput. Ind. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.knosys.2015.12.022","article-title":"SCA: A sine cosine algorithm for solving optimization problems","volume":"96","author":"Mirjalili","year":"2016","journal-title":"Knowl. Based Syst."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Cowling, P., Kendall, G., and Soubeiga, E. (2000, January 16\u201318). A hyperheuristic approach to scheduling a sales summit. Proceedings of the International Conference on the Practice and Theory of Automated Timetabling, Konstanz, Germany.","DOI":"10.1007\/3-540-44629-X_11"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Cowling, P., Kendall, G., and Soubeiga, E. (2001, January 26\u201328). A parameter-free hyperheuristic for scheduling a sales summit. Proceedings of the 4th Metaheuristic International Conference, MIC, Fairfax, VA, USA.","DOI":"10.1007\/3-540-44629-X_11"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"113103","DOI":"10.1016\/j.eswa.2019.113103","article-title":"Boosting salp swarm algorithm by sine cosine algorithm and disrupt operator for feature selection","volume":"145","author":"Neggaz","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"125705","DOI":"10.1088\/0957-4484\/20\/12\/125705","article-title":"Nano-indentation studies on polymer matrix composites reinforced by few-layer graphene","volume":"20","author":"Das","year":"2009","journal-title":"Nanotechnology"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1016\/j.asoc.2018.11.012","article-title":"Adaptive multi-parent crossover GA for feature optimization in epileptic seizure identification","volume":"75","author":"Bimba","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Droste, S. (2003, January 9\u201311). Analysis of the (1 + 1) EA for a dynamically bitwise changing OneMax. Proceedings of the Genetic and Evolutionary Computation Conference, Chicago, IL, USA.","DOI":"10.1007\/3-540-45105-6_103"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Nakamura, R.Y.M., Pereira, L.A.M., Rodrigues, D., Costa, K.A.P., Papa, J.P., and Yang, X.-S. (2013). Binary bat algorithm for feature selection. Swarm Intelligence and Bio-Inspired Computation, Elsevier.","DOI":"10.1016\/B978-0-12-405163-8.00009-0"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"139792","DOI":"10.1109\/ACCESS.2021.3117853","article-title":"An improved binary grey-wolf optimizer with simulated annealing for feature selection","volume":"9","author":"Sallam","year":"2021","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1007\/s00521-013-1525-5","article-title":"Binary bat algorithm","volume":"25","author":"Mirjalili","year":"2014","journal-title":"Neural Comput. Appl."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","article-title":"The whale optimization algorithm","volume":"95","author":"Mirjalili","year":"2016","journal-title":"Adv. Eng. Softw."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.neucom.2019.07.026","article-title":"Evolving deep convolutional neural networks by quantum behaved particle swarm optimization with binary encoding for image classification","volume":"362","author":"Li","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"107608","DOI":"10.1016\/j.patcog.2020.107608","article-title":"Topological optimization of the densenet with pretrained-weights inheritance and genetic channel selection","volume":"109","author":"Fang","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Wang, B., Sun, Y., Xue, B., and Zhang, M. (2018, January 11\u201314). A hybrid differential evolution approach to designing deep convolutional neural networks for image classification. Proceedings of the Australasian Joint Conference on Artificial Intelligence, Wellington, New Zealand.","DOI":"10.1007\/978-3-030-03991-2_24"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1016\/j.procs.2018.05.196","article-title":"Automatic Construction of Generic Stop Words List for Hindi Text","volume":"132","author":"Rani","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1108\/eb046814","article-title":"An algorithm for suffix stripping","volume":"14","author":"Porter","year":"1980","journal-title":"Program"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1145\/267954.267957","article-title":"Corpus-based stemming using cooccurrence of word variants","volume":"16","author":"Xu","year":"1998","journal-title":"ACM Trans. Inf. Syst. (TOIS)"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1145\/601858.601867","article-title":"Foundations of statistical natural language processing","volume":"31","author":"Weikum","year":"2002","journal-title":"ACM SIGMOD Rec."},{"key":"ref_49","unstructured":"Porter, M.F. (2022, September 10). Snowball: A Language for Stemming Algorithms. Available online: http:\/\/snowball.tartarus.org\/texts\/introduction.html."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"113384","DOI":"10.1016\/j.eswa.2020.113384","article-title":"Enhanced attentive convolutional neural networks for sentence pair modeling","volume":"151","author":"Xu","year":"2020","journal-title":"Expert Syst. Appl."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/6\/4\/104\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:41:21Z","timestamp":1760143281000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/6\/4\/104"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,28]]},"references-count":50,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["bdcc6040104"],"URL":"https:\/\/doi.org\/10.3390\/bdcc6040104","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,28]]}}}