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Aiming at the shortcoming that ALO has unbalanced exploration and development capability for some complex optimization problems, inspired by the particle swarm optimization (PSO), the updated position of antlions in elitism operator of ALO is improved, and thus the improved ALO (IALO) is obtained. The proposed IALO is compared against sine cosine algorithm (SCA), PSO, Moth\u2010flame optimization algorithm (MFO), multi\u2010verse optimizer (MVO), and ALO by performing on 23 classic benchmark functions. The experimental results show that the proposed IALO outperforms SCA, PSO, MFO, MVO, and ALO according to the average values and the convergence speeds. And the proposed IALO is tested to optimize the parameters of BP neural network for predicting the Chinese influenza and the predicted model is built, written as IALO\u2010BPNN, which is against the models: BPNN, SCA\u2010BPNN, PSO\u2010BPNN, MFO\u2010BPNN, MVO\u2010BPNN, and ALO\u2010BPNN. It is shown that the predicted model IALO\u2010BPNN has smaller errors than other six predicted models, which illustrates that the IALO has potentiality to optimize the weights and basis of BP neural network for predicting the Chinese influenza effectively. Therefore, the proposed IALO is an effective and efficient algorithm suitable for optimization problems.<\/jats:p>","DOI":"10.1155\/2019\/1480392","type":"journal-article","created":{"date-parts":[[2019,8,5]],"date-time":"2019-08-05T23:30:35Z","timestamp":1565047835000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["The Improved Antlion Optimizer and Artificial Neural Network for Chinese Influenza Prediction"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9708-9141","authenticated-orcid":false,"given":"Hongping","family":"Hu","sequence":"first","affiliation":[]},{"given":"Yangyang","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2043-8363","authenticated-orcid":false,"given":"Yanping","family":"Bai","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5081-1256","authenticated-orcid":false,"given":"Juping","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Maoxing","family":"Liu","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2019,8,5]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/1090.001.0001"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1142\/2792"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511804441"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.3901\/CJME.2012.05.990"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2016.04.005"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compstruc.2015.11.014"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.juro.2015.09.090"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2017.04.030"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.1038\/scientificamerican0792-66"},{"key":"e_1_2_10_10_2","doi-asserted-by":"crossref","unstructured":"EberhartR.andKennedyJ. 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