{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T22:52:47Z","timestamp":1780527167661,"version":"3.54.1"},"reference-count":50,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T00:00:00Z","timestamp":1673222400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"111 Project of China","award":["D21009"],"award-info":[{"award-number":["D21009"]}]},{"name":"111 Project of China","award":["20210102"],"award-info":[{"award-number":["20210102"]}]},{"name":"State Key Laboratory Fund Project of China","award":["D21009"],"award-info":[{"award-number":["D21009"]}]},{"name":"State Key Laboratory Fund Project of China","award":["20210102"],"award-info":[{"award-number":["20210102"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The Aquila Optimizer (AO) is a new bio-inspired meta-heuristic algorithm inspired by Aquila\u2019s hunting behavior. Adaptive Aquila Optimizer Combining Niche Thought with Dispersed Chaotic Swarm (NCAAO) is proposed to address the problem that although the Aquila Optimizer (AO) has a strong global exploration capability, it has an insufficient local exploitation capability and a slow convergence rate. First, to improve the diversity of populations in the algorithm and the uniformity of distribution in the search space, DLCS chaotic mapping is used to generate the initial populations so that the algorithm is in a better exploration state. Then, to improve the search accuracy of the algorithm, an adaptive adjustment strategy of de-searching preferences is proposed. The exploration and development phases of the NCAAO algorithm are effectively balanced by changing the search threshold and introducing the position weight parameter to adaptively adjust the search process. Finally, the idea of small habitats is effectively used to promote the exchange of information between groups and accelerate the rapid convergence of groups to the optimal solution. To verify the optimization performance of the NCAAO algorithm, the improved algorithm was tested on 15 standard benchmark functions, the Wilcoxon rank sum test, and engineering optimization problems to test the optimization-seeking ability of the improved algorithm. The experimental results show that the NCAAO algorithm has better search performance and faster convergence speed compared with other intelligent algorithms.<\/jats:p>","DOI":"10.3390\/s23020755","type":"journal-article","created":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T07:55:39Z","timestamp":1673250939000},"page":"755","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Adaptive Aquila Optimizer Combining Niche Thought with Dispersed Chaotic Swarm"],"prefix":"10.3390","volume":"23","author":[{"given":"Yue","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Opto-Electronic Engineering, Changchun University of Science and Technology, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiping","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Opto-Electronic Engineering, Changchun University of Science and Technology, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Opto-Electronic Engineering, Changchun University of Science and Technology, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kailin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Opto-Electronic Engineering, Changchun University of Science and Technology, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weida","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Opto-Electronic Engineering, Changchun University of Science and Technology, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Opto-Electronic Engineering, Changchun University of Science and Technology, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6617","DOI":"10.1007\/s00500-018-3310-y","article-title":"An improved hybrid grey wolf optimization algorithm","volume":"23","author":"Teng","year":"2019","journal-title":"Soft Comput."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Neumann, F., and Witt, C. (2013, January 6\u201310). Bioinspired computation in combinatorial optimization: Algorithms and their computational complexity. Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation, Amsterdam, The Netherlands.","DOI":"10.1145\/2464576.2466738"},{"key":"ref_3","first-page":"2779","article-title":"AGV path planning based on improved grey wolf optimization algorithm and its implementation prototype platform","volume":"24","author":"Liu","year":"2018","journal-title":"Comput. Integr. Manuf. Syst."},{"key":"ref_4","first-page":"36","article-title":"Summary of the application of swarm intelligence algorithms in image segmentation","volume":"57","author":"Shi","year":"2021","journal-title":"Comput. Eng. Appl."},{"key":"ref_5","first-page":"1","article-title":"Application of improved equilibrium optimizer algorithm to constrained optimization problems","volume":"9","author":"Li","year":"2021","journal-title":"J. Front. Comput. Sci. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1016\/j.asoc.2017.05.016","article-title":"Integrated civilian\u2013military pre-positioning of emergency supplies: A multi-objective optimization approach","volume":"58","author":"Zheng","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.compag.2016.06.001","article-title":"Optimized algorithm of sensor node deployment for intelligent agricultural monitoring","volume":"127","author":"Zou","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1016\/j.proeng.2014.11.498","article-title":"Optimization of drinking water and sewer hydraulic management: Coupling of a genetic algorithm and two network hydraulic tools","volume":"89","author":"Mandel","year":"2014","journal-title":"Procedia Eng."},{"key":"ref_9","unstructured":"Kennedy, J., and Eberhart, R. (December, January 7). Particle swarm optimization. Proceedings of the ICNN\u201995-International Conference on Neural Networks, Perth, WA, Australia."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1038\/scientificamerican0792-66","article-title":"Genetic algorithms","volume":"267","author":"Holland","year":"1992","journal-title":"Sci. Am."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6915","DOI":"10.4249\/scholarpedia.6915","article-title":"Artificial bee colony algorithm","volume":"5","author":"Karaboga","year":"2010","journal-title":"Scholarpedia"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"115904","DOI":"10.1016\/j.eswa.2021.115904","article-title":"Heuristic recommendation technique in Internet of Things featuring swarm intelligence approach","volume":"187","author":"Forestiero","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"107250","DOI":"10.1016\/j.cie.2021.107250","article-title":"Aquila optimizer: A novel meta-heuristic optimization algorithm","volume":"157","author":"Abualigah","year":"2021","journal-title":"Comput. Ind. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, S., Jia, H., Abualigah, L., Liu, Q., and Zheng, R. (2021). An improved hybrid aquila optimizer and harris hawks algorithm for solving industrial engineering optimization problems. Processes, 9.","DOI":"10.3390\/pr9091551"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"89153","DOI":"10.1109\/ACCESS.2022.3200386","article-title":"Chaotic Mapping Based Advanced Aquila Optimizer with Single Stage Evolutionary Algorithm","volume":"10","author":"Verma","year":"2022","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Akyol, S. (2022). A new hybrid method based on Aquila optimizer and tangent search algorithm for global optimization. J. Ambient. Intell. Humaniz. Comput., 1\u201321.","DOI":"10.1007\/s12652-022-04347-1"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4863","DOI":"10.1007\/s00500-022-06873-8","article-title":"Hybrid Aquila optimizer with arithmetic optimization algorithm for global optimization tasks","volume":"26","author":"Mahajan","year":"2022","journal-title":"Soft Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"10907","DOI":"10.1109\/ACCESS.2022.3144431","article-title":"AOAAO: The hybrid algorithm of arithmetic optimization algorithm with aquila optimizer","volume":"10","author":"Zhang","year":"2022","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"AlRassas, A.M., Al-qaness, M.A., Ewees, A.A., Ren, S., Abd Elaziz, M., Dama\u0161evi\u010dius, R., and Krilavi\u010dius, T. (2021). Optimized ANFIS model using Aquila Optimizer for oil production forecasting. Processes, 9.","DOI":"10.3390\/pr9071194"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Abd Elaziz, M., Dahou, A., Alsaleh, N.A., Elsheikh, A.H., Saba, A.I., and Ahmadein, M. (2021). Boosting COVID-19 image classification using MobileNetV3 and aquila optimizer algorithm. Entropy, 23.","DOI":"10.3390\/e23111383"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"100399","DOI":"10.1016\/j.rineng.2022.100399","article-title":"A hybrid chaotic-based discrete wavelet transform and Aquila optimisation tuned-artificial neural network approach for wind speed prediction","volume":"14","author":"Jnr","year":"2022","journal-title":"Results Eng."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ma, L., Li, J., and Zhao, Y. (2021). Population Forecast of China\u2019s Rural Community Based on CFANGBM and Improved Aquila Optimizer Algorithm. Fractal Fract., 5.","DOI":"10.3390\/fractalfract5040190"},{"key":"ref_23","first-page":"808","article-title":"Optimization of PID parameters for controlling DC motor based on the aquila optimizer algorithm","volume":"13","author":"Aribowo","year":"2022","journal-title":"Int. J. Power Electron. Drive Syst. (IJPEDS)"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ali, M.H., Salawudeen, A.T., Kamel, S., Salau, H.B., Habil, M., and Shouran, M. (2022). Single-and multi-objective modified aquila optimizer for optimal multiple renewable energy resources in distribution network. Mathematics, 10.","DOI":"10.3390\/math10122129"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yao, J., Sha, Y., Chen, Y., Zhang, G., Hu, X., Bai, G., and Liu, J. (2022). IHSSAO: An Improved Hybrid Salp Swarm Algorithm and Aquila Optimizer for UAV Path Planning in Complex Terrain. Appl. Sci., 12.","DOI":"10.3390\/app12115634"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Alkayem, N.F., Shen, L., Al-hababi, T., Qian, X., and Cao, M. (2022). Inverse Analysis of Structural Damage Based on the Modal Kinetic and Strain Energies with the Novel Oppositional Unified Particle Swarm Gradient-Based Optimizer. Appl. Sci., 12.","DOI":"10.3390\/app122211689"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"108919","DOI":"10.1016\/j.asoc.2022.108919","article-title":"The combined social engineering particle swarm optimization for real-world engineering problems: A case study of model-based structural health monitoring","volume":"123","author":"Alkayem","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"112952","DOI":"10.1016\/j.cam.2020.112952","article-title":"On the construction of one-dimensional discrete chaos theory based on the improved version of Marotto\u2019s theorem","volume":"380","author":"Li","year":"2020","journal-title":"J. Comput. Appl. Math."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"109851","DOI":"10.1016\/j.chaos.2020.109851","article-title":"Signal separation in an aggregation of chaotic signals","volume":"138","author":"Jafari","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.1007\/s10462-019-09707-6","article-title":"A novel chaotic selfish herd optimizer for global optimization and feature selection","volume":"53","author":"Anand","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref_31","first-page":"2359","article-title":"Divided chaotic oscillatory annealing TSP optimization algorithm based on greedy strategy","volume":"38","author":"Lin","year":"2021","journal-title":"Appl. Res. Comput."},{"key":"ref_32","first-page":"720","article-title":"Piecewise Logistic Chaotic Map and Its Performance Analysis","volume":"37","author":"Fan","year":"2009","journal-title":"Acta Electron. Sin."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, C., Di, Y., Tang, J., Shuai, J., Zhang, Y., and Lu, Q. (2021). The Dynamic Analysis of a Novel Reconfigurable Cubic Chaotic Map and Its Application in Finite Field. Symmetry, 13.","DOI":"10.3390\/sym13081420"},{"key":"ref_34","first-page":"857","article-title":"Research on decision-makings of structure optimization based on improved Tent PSO","volume":"8","author":"Zhang","year":"2008","journal-title":"Control Decis."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, K., and Mao, W. (2021, January 24\u201326). Simulation of Vertical Temperature Distribution in Green Building Space Based on the Niche Genetic Algorithm. Proceedings of the 2021 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI), Harbin, China.","DOI":"10.1109\/IAAI54625.2021.9699944"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/4235.771163","article-title":"Evolutionary programming made faster","volume":"3","author":"Yao","year":"1999","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1080\/00207160108805080","article-title":"On benchmarking functions for genetic algorithms","volume":"77","author":"Digalakis","year":"2001","journal-title":"Int. J. Comput. Math."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","article-title":"Grey wolf optimizer","volume":"69","author":"Mirjalili","year":"2014","journal-title":"Adv. Eng. Softw."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1016\/j.future.2019.02.028","article-title":"Harris hawks optimization: Algorithm and applications","volume":"97","author":"Heidari","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.advengsoft.2015.01.010","article-title":"The ant lion optimizer","volume":"83","author":"Mirjalili","year":"2015","journal-title":"Adv. Eng. Softw."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"113917","DOI":"10.1016\/j.eswa.2020.113917","article-title":"An improved grey wolf optimizer for solving engineering problems","volume":"166","author":"Taghian","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.swevo.2011.02.002","article-title":"A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms","volume":"1","author":"Derrac","year":"2011","journal-title":"Swarm Evol. Comput."},{"key":"ref_44","first-page":"2035","article-title":"A grey wolf optimization algorithm based on Cubic mapping and its application","volume":"43","author":"Zhang","year":"2021","journal-title":"Comput. Eng. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Kannan, B., and Kramer, S.N. (1993, January 19\u201322). An augmented Lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design. Proceedings of the International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Albuquerque, NM, USA.","DOI":"10.1115\/DETC1993-0382"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.advengsoft.2017.01.004","article-title":"Grasshopper optimization algorithm: Theory and application","volume":"105","author":"Saremi","year":"2017","journal-title":"Adv. Eng. Softw."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.advengsoft.2017.07.002","article-title":"Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems","volume":"114","author":"Mirjalili","year":"2017","journal-title":"Adv. Eng. Softw."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.knosys.2015.07.006","article-title":"Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm","volume":"89","author":"Mirjalili","year":"2015","journal-title":"Knowl.-Based Syst."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2232","DOI":"10.1016\/j.ins.2009.03.004","article-title":"GSA: A gravitational search algorithm","volume":"179","author":"Rashedi","year":"2009","journal-title":"Inf. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"103541","DOI":"10.1016\/j.engappai.2020.103541","article-title":"Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization","volume":"90","author":"Kaur","year":"2020","journal-title":"Eng. Appl. Artif. Intell."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/755\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:04:37Z","timestamp":1760119477000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/755"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,9]]},"references-count":50,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23020755"],"URL":"https:\/\/doi.org\/10.3390\/s23020755","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,9]]}}}