{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T02:58:50Z","timestamp":1755226730570,"version":"3.43.0"},"reference-count":35,"publisher":"IWA Publishing","license":[{"start":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T00:00:00Z","timestamp":1753142400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62241307"],"award-info":[{"award-number":["62241307"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["iwaponline.com"],"crossmark-restriction":true},"short-container-title":[],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n               <jats:p>To address the issues of limited exploration capability and premature convergence in the optimization process of the Blood-Sucking Leech Optimizer (BSLO) algorithm, we propose an Improved BSLO (IBSLO) algorithm. Initially, a directional leeches switching mechanism based on an inverted S-shaped nonlinear perceived distance to strike a balance between exploitative and exploratory capabilities of the algorithm. Subsequently, a dynamic perception signal was designed to simulate dynamic stimulus signals, guiding leeches to search and optimize more accurately. Finally, the memory sharing mechanism is incorporated to improve search efficiency and secure the global optimal solution of the algorithm. In addition, the IBSLO algorithm is assessed through 23 benchmark functions and the standard test set from CEC-2017, with its superiority confirmed by a detailed analysis of the algorithm's convergence. To further assess the efficacy of the IBSLO algorithm in addressing practical optimization challenges, it was utilized to enhance the predictive model for crucial water quality parameters within the wastewater treatment procedure. The IBSLO-Deep Belief Network model's prediction results demonstrated superior accuracy compared with other optimization strategies, further confirming the excellent performance of the IBSLO algorithm.<\/jats:p>","DOI":"10.2166\/wst.2025.096","type":"journal-article","created":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T15:42:45Z","timestamp":1753198965000},"update-policy":"https:\/\/doi.org\/10.2166\/iwapcrossmarkpolicypage","source":"Crossref","is-referenced-by-count":0,"title":["Optimized deep belief network based on an improved Blood-sucking Leech Optimizer algorithm for wastewater quality forecasting"],"prefix":"10.2166","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-7257-1996","authenticated-orcid":false,"given":"Yanping","family":"Yao","sequence":"first","affiliation":[{"name":"College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8137-0286","authenticated-orcid":false,"given":"Xianjun","family":"Du","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"945","published-online":{"date-parts":[[2025,7,22]]},"reference":[{"key":"2025081205521369100_WST-D-25-00175C1","unstructured":"Alex\n            J.\n          , BenedettiL., CoppJ., GernaeyK. V., JeppssonU., NopensI., PonsM. N., SteyerJ. P. & VanrolleghemP. (2008) Benchmark Simulation Model no. 1 (BSM1). IWA Publishing, London. Report by the IWA Taskgroup on benchmarking of control strategies for WWTPs, 1."},{"issue":"3","key":"2025081205521369100_WST-D-25-00175C2","doi-asserted-by":"crossref","first-page":"611","DOI":"10.3390\/electronics14030611","article-title":"Hawkfish optimization algorithm: a gender-bending approach for solving complex optimization problems","volume":"14","year":"2025","journal-title":"Electronics"},{"key":"2025081205521369100_WST-D-25-00175C3","unstructured":"Awad\n            N. H.\n          , AliM. Z., LiangJ. J., QuB., QuY. & SuganthanP. N. (2016) Problem Definitions and Evaluation Criteria for the CEC 2017 Special Session and Competition on Single Objective Bound Constrained Real-Parameter Numerical Optimization, Technical Report. Nanyang Technological University Press, Singapore. 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