{"title":"利用超参数调整元搜索法识别工业活性污泥模型","authors":"","doi":"10.1016/j.swevo.2024.101733","DOIUrl":null,"url":null,"abstract":"<div><p>This study focuses on the parameter estimation of an industrial activated sludge model using hyperparameter-tuned metaheuristic techniques. The data used in this study were collected on-site from a textile industry wastewater treatment plant. A Modified Activated Sludge Model (M-ASM) was the 'first-principle model’ selected and implemented with suitable assumptions. Advanced metaheuristic techniques, as Adaptive Tunicate Swarm Optimization (ATSO), Whale Optimization Algorithm (WOA), Rao-3 Optimization (Rao-3) and Driving Training Based Optimization (DTBO) were implemented. The hyperparameter tuning was performed with Bayesian Optimization (BO). Optimized metaheuristic algorithms were implemented for model-parameter identification. The Bayesian optimized Rao-3(BO-Rao-3) algorithm provided the best validation results, with a Mean Absolute Percentage Error (MAPE) value of 7.0141 and Normalized Root Mean Square Error (NRMSE) value of 0.2629. It also had the least execution time. BO-Rao-3 is 0.93% to 4.7% better than the other implemented hyperparameter-tuned metaheuristic techniques.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2000,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Industrial activated sludge model identification using hyperparameter-tuned metaheuristics\",\"authors\":\"\",\"doi\":\"10.1016/j.swevo.2024.101733\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This study focuses on the parameter estimation of an industrial activated sludge model using hyperparameter-tuned metaheuristic techniques. The data used in this study were collected on-site from a textile industry wastewater treatment plant. A Modified Activated Sludge Model (M-ASM) was the 'first-principle model’ selected and implemented with suitable assumptions. Advanced metaheuristic techniques, as Adaptive Tunicate Swarm Optimization (ATSO), Whale Optimization Algorithm (WOA), Rao-3 Optimization (Rao-3) and Driving Training Based Optimization (DTBO) were implemented. The hyperparameter tuning was performed with Bayesian Optimization (BO). Optimized metaheuristic algorithms were implemented for model-parameter identification. The Bayesian optimized Rao-3(BO-Rao-3) algorithm provided the best validation results, with a Mean Absolute Percentage Error (MAPE) value of 7.0141 and Normalized Root Mean Square Error (NRMSE) value of 0.2629. It also had the least execution time. BO-Rao-3 is 0.93% to 4.7% better than the other implemented hyperparameter-tuned metaheuristic techniques.</p></div>\",\"PeriodicalId\":48682,\"journal\":{\"name\":\"Swarm and Evolutionary Computation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":8.2000,\"publicationDate\":\"2024-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Swarm and Evolutionary Computation\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2210650224002712\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Swarm and Evolutionary Computation","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210650224002712","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Industrial activated sludge model identification using hyperparameter-tuned metaheuristics
This study focuses on the parameter estimation of an industrial activated sludge model using hyperparameter-tuned metaheuristic techniques. The data used in this study were collected on-site from a textile industry wastewater treatment plant. A Modified Activated Sludge Model (M-ASM) was the 'first-principle model’ selected and implemented with suitable assumptions. Advanced metaheuristic techniques, as Adaptive Tunicate Swarm Optimization (ATSO), Whale Optimization Algorithm (WOA), Rao-3 Optimization (Rao-3) and Driving Training Based Optimization (DTBO) were implemented. The hyperparameter tuning was performed with Bayesian Optimization (BO). Optimized metaheuristic algorithms were implemented for model-parameter identification. The Bayesian optimized Rao-3(BO-Rao-3) algorithm provided the best validation results, with a Mean Absolute Percentage Error (MAPE) value of 7.0141 and Normalized Root Mean Square Error (NRMSE) value of 0.2629. It also had the least execution time. BO-Rao-3 is 0.93% to 4.7% better than the other implemented hyperparameter-tuned metaheuristic techniques.
期刊介绍：
Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.
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