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optimization of β-cyclodextrin based ultrasonic-assisted extraction of apigenin and luteolin from celery seed using support vector regression and different nature-inspired optimization algorithms

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CHEMICAL ENGINEERING COMMUNICATIONS 2024年第6期211卷 920-936页

作者： Hang, Nguyen Thu My, Than Thi Kieu Thom, Bui Thi Nghia, Truong Minh Van, Phuong Nguyen Hanoi Univ Pharm Fac Pharmacognosy & Tradit Med Dept Pharmacognosy Hanoi Vietnam

This study aimed to extract apigenin and luteolin from celery seeds using 2-hydroxypropyl-beta-cyclodextrin (HP-beta-CD) based ultrasonic-assisted technique. Optimal extraction conditions were determined through the integration of support vector regression combined with various optimization algorithms, including Genetic Algorithm, Particle Swarm optimization, Gray Wolf Optimizer, Bat Algorithm, Firefly Algorithm, and Cuckoo Search Algorithm. The identified optimal conditions included an extraction time of 52 min, an HP-beta-CD concentration of 24 mg/mL, and a solvent-to-solid ratio of 24 mL/g. Under these conditions, the predicted apigenin and luteolin content in the extract were 0.156 and 0.814 mg/g, respectively, closely aligning with the actual values of 0.158 +/- 0.005 mg/g and 0.807 +/- 0.008 mg/g. Notably, the HP-beta-CD/water extract exhibited significantly higher concentrations of active ingredients and antioxidant activity compared to the water extract, with the content of apigenin and luteolin in the water extract being only 25 and 45%, respectively, compared to the HP-beta-CD/water extract. Regarding the antioxidant effect, the water extract displayed a 50% scavenging capacity (SC50) of 331.29 +/- 20.5 g/mL, whereas the SC50 for the HP-beta-CD/water extract was 89.04 +/- 4.70 g/mL. Molecular modeling and IR spectra analysis also suggested the formation of inclusion complexes between HP-beta-CD and apigenin/luteolin. These findings suggested that HP-beta-CD could be a preferable alternative to conventional solvents due to its advantages, such as easier control, safety, and cost-effectiveness. In addition, the use of support vector regression and nature-inspired optimization algorithms could be promising alternatives to traditional response surface methodology.

关键词： Celery seed cyclodextrin optimization algorithms support vector regression apigenin luteolin

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A probabilistic metric for comparing metaheuristic optimization algorithms

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STRUCTURAL SAFETY 2018年 70卷 59-70页

作者： Gomes, Wellison J. S. Beck, Andre T. Lopez, Rafael H. Miguel, Leandro F. F. Univ Fed Santa Catarina CORE Dept Civil Engn Florianopolis SC Brazil Univ Sao Paulo Dept Struct Engn Sao Carlos SP Brazil

The evolution of metaheuristic optimization algorithms towards identification of a global minimum is based on random numbers, making each run unique. Comparing the performance of different algorithms hence requires several runs, and some statistical metric of the results. Mean, standard deviation, best and worst values metrics have been used with this purpose. In this paper, a single probabilistic metric is proposed for comparing metaheuristic optimization algorithms. It is based on the idea of population interference, and yields the probability that a given algorithm produces a smaller (global?) minimum than an alternative algorithm, in a single run. Three benchmark example problems and four optimization algorithms are employed to demonstrate that the proposed metric is better than usual statistics such as mean, standard deviation, best and worst values obtained over several runs. The proposed metric actually quantifies how much better a given algorithm is, in comparison to an alternative algorithm. Statements about the superiority of an algorithm can also be made in consideration of the number of algorithm runs and the number of objective function evaluations allowed in each run. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： optimization algorithms Metaheuristic Evolutionary algorithms Performance metric Population interference

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The Challenge for the Nature-Inspired Global optimization algorithms: Non-Symmetric Benchmark Functions

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IEEE ACCESS 2021年 9卷 106317-106339页

作者： Gao, Zheng-Ming Zhao, Juan Hu, Yu-Rong Chen, Hua-Feng Jingchu Univ Technol Sch Comp Engn Jingmen 448000 Peoples R China Jingchu Univ Technol Sch Elect & Informat Engn Jingmen 448000 Peoples R China Jingchu Univ Technol Dept Sci & Technol Jingmen 448000 Peoples R China

Along with the increasing number of nature-inspired algorithms, more and more benchmark functions were also involved in the initial verification experiments. The benchmark functions were introduced to verify the capability of algorithms in optimization, but not all of them could be optimized, because they were different from each other in dimensionality, separability, scalability, and modality ***.. In this paper, we introduced another property called symmetry or non-symmetry, which should be another embedded characteristic of functions affecting the capability of algorithms in optimization. 67 non-symmetric benchmark functions were collected and 9 popular capability-verified algorithms were introduced in four types of simulation experiments. Experimental results show that most of the non-symmetric algorithms could not be optimized. And none of the algorithms involved could optimize them all. Efforts remain in need of new methods and improvements of nature-inspired algorithms.

关键词： Benchmark testing optimization Classification algorithms Genetic algorithms Scalability Rabbits Mathematical model Nature-inspired algorithm optimization algorithms benchmark functions non-symmetry

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A comparative study of social group optimization with a few recent optimization algorithms

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COMPLEX & INTELLIGENT SYSTEMS 2021年第1期7卷 249-295页

作者： Naik, Anima Satapathy, Suresh Chandra KL Univ Dept CSE Hyderabad Telangana India KIIT Deemed Be Univ Sch Comp Engn Bhubaneswar Odisha India

From the past few decades, the popularity of meta-heuristic optimization algorithms is growing compared to deterministic search optimization algorithms in solving global optimization problems. This has led to the development of several optimization algorithms to solve complex optimization problems. But none of the algorithms can solve all optimization problems equally well. As a result, the researchers focus on either improving exiting meta-heuristic optimization algorithms or introducing new algorithms. The social group optimization (SGO) Algorithm is a meta-heuristic optimization algorithm that was proposed in the year 2016 for solving global optimization problems. In the literature, SGO is shown to perform well as compared to other optimization algorithms. This paper attempts to compare the performance of the SGO algorithm with other optimization algorithms proposed between 2017 and 2019. These algorithms are tested through several experiments, including multiple classical benchmark functions, CEC special session functions, and six classical engineering problems etc. optimization results prove that the SGO algorithm is extremely competitive as compared to other algorithms.

关键词： Meta-heuristic Benchmark functions optimization algorithms Fitness evaluations

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Prediction of Vibration Velocity Generated in Mine Blasting Using Support Vector Regression Improved by optimization algorithms

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NATURAL RESOURCES RESEARCH 2020年第2期29卷 807-830页

作者： Yang, Haiqing Rad, Hima Nikafshan Hasanipanah, Mahdi Amnieh, Hassan Bakhshandeh Nekouie, Atefeh Chongqing Univ Sch Civil Engn Chongqing Peoples R China Tabari Univ Babol Coll Comp Sci Babol Sar Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Univ Tehran Coll Engn Sch Min Tehran 111554563 Iran Islamic Azad Univ Dept Comp Engn Mashhad Branch Mashhad Razavi Khorasan Iran

Ground vibration generated from blasting is a detrimental side effect of the use of explosives to break the rock mass in mines. Therefore, accurately predicting ground vibration is a practical need, especially for safety issues. This research proposes hybrid artificial intelligence schemes for predicting ground vibration. The approaches are based on support vector regression (SVR) optimized with firefly algorithm (FFA), genetic algorithm (GA), and particle swarm optimization (PSO). Additionally, a hybrid FFA and artificial neural network (ANN) model and several well-known empirical models were also employed in this study. In the predictive modeling process, 90 sets of data, collected from two quarry mines in Iran, divided into two datasets, namely a training dataset and a testing dataset, were used. After model development, to provide an objective assessment of the predictive model performances, their results were compared based on several well-known and popular statistical criteria. FFA-SVR exhibits much more efficiency and reliability than PSO-SVR, GA-SVR, FFA-ANN models in terms of ground vibration prediction, indicating the superiority of FFA over PSO and GA in the SVR training.

关键词： Blasting Ground vibration Support vector regression optimization algorithms

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Hybrid Metaheuristic optimization algorithms with Least-Squares Support Vector Machine and Boosted Regression Tree Models for Prediction of Air-Blast Due to Mine Blasting

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NATURAL RESOURCES RESEARCH 2024年第3期33卷 1349-1363页

作者： Ding, Xiaohua Hasanipanah, Mahdi Ulrikh, Dmitrii Vladimirovich China Univ Min & Technol Sch Mines Xuzhou 221116 Peoples R China China Univ Min & Technol State Key Lab Coal Resources & Safe Min Xuzhou 221116 Peoples R China Duy Tan Univ Inst Res & Dev Da Nang Vietnam Duy Tan Univ Sch Engn & Technol Da Nang Vietnam South Ural State Univ Inst Architecture & Construct Dept Urban Planning Engn Networks & Syst 76 Lenin Prospect Chelyabinsk 454080 Russia St Petersburg State Univ Architecture & Civil Engn Dept Water Use & Environm 42nd Krasnoarmeiskaya Str St Petersburg 190005 Russia

After each blasting operation in surface mines, undesirable environmental impacts, such as air-blast (AB) and ground vibration, are inevitable. Therefore, minimizing and controlling these impacts are crucial in order to reduce environmental problems. This study presents new and practical advanced machine learning methods for AB prediction using 62 datasets gathered from four quarry sites in Malaysia. The developed models were constructed based on the boosted regression tree (BRT) and least-squares support vector machine (LSSVM), improved with three metaheuristic algorithms: the gray wolf optimizer (GWO), genetic algorithm (GA), and artificial bee colony (ABC). Six hybrid models, namely BRT-GA, BRT-ABC, BRT-GWO, LSSVM-GA, LSSVM-ABC, and LSSVM-GWO models, were developed and their performances were evaluated using metrics such as R-squared correlation and other methods like the Taylor diagram and quantile-quantile plots. To provide a better assessment of the models' performances, the dataset were categorized into training and testing parts. The results demonstrated that, among the six hybrid models, the LSSVM-GWO model provided the highest efficiency in the testing part while the BRT-GWO model had the best accuracy but the performance of BRT-GWO was the best in the training part. In other words, the BRT hybrid models had the best performance in training, and LSSVM hybrid models in the testing part. The results indicate the effectiveness of combining GWO with LSSVM and BRT models to predict AB.

关键词： Blast-induced air-blast LSSVM BRT optimization algorithms

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Application of machine learning, deep learning and optimization algorithms in geoengineering and geoscience: Comprehensive review and future challenge

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GONDWANA RESEARCH 2022年 109卷 1-17页

作者： Zhang, Wengang Gu, Xin Tang, Libin Yin, Yueping Liu, Dongsheng Zhang, Yanmei Chongqing Univ Coll Aerosp Engn Chongqing 400044 Peoples R China Chongqing Univ Key Lab New Technol Construction Cities Mt Area Chongqing 400045 Peoples R China Chongqing Univ Sch Civil Engn Chongqing 400045 Peoples R China China Inst Geol Environm Monitoring Beijing 100081 Peoples R China Chongqing Bur Geol Survey & Minerals Explorat Chongqing 401121 Peoples R China

The so-called Fourth Paradigm has witnessed a boom during the past two decades, with large volumes of observational data becoming available to scientists and engineers. Big data is characterized by the rule of the five Vs: Volume, Variety, Value, Velocity and Veracity. The concept of big data naturally matches well with the features of geoengineering and geoscience. Large-scale, comprehensive, multidirectional and multifield geotechnical data analysis is becoming a trend. On the other hand, Machine learning (ML), Deep Learning (DL) and optimization Algorithm (OA) provide the ability to learn from data and deliver in-depth insight into geotechnical problems. Researchers use different ML, DL and OA models to solve various problems associated with geoengineering and geoscience. Consequently, there is a need to extend its research with big data research through integrating the use of ML, DL and OA techniques. This work focuses on a systematic review on the state-of-the-art application of ML, DL and OA algo-rithms in geoengineering and geoscience. Various ML, DL, and OA approaches are firstly concisely intro-duced, concerning mainly the supervised learning, unsupervised learning, deep learning and optimization algorithms. Then their representative applications in the geoengineering and geoscience are summarized via VOSviewer demonstration. The authors also provided their own thoughts learnt from these applica-tions as well as work ongoing and future recommendations. This review paper aims to make a compre-hensive summary and provide fundamental guidelines for researchers and engineers in the discipline of geoengineering and geoscience or similar research areas on how to integrate and apply ML, DL and OA methods.(c) 2022 International Association for Gondwana Research. Published by Elsevier B.V. All rights reserved.

关键词： Machine learning Deep learning optimization algorithms Geoengineering and geoscience VOSviewer

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In-Plane Flexible Ring Tire Model Parameter Identification: optimization algorithms

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SAE INTERNATIONAL JOURNAL OF VEHICLE DYNAMICS STABILITY AND NVH 2018年第1期2卷 71-87页

作者： Li, Bin Yang, Xiaobo Yang, James Texas Tech Univ Lubbock TX 79409 USA Oshkosh Corp Oshkosh WI USA

Parameter identification is an important part of tire model development. The prediction performance of a tire model highly depends on the identified parameter values of the tire model. Different optimization algorithms may yield different tire parameters with different computational accuracy. It is essential to find out which optimization algorithm is most likely to generate a set of parameters with the best prediction performance. In this study, four different MATLAB (R) optimization algorithms, including fminsearchcon, patternsearch, genetic algorithm (GA), and particles warm, are used to identify the parameters of a newly proposed in-plane flexible ring tire model. The reference data used for parameter identification are obtained through a ADAMS FTire (R) virtual cleat test. After parameters are identified based on above four algorithms, their performances are compared in terms of effectiveness, efficiency, reliability, and robustness. Once the best optimization algorithm for the proposed tire model is determined, this optimization algorithm is used to test different types of cost functions to determine which cost function is the best choice for tire model parameter identification. The study in this article provides some important insights for the tire model parameter identification.

关键词： Flexible ring tire model parameter identification optimization algorithms vehicle dynamics cleat tests

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Use of optimization algorithms to prioritize protected areas in Mazandaran Province of Iran

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JOURNAL FOR NATURE CONSERVATION 2014年第5期22卷 462-470页

作者： Mehri, Azade Salmanmahiny, Abdolrassoul Mirkarimi, Seyed Hamed Rezaei, Hamid Reza Gorgan Univ Agr Sci & Nat Resources Gorgan Golestan Iran

A systematic approach for prioritization of protected areas is the use of artificial intelligence. This approach employs computer algorithms based on an objective function to identify the best network of areas to be protected. Site selection algorithms are commonly used to identify areas of high conservation value. This study used three types of heuristic algorithms (simulated annealing, greedy, rarity) to prioritize areas for protection in Mazandaran Province of Iran using Marxan software. The goal was to select a conservation network with the smallest possible area in which maximum protection targets are achievable. The effects of spatial scale, algorithm, and zone compactness were also examined. We found that the existing network of protected areas is inadequate to achieve conservation targets. The algorithm results provided the best areas for supplementation of the current network. The simulated annealing algorithm provided the most plausible results for all scenarios. These results can be used to modify the existing boundaries of the protected areas network and introduce new sites for protection of plant and animal species. (C) 2014 Elsevier GmbH. All rights reserved.

关键词： optimization algorithms Simulated annealing Greedy Rarity Marxan Systematic conservation planning

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EXPERIMENTS TO TRACK STORMS USING MODERN optimization algorithms

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Journal of Tropical Meteorology 2010年第3期16卷 280-291页

作者：李南魏鸣 Key Laboratory of Meteorological Disaster of Ministry of Education Nanjing University of Information Science & Technology School of Atmospheric Physics Nanjing University of Information Science & Technology State Key Laboratory of Severe Weather Chinese Academy of Meteorological Sciences

Storm identification and tracking based on weather radar data are essential to nowcasting and severe weather warning. A new two-dimensional storm identification method simultaneously seeking in two directions is proposed, and identification results are used to discuss storm tracking algorithms. Three modern optimization algorithms （simulated annealing algorithm, genetic algorithm and ant colony algorithm） are tested to match storms in successive time intervals. Preliminary results indicate that the simulated annealing algorithm and ant colony algorithm are effective and have intuitionally adjustable parameters, whereas the genetic algorithm is unsatisfaetorily constrained by the mode of genetic operations Experiments provide not only the feasibility and characteristics of storm tracking with modern optimization algorithms, but also references for studies and applications in relevant fields.

关键词： raw weather radar data storm identification and tracking optimization algorithms

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