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Optimal Allocation of Urban Water Resources Based on Multi-Objective Nutcracker optimization algorithm

WATER 2024年第23期16卷 3475页

作者： Wang, Dong Zhang, Dandan Liu, Zhen Li, Lei Liu, Xin Hebei Univ Engn Sch Water Conservancy & Hydroelect Power Handan 056038 Peoples R China Hebei Univ Engn Hebei Key Lab Intelligent Water Conservancy Handan 056038 Peoples R China Hebei Univ Engn Sch Informat & Elect Engn Handan 056038 Peoples R China Hebei Univ Engn Sch Mat Sci & Engn Handan 056038 Peoples R China

The imbalance between water supply and demand (WSD) has been growing noticeable as a result of the economy's fast expansion which can be effectively alleviated using optimal allocation of water resources. An urban water resources allocation (WRA) model based on the innovative Multi-Objective Nutcracker optimization algorithm (MONOA) is proposed in this study. Taking into account economic, social and ecological benefits, a comprehensive multi-objective optimization (MOO) model is established. By introducing the opposite learning strategy, non-dominated sorting approach and crowding distance mechanism to a recently reported intelligent optimization algorithm called the Nutcracker optimization algorithm (NOA), the novel nature-inspired metaheuristic algorithm MONOA is proposed to solve the multi-objective optimization model. The MONOA is evaluated on ten benchmark test functions, and it exhibits superior distribution and convergence by comparing with some highly cited algorithms. The proposed model is applied to Handan, China, in order to obtain a reasonable water allocation scheme in the planning year. The simulation results reveal that the economic benefit is in the range CNY [1.36, 1.44] x 1011, water shortage is in the range [0.66, 0.98] x 108 m3 and COD emission is in the range [3.70, 3.91] x 104 t in all the obtained Pareto solutions. The water resources management departments might create customized water allocation plans by balancing different goals and taking preferences into account. Moreover, the proposed method is a general approach that can be applied to many other cities. Hence, it is of great significance to the sustainable development and utilization of urban water resources.

关键词： water resources allocation multi-objective optimization intelligent optimization algorithm water supply water demand

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A simultaneous optimization of a flexible heat exchanger network under uncertain conditions

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APPLIED THERMAL ENGINEERING 2021年第Part2期183卷 116230-116230页

作者： Wang, Shihao Tian, Yitong Li, Shaojun East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

Heat exchanger networks are important for energy recovery and cost reduction. Flexible networks can handle environmental disturbances, and is more practical for industrial processes than the conventional HEN design with fixed parameters. Previous research on flexible HEN synthesis has mainly applied sequential synthesis, in which the structure determination and area optimization are conducted separately, or other multi-period methods in which only a few extreme operating conditions are selected;however, these methods are not sufficiently accurate for solving nonconvex problems. In this study, a bi-level simultaneous optimization strategy is proposed for the synthesis of flexible HENs under the nominal and all the extreme operating conditions. A binary particle swarm optimization is used in the outer layer to optimize the structural variables, and an Alopex-based evolutionary algorithm is used in the inner layer to determine the heat exchanger areas. Via this method, the result of a convex problem can be directly obtained. Furthermore, to deal with nonconvex problems, a flexibility analysis is conducted to adjust the heat exchanger areas on each critical point. The results of two cases with lower total annual costs than those reported previously are included to demonstrate the efficiency of the proposed method.

关键词： Flexible heat exchanger network Simultaneous synthesis Uncertain fluctuations intelligent optimization algorithm

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Adaptive Audio Steganography Based on Improved Syndrome-Trellis Codes

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IEEE ACCESS 2021年 9卷 11705-11715页

作者： Ying, Kaiyu Wang, Rangding Lin, Yuzhen Yan, Diqun Ningbo Univ Coll Informat Sci & Engn Ningbo 315211 Peoples R China

Syndrome-Trellis Code (STC) is a near-optimal convolutional method for adaptive steganography. Hitherto, the existing adaptive steganography commonly depends on the carefully designed distortion cost function, which controls the embedding position of the message in the cover signal. From another point of view, we implement adaptive steganography by improving the STC coding process (named Adaptive-STC). The parity-check matrix is the key to the encoding and extraction process of STC. In this work, we prove that the average embedding change probability of corresponding elements can be changed by adjusting its submatrix. Following that, we specially design an adaptive parity-check matrix to replace the designed distortion cost to restrict the embedding position. The generation of the adaptive parity-check matrix can be formulated as a multi-constrained integer programming problem in which the width of the submatrix is allocated at a fixed height. To solve this particular problem, we propose a targeted intelligent optimization algorithm (named GOAS) that can adaptively generate the parity-check matrix according to different audio cover. The experimental results show that the proposed method outperforms the state-of-the-art adaptive steganography with reduced embedding changes and improved audio quality while ensuring the ability against steganalysis.

关键词： Distortion Encoding Sparse matrices Generative adversarial networks Decoding Payloads Media Steganography syndrome-trellis codes parity-check matrix intelligent optimization algorithm

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Colorimetric sensor array based on metal porphyrin Dye-Modified mesoporous silica nanospheres for quantitative detection of aflatoxin B1 in wheat

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MICROCHEMICAL JOURNAL 2024年 207卷

作者： Ge, Menghan Deng, Jihong Jiang, Hui Chen, Quansheng Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Peoples R China Jimei Univ Coll Ocean Food & Biol Engn Xiamen 361021 Peoples R China

This study developed a novel colorimetric sensor array (CSA) based on mesoporous silica nanospheres (MSNs) for the quantitative detection of aflatoxin B1 (AFB1) in wheat. First, 12 kinds of nano-composite color-sensitive materials (Nano-CSMs) were prepared based on MSNs combined with metal porphyrin dyes, and their colorsensitive materials (CSMs) were modified. The volatile gas information of wheat samples with different degrees of mold was collected using two different sensor arrays, and the characteristic maps were visualized. Next, the genetic algorithm (GA) and particle swarm optimization (PSO) were used to screen the optimal characteristic components, and a support vector regression (SVR) model was established to achieve rapid quantitative detection of AFB1. The optimization results show that the SVR model based on Nano-CSA performed best, with a correlation coefficient (Rp) of 0.9754. The results show that the colorimetric sensor array modified by MSNSs has higher sensitivity and efficiency in the detection of AFB1 in wheat.

关键词： Colorimetric sensor array Mesoporous silica nanospheres intelligent optimization algorithm Aflatoxin B1 Support vector machine regression

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Rapid Determination of Hemicellulose Content in Corn Stalks by Near-infrared Spectroscopy Based on Dung Beetle Optimizer

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Agricultural Biotechnology 2024年第5期13卷 83-85,92页

作者： Baihong TONG Jinming LIU Jianfei SHI College of Information and Electrical Engineering Heilongjiang Bayi Agricultural UniversityDaqing 163000China

Corn stalks are a kind of common organic fertilizer and feed material in agriculture in China,as well as an important source of modern biomass energy and new *** is an important component in corn stalks,and it is very important to determine its content in corn *** this paper,the feasibility of near-infrared spectroscopy(NIRS)combined with chemometrics for rapid detection of hemicellulose content in corn stalks was *** order to improve the accuracy of NIRS detection,a new intelligent optimization algorithm,dung beetle optimizer(DBO),was applied to select characteristic wavelengths of *** modeling performance was compared with that based on characteristic wavelength selection using genetic algorithm(GA)and binary particle swarm optimization(BPSO),and it was found that the characteristic wavelength selection performance of DBO was excellent,and the regression accuracy of hemicellulose quantitative detection model established by its preferred characteristic wavelengths was better than the above two intelligent optimization algorithms.

关键词： Hemicellulose Near-infrared spectrum Characteristic wavelength selection intelligent optimization algorithm Dung beetle algorithm

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An improved particle swarm optimization algorithm with adaptive weighted delay velocity

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SYSTEMS SCIENCE & CONTROL ENGINEERING 2021年第1期9卷 188-197页

作者： Xu, Lin Song, Baoye Cao, Maoyong Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao Peoples R China

An improved particle swarm optimization (PSO) with adaptive weighted delay velocity (PSO-AWDV) is proposed in this paper. A new scheme blending weighted delay velocity is firstly presented for a new PSO with weighted delay velocity (PSO-WDV) algorithm. Then, to adaptively update the velocity inertia weight, an adaptive PSO-AWDV algorithm is developed based on the evolutionary state of the particle swarm evaluated via a new estimation method. The newly proposed adaptive PSO-AWDV algorithm is tested based on some famous benchmark functions, which can confirm that the performance of PSO-AWDV is superior to several well-known PSO variants and intelligent optimization algorithms in literature.

关键词： PSO particle swarm optimization adaptive weighted delay velocity intelligent optimization algorithm

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Forecasting carbon price in China using a novel hybrid model based on secondary decomposition, multi-complexity and error correction

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JOURNAL OF CLEANER PRODUCTION 2023年第1期401卷

作者： Yang, Hong Yang, Xiaodie Li, Guohui Xian Univ Posts & Telecommun Sch Elect Engn Xian 710121 Shaanxi Peoples R China

As global warming intensifies, the reduction of carbon emissions is imminent. Carbon price is directly related to whether carbon can be effectively reduced. Therefore, accurately forecasting carbon price has important prac-tical significance. Aiming at the nonstationary and nonlinear characteristics of carbon price, this paper proposes a novel hybrid model for forecasting carbon price, which is based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), multiscale fuzzy entropy (MFE), complete ensemble empirical mode decomposition (CEEMD), improved random forest by salp swarm algorithm (SSARF), improved back propagation by cuckoo search (CSBP), improved extreme learning machine by whale optimization algo-rithm (WOAELM) and error correction (EC), named ICEEMDAN-MFE-CEEMD-SSARF-CSBP-WOAELM-EC. Firstly, carbon price is decomposed by ICEEMDAN, divided into high-, medium-, and low-complexity components by MFE. Secondly, high-complexity components are merged and secondarily decomposed by CEEMD, which are still recorded as high-complexity components. Then, SSARF, CSBP and WOAELM are used to forecast high-, medium-, and low-complexity components, respectively, and forecasting results are reconstructed. Finally, EC is carried out using an extreme learning machine to obtain the final forecasting results, and the Diebold-Mariano test is introduced for a comprehensive evaluation of the model. Taking carbon price in the pilot cities of Shenzhen and Hubei as examples, after 6 aspects and 20 comparative experiments, the results show that the proposed model has higher forecast accuracy, with MAPE, MAE and RMSE up to 0.03131, 0.00089 and 4.02e-06 in Hubei, and its forecasting ability is better than other commonly used international carbon financial price forecasting models, providing a theoretical and data basis for carbon pricing and formulating carbon reduction policies in China. The main contributions of this paper are the improved primary dec

关键词： Carbon price Forecasting Hybrid framework Mode decomposition intelligent optimization algorithm Error correction

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Prediction and design of high hardness high entropy alloy through machine learning

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MATERIALS & DESIGN 2023年 235卷

作者： Ren, Wei Zhang, Yi-Fan Wang, Wei-Li Ding, Shu-Jian Li, Nan Northwestern Polytech Univ Xian 710072 Peoples R China Xian Univ Posts & Telecommun Sch Sci Xian 710121 Peoples R China

Two data-driven machine learning (ML) models were proposed for the hardness prediction of high-entropy alloys (HEA) and the composition optimization of high hardness HEAs, respectively. The hardness prediction model combined interpretable ML methods with solid solution strengthening theory, and the R2 and RMSE values of 0.9716 and 39.2525 were respectively achieved under the leave-one-out validation method. The optimization model adopted an intelligent optimization algorithm to design the optimized elemental molar ratios of high hardness HEAs and was experimentally verified. A general design framework was summarized for prediction and composition optimization of various HEA performances.

关键词： High-entropy alloy Machine learning intelligent optimization algorithm Interpretable machine learning Solid solution strengthening

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Research on Wind Turbine Fault Detection Based on the Fusion of ASL-CatBoost and TtRSA

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SENSORS 2023年第15期23卷 6741-6741页

作者： Kong, Lingchao Liang, Hongtao Liu, Guozhu Liu, Shuo Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Peoples R China

The internal structure of wind turbines is intricate and precise, although the challenging working conditions often give rise to various operational faults. This study aims to address the limitations of traditional machine learning algorithms in wind turbine fault detection and the imbalance of positive and negative samples in the fault detection dataset. To achieve the real-time detection of wind turbine group faults and to capture wind turbine fault state information, an enhanced ASL-CatBoost algorithm is proposed. Additionally, a crawling animal search algorithm that incorporates the Tent chaotic mapping and t-distribution mutation strategy is introduced to assess the sensitivity of the ASL-CatBoost algorithm toward hyperparameters and the difficulty of manual hyperparameter setting. The effectiveness of the proposed hyperparameter optimization strategy, termed the TtRSA algorithm, is demonstrated through a comparison of traditional intelligent optimization algorithms using 11 benchmark test functions. When applied to the hyperparameter optimization of the ASL-CatBoost algorithm, the TtRSA-ASL-CatBoost algorithm exhibits notable enhancements in accuracy, recall, and other performance measures compared with the ASL-CatBoost algorithm and other ensemble learning algorithms. The experimental results affirm that the proposed algorithm model improvement strategy effectively enhances the wind turbine fault detection classification recognition rate.

关键词： wind turbine CatBoost algorithm fault detection category imbalance intelligent optimization algorithm

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A novel mutual aid Salp Swarm algorithm for global optimization

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第17期35卷

作者： Zhang, Huanlong Feng, Yuxing Huang, Wanwei Zhang, Jie Zhang, Jianwei Zhengzhou Univ Light Ind Coll Elect & Informat Engn Zhengzhou 450002 Peoples R China Zhengzhou Univ Light Ind Coll Software Zhengzhou Peoples R China

Salp Swarm algorithm is a new intelligent optimization algorithm. Because of it is fewer control parameters and convenient operation, it has attracted the attention of researchers from all circles. However, due to the lack of complex iterative process, it has some disadvantages, such as low optimization precision and poor population diversity in the late iteration. To solve these problems of Salp Swarm algorithm, we proposed a Salp Swarm algorithm based on mutual learning mechanism. In this article, the improved Salp Swarm algorithm uses the iteration factor of tangent change to update the population position, which balances the global exploration and local development ability of the algorithm. At the same time, the introduction of mutual learning mechanism in the local development stage solves the problem of poor population diversity in the later iteration of Salp Swarm algorithm, and improves the convergence accuracy of the algorithm. Finally, 23 classical and CEC2014 benchmark functions are used to evaluate the effectiveness of the proposed algorithm. The experimental results show that the improved Salp Swarm algorithm has better optimization accuracy and stability compared with the algorithm of Salp Swarm, Moth Flame optimization, Grasshopper optimization, and Ant Lion optimization.

关键词： intelligent optimization algorithm mutual learning mechanism Salp Swarm algorithm tangent function

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