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An optimisation approach to road sanitation workforce planning using differential evolution

JOURNAL OF URBAN MANAGEMENT 2020年第4期9卷 398-407页

作者： Ighravwe, Desmond Eseoghene Oke, Sunday Ayoola Aikhuele, Daniel Ojo, Abiodun Bells Univ Technol Dept Mech & Biomed Engn Ota Nigeria Univ Lagos Dept Mech Engn Lagos Nigeria

At present, labour unions of waste disposal agencies and company management are at loggerheads, frequently turning out contradictory sanitation assessments. This reveals a shifting outlook of sanitation accomplishment that should be resolved. Unfortunately, there is scanty research on road sanitation and no study exists on how to determine the important workforce variables of these workers. To solve this research problem, a multi-objective optimisation model is developed and solved using the differential evolution model. The proposed model considered different constraints including workforce size, budgets, and service time. Three conflicting goals of maximization of cleanliness, maximization of workers' effectiveness and minimization of traffic obstruction were incorporated into the model and solved using practical data from a waste disposal agency in a developing country. A key result shows that the system's average workers' turnover rate is 0.2472 while the system's average service failure rate is 0.2518. For each location, the system requires an average of eight workers per period. The worker's average quality of work done is 0.8552. The outcome of the work revealed the feasibility of the model application. It was concluded that the model serves as a basis to evaluate road sanitation workers and may be used for budgetary purposes.

关键词： Road sanitation Maintenance workforce model differential evolution algorithm Multi-objective

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MISO Nonlinear Gauss-Hammerstein Model Identification of Ultrasonic Motor

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2021年第2期32卷 356-366页

作者： Huang Wenwen Shi Jingzhuo Henan Univ Sci & Technol Luoyang 471023 Peoples R China

A novel two-input nonlinear Hammerstein model of ultrasonic motor is proposed to provide the basis for the design and analysis of motor's multivariable control system. The static nonlinear part of the model is designed as Gauss function to describe the nonlinear characteristics of ultrasonic motor more accurately. differential evolution algorithm is used to identify the model parameters, and the algorithm is improved to ensure the correctness and validity of the modeling process. The debugging method of optimization algorithm parameter value is also proposed to ensure the robustness of the algorithm. The comparison between the experimental data and the calculated results of the model indicates that the established model has higher precision and the modeling method is effective.

关键词： Ultrasonic motor Identification Hammerstein model differential evolution algorithm

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Application of Multiple-Optimization Filtering algorithm in Remote Sensing Image Denoising

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SENSORS 2023年第18期23卷 7813-7813页

作者： Zhang, Xuelin Li, Yuan Feng, Xiang Hua, Jian Yue, Dong Wang, Jianxiong Yunnan Agr Univ Univ Res Ctr Agr Remote Sensing & Precis Agr Engn Sch Water Conservancy Kunming 650201 Peoples R China

Denoising remote sensing images is crucial in the application and research of remote sensing imagery. Noise in remote sensing images originates from sensor characteristics, signal transmission, and environmental conditions, among which Gaussian noise is the most common type. In this paper, we proposed a multiple-optimization bilateral filtering (MOBF) algorithm based on edge detection and differential evolution (DE) methods. The proposed algorithm optimizes the spatial domain filtering kernel and the spatial domain Gaussian kernel by using the standard deviation and width of the edge response. By employing the DE algorithm, the individuals in the population based on the standard deviation of the gray value domain are subjected to iterative mutation, crossover, and selection operations to refine the latent solution vectors and determine the optimal color space for optimizing the standard deviation of the pixel range domain kernel. As a result, the MOBF algorithm, which does not require any parameter input, is realized. To verify the feasibility and effectiveness of the proposed algorithm, denoising experiments were conducted on remote sensing images by using evaluation metrics such as the mean squared error, peak signal-to-noise ratio, and structural similarity index. The experimental results revealed that the MOBF algorithm outperforms traditional algorithms for all three evaluation metrics.

关键词： remote sensing imagery Gaussian noise edge detection operator differential evolution algorithm bilateral filtering

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Large-scale mobile users deployment optimization based on a two-stage hybrid global HS-DE algorithm in multi-UAV-enabled mobile edge computing

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第1期124卷

作者： Ouyang, Haibin Liu, Kang Zhang, Chunliang Li, Steven Gao, Liqun Guangzhou Univ Sch Mech & Elect Engn Guangzhou 510006 Peoples R China Guangzhou Univ Guangzhou Key Lab Condit Monitoring & Control Mech Guangzhou Peoples R China RMIT Univ Grad Sch Business & Law Melbourne 3000 Australia Northeastern Univ Coll Informat Sci & Engn Shenyang 110004 Peoples R China

Multi-UAV (unmanned aerial vehicle)-supported mobile edge computing system deploys multiple UAVs as flight edge clouds for large-scale users. In this system, how to optimize the deployment of UAVs is important for providing good service for all mobile users. Specifically, for each mobile user, we need to determine whether the task can be performed locally or on the drone (i.e., the unload decision) and how much resources should be allocated. In this paper, a new two-level optimization method is proposed for UAV deployment and task scheduling problem to minimize the system energy consumption. The proposed algorithm is named TSHGHSDE algorithm, in which a few features are designed to enhance the algorithm performance. Firstly, a two-stage optimization strategy is proposed based on HS-DE algorithm. In the early stage, HS algorithm is used for the local search in a limited space, and the DE algorithm is employed for the global search in the late stage. Secondly, we design an effective control parameter to adjust the search process, which aims to effectively combine the local search ability of HS algorithm and the global search ability of DE algorithm. Thirdly, a novel encoding mechanism is proposed in the process of the UAV deployment optimization. A global search strategy is designed to enhance the performance of the proposed algorithm. To assess the performance of the proposed algorithm, some well-known algorithms are used for comparison in experiments. The results demonstrate that the proposed algorithm performs better in terms of the search efficiency, search accuracy and stability.

关键词： Multi-UAV-enabled mobile edge computing differential evolution algorithm Harmony search algorithm Global search

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Quality classification of stored wheat based on evidence reasoning rule and stacking ensemble learning

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2023年 214卷

作者： Jiang, Huawei Zhang, Shulong Yang, Zhen Zhao, Like Zhou, Yan Zhou, Dexiang Henan Univ Technol Coll Informat Sci & Engn Zhengzhou 450001 Peoples R China

Classification of stored wheat quality is extremely important for reducing wheat storage losses and ensuring food security. However, the limitations of conventional single models and the uncertainties in the decision-making process have brought great challenges to the quality classification of stored wheat. To solve this problem, an accurate classification model for stored wheat quality based on Evidence Reasoning rule and Stacking ensemble learning (ER-Stacking) is proposed. Firstly, the base learners of the ensemble model are selected by the fusion measurement method of diversity and classification performance. Then, the importance weight and reliability factor of the evidence are obtained by the differential evolution (DE) algorithm and probability distance similarity, respectively. Finally, the ER rule is used to fuse the evidences optimized by weight and reliability to complete the identification and classification of stored wheat quality. In order to verify the validity of the method, experiments are conducted using the structured data on physiological and biochemical indicators of stored wheat. The experimental results show that the ER-Stacking ensemble model achieves 88.1%, 88.05%, 89.31% and 88.4% in the accuracy, precision, recall and f1-score, respectively, whose classification performance is significantly higher than that of the conventional single models. Compared with the models using other integration methods or different combination strategies of base learners, the proposed model also has obvious advantages, which can effectively improve the accuracy and reliability of the classification results of stored wheat quality.

关键词： Ensemble learning Evidential reasoning rule differential evolution algorithm Diversity measurement Classification of stored wheat quality

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Prediction model of dissolved oxygen in marine pasture based on hybrid gray wolf algorithm optimized support vector regression

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DESALINATION AND WATER TREATMENT 2021年 222卷 156-167页

作者： Yin, Baoan Wang, Rong Qi, Shengbo Yu, Jingdong Jiang, Wenliang Ocean Univ China Coll Engn Qingdao 266100 Peoples R China Sencott Intelligent Instrument Co Ltd Qingdao 266100 Peoples R China

Water quality prediction plays a vital role in water pollution warning and control. However, traditional prediction models usually suffer from low efficiency and poor robustness. To predict accurately the dissolved oxygen concentration in the marine pasture, a dissolved oxygen prediction model, based on wavelet analysis and hybrid gray wolf algorithm (HGWO) optimized vector regression, was established. Because of the water quality data in the marine pasture is a stationary time series. To improve the accuracy of water quality data, wavelet analysis was applied for data pre-processing in this paper. Besides, after the gray wolf algorithm (GWO) was optimized by the differential evolution algorithm (DE), it was used to optimize the support vector regression (SVR). Hence, the SVR's disadvantages of optimization ability and prediction accuracy both were improved. Back propagation neural network (BPNN), SVR, GWO-SVR, DE-SVR, and this model were, respectively, used to predict the dissolved oxygen concentration of Beidaihe marine pasture. The experimental results show that the mean square error, mean absolute error, and average percentage error of the model are 0.1658, 0.359, and 0.0305, respectively, which are better than the traditional prediction model. So this model has higher prediction accuracy and stronger generalization ability, and it can provide a reference for the precise regulation of aquaculture.

关键词： Wavelet analysis Gray wolf algorithm differential evolution algorithm Support vector regression Dissolved oxygen

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A Self-Adaptive Trajectory Optimization algorithm Using Fuzzy Logic for Mobile Edge Computing System Assisted by Unmanned Aerial Vehicle

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DRONES 2023年第4期7卷 266-266页

作者： Subburaj, Brindha Jayachandran, Uma Maheswari Arumugham, Vinothini Amalraj, Miruna Joe Amali Suthanthira Vellore Inst Technol Sch Comp Sci & Engn Chennai 600127 India KLN Coll Engn Dept Comp Sci & Engn Sivagangai 630612 India

The advancement of the Internet of Things (IoT) and the availability of wide cloud services have led to the horizon of edge computing paradigm which demands for processing the data at the edge of the network. The development of 5G technology has led to the increased usage of IoT-based devices and the generation of a large volume of data followed by increased data traffic, which is difficult to process by the mobile edge computing (MEC) platform. The latest inventions related to unmanned aerial vehicles (UAVs) helps to assist and replace the edge servers used for MEC. In the present work, the objective is to develop self-adaptive trajectory optimization algorithm (STO) which is a multi-objective optimization algorithm used to solve the vital objectives associated with the above scenario of a UAV-assisted MEC system. The objectives identified are minimizing the energy consumed by the MEC and minimizing the process emergency indicator, where the process emergency indicator implies the urgency level of a particular process. Finding the optimal values for these conflicting objectives will help to further efficiently apply UAV for MEC systems. A self-adaptive multi-objective differential evolution-based trajectory optimization algorithm (STO) is proposed, where a pool of trial vector generation strategies is extended. The strategies and the crossover rate associated with a differential evolution (DE) algorithm are self-adapted using fuzzy systems to improve the population diversity. The experimentation is planned to be conducted on hundreds of IoT device instances considered to be fixed on the ground level and to evaluate the performance of the proposed algorithm for a single unmanned aerial vehicle-assisted mobile edge computing system.

关键词： multi-objective optimization mobile edge computing unmanned aerial vehicle differential evolution algorithm

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Remote Sensing Inversion of Typical Offshore Water Quality Parameter Concentration Based on Improved SVR algorithm

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REMOTE SENSING 2023年第8期15卷 2104-2104页

作者： Ren, Jianghua Cui, Jianyong Dong, Wen Xiao, Yanfang Xu, Mingming Liu, Shanwei Wan, Jianhua Li, Zhongwei Zhang, Jie China Univ Petr Coll Oceanog & Space Informat Qingdao 266580 Peoples R China Minist Nat Resources Technol Innovat Ctr Maritime Silk Rd Marine Resour Qingdao 266580 Peoples R China Chinese Acad Sci Acad Aerosp Informat Innovat Beijing 100094 Peoples R China Minist Nat Resources Inst Oceanog 1 Remote Sensing Off Qingdao 266061 Peoples R China

Chlorophyll a concentration and suspended matter concentration, as typical water quality parameters related to spectral characteristics, are essential for characterizing the degree of eutrophication in water bodies. They have become crucial indicators for water quality assessment of inland water bodies. The support vector regression model (SVR) is suitable for small samples, has excellent generalization ability, and has high prediction accuracy. Still, it has the problem of difficult selection of model parameters and quickly falling into local extremes. To solve this problem, a hybrid differential evolution-Grey Wolf Optimizer (DE-GWO) algorithm is introduced into the parameter selection process of the support vector regression model, and an improved SVR algorithm (DE-GWO-SVR) is proposed for the remote sensing inversion of chlorophyll a concentration and suspended sediment concentration in water bodies. In this paper, the spectral reflectance of the water surface and the chlorophyll a and broken matter concentration values were obtained by field measurements in the Tangdao Bay waters of Qingdao, Shandong Province. The inverse model between the concentration values of the two water quality parameters and the corresponding sensitive factors was established by first determining the sensitive factors based on the response of the spectral reflectance to the two water quality parameters and introducing the DE-GWO optimization algorithm into the parameter selection process of the SVR model. Finally, the accuracy of the model was verified using Sentinel II satellite remote sensing spectral data, and then the inverse accuracy of the two water quality parameters was obtained. The mean relative error (MRE) of the chlorophyll a prediction model built by the DE-GWO algorithm optimizing the SVR is 25.1%, and the mean relative error (MRE) of the suspended matter prediction model is 32.5%. The inversion results were all better than the other models (linear regression, SVR, and GWO

关键词： chlorophyll a suspended matter SVR differential evolution algorithm grey wolf optimization algorithm

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A novel reversible fragile watermarking method in DWT domain for tamper localization and digital image authentication

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第1期84卷

作者： Azizoglu, Gokhan Toprak, Ahmet Nusret Cumhuriyet Univ Fac Engn Dept Comp Engn TR-58140 Sivas Turkiye Erciyes Univ Fac Engn Dept Comp Engn Melikgazi TR-38039 Kayseri Turkiye

Medical images are extremely valuable and sensitive data because of their significance in research, diagnosis, and treatment. However, the transmission of medical images over an unsecured network can lead to security issues such as modification, removal, copying, and unauthorized access. For this reason, the protection of medical information is of great importance. This paper proposes a novel reversible fragile digital watermarking method for tamper localization and authenticity and integrity protection in medical images. In the proposed method, the host image is firstly divided into 8x8 non-overlapped blocks. Then the image is transformed to the frequency domain by applying a Discrete Wavelet Transform, and the watermark information is embedded. Finally, the image is converted from the frequency domain to the spatial domain using an inverse Discrete Wavelet Transform. During this transformation, an intelligent optimization technique is used to correct the rounding errors and obtain the watermarked image with the highest level of imperceptibility. The experimental results demonstrate that the proposed method offers a watermarked image with high imperceptibility, extracts the host image lossless during the watermark extraction phase, and performs tamper localization with high precision.

关键词： Fragile watermarking Tamper detection Tamper localization Reversibility Discrete wavelet transform differential evolution algorithm

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A distributed subarray position optimization method based on DE-MIFT algorithm

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DIGITAL SIGNAL PROCESSING 2023年 143卷

作者： Qi, Keyan Jiang, Chaoshu Lin, Jie Deng, Xiaobo Hu, Qinzhen Chen, Fengfeng Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Sichuan Peoples R China Xihua Univ Sch Aeronaut & Astronaut Chengdu 610039 Sichuan Peoples R China Xihua Univ Engn Res Ctr Intelligent Air Ground Integrat Vehic Minist Educ Chengdu 610039 Sichuan Peoples R China China Leihua Elect Technol Res Inst Wuxi 214063 Jiangsu Peoples R China

Distributed array radar is a next generation radar that has been a hot research direction due to its high maneuverability, low cost, and high reliability. However, the synthesized beam pattern of the distributed array usually has some high sidelobes and grating lobes, which reduce the system performance. In this paper, a hybrid algorithm based on the differential evolution algorithm and modified iterative Fourier transform algorithm called DE-MIFT is proposed for optimizing subarray positions in a distributed array model. This distributed array model is based on a uniform linear subarray with equal amplitude excitation. Moreover, an integer coding method based on the minimum spacing between subarrays is adopted in the proposed algorithm to encode and initialize population, then the two algorithms are used to optimize in parallel. Then a variable selection factor is used to select dominant individuals from the parallel optimization results, and the subarray position design of distributed array is obtained through iterations. It is shown by simulation results that the proposed algorithm can effectively suppress system grating lobes, reduce sidelobe levels, and has powerful global search capability and fast convergence speed.

关键词： Distributed array Subarray position optimization Sidelobe and grating lobe suppression Improved iterative Fourier algorithm differential evolution algorithm

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