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Study on site selection of municipal solid waste incineration plant based on swarm optimization algorithm

WASTE MANAGEMENT & RESEARCH 2022年第2期40卷 205-217页

作者： Jiang, Shan Li, Zhigang Gao, Chuang Univ Sci & Technol Liaoning Sch Elect & Informat Engn 185Qianshan Middle Rd Anshan 114051 Liaoning Peoples R China Shandong Key Lab Intelligent Bldg Technol Jinan Shandong Peoples R China

In this paper, based on an improved swarm optimization algorithm, a new site selection model of a municipal solid waste (MSW) incineration plant is proposed. First, the whale optimization algorithm and particle swarm optimization algorithm are combined according to certain rules to improve the performance of the hybrid algorithm. Through a verification of the single and multi-peak functions, the results show that the algorithm achieves a good performance. The location model of the MSW incineration plant is based on many factors, including the economy, environmental protection, population scale, and operation cost. Finally, based on a sample analysis, a new location model of an MSW incineration plant is used to select the location of an Anshan MSW incineration plant, and a reasonable location is obtained.

关键词： Garbage collection MSW incineration plant site selection model swarm optimization algorithm particle swarm optimization whale optimization algorithm

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Double-sided friction stir welding of AA 2024-T6 joints: Mathematical modeling and optimization

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CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY 2022年 36卷 1-11页

作者： Nosrati, Hasan Ghaforian Yazdani, Navid Moshtaghi Khoran, Mohammad Esfarayen Univ Technol Dept Mech Engn Esfarayen North Khorasan Iran Univ Tehran Dept Mech Engn 16th Azar StEnghelab Sq Tehran Iran

The heat-treatable AA 2024-T6 has been used widely in the aerospace and defense industry. In this paper, the effective parameters of the double-sided friction stir welding (DFSW) process of AA 2024-T6 were investigated by using Response Surface Methodology (RSM). Also, the optimal parameters have been obtained by using the Artificial Neural Network (ANN) and optimization algorithms. The most significant process parameters, i.e. rotational speed, welding speed, tilt angle, and tool pin length, were considered to predict the behavior of the response, i.e. ultimate tensile strength (UTS) and percentage elongation (% El). For this purpose, a central composite design, as well as analysis of variance, was used to investigate effective parameters. Also, an intelligent connection between the above parameters and responses was developed by using ANN. The optimal neural network was integrated with the whale optimization algorithm (WOA) to find optimal parameters and achieve desirable responses. The obtained result was verified by experiment. Results indicated that the tool pin length (h) factor has the highest contribution percentage between the parameters (14.95% and 9.59% for UTS and %El, respectively). Besides, the parameters of welding speed (v) and tilt angle (a) have a significant effect on the UTS and %El of DFSW joints, respectively. Using a rotational speed of 1250 rpm, welding speed of 80 mm/min, the tilt angle of 4 degrees, and a tool pin length of 2.3 mm, as optimal parameters of the DFSW process, can be achieved the maximum of ultimate tensile strength and elongation. (C) 2021 CIRP.

关键词： Double-sided friction stir welding Response surface methodology Artificial neural network Particle swarm optimization whale optimization algorithm

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Lung Lesion Classification Scheme Using optimization Techniques and Hybrid (KNN-SVM) Classifier

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IETE JOURNAL OF RESEARCH 2022年第2期68卷 1485-1499页

作者： Rani, K. Vijila Jawhar, S. Joseph Anna Univ Informat & Commun Engn Chennai Tamil Nadu India Arunachala Coll Engn Women Nagercoil Tamil Nadu India

This paper proposes an intellectual classification system to classify normal and abnormal CT scan lung images. Prediction of cancer cells from lung image is such a difficult task for physicians and researchers. A novel technique for lung tumour detection with Grey Wolf Optimized & whale optimization algorithm - Support Vector Machine (GWO & WOA-SVM) is proposed. CT scan images are used as input images. Investigations focused on the segmentation and classification part to find the lung lesion region. For finding the lung abnormality and lesion region in the lung, four different stages are used. The first stage is the image acquisition process. The second stage is the image pre-processing and enhancement stage, here Advanced Clustering (AC) technique is used. The next stage is the segmentation process, here the advanced surface normal overlap (ASNO) lung segmentation algorithm is used. Finally, the lung lesion is classified using the hybrid classifier followed by a different optimization technique. The research gap for this paper is to find the lung lesion using an optimization-based hybrid classification technique. Nowadays, classification decision and treatment of lung tumours are based on symptoms and radiological appearance. Here Hybrid Classifier (KNN-SVM) is used to classify 700 images;it is observed from the results that the Hybrid classifier KNN-SVM demonstrated the highest classification accuracy rate of 97.6% among other methods.

关键词： Lung tumour Advanced Clustering Segmentation process ASNO Hybrid KNN-SVM Classifier Grey Wolf Optimized whale optimization algorithm

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An Enhanced whale algorithm for Three-Dimensional Path Planning for Meteorological Detection of the Unmanned Aerial Vehicle in Complex Environments

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IEEE ACCESS 2024年 12卷 60039-60057页

作者： Yin, Shaotian Yang, Jie Ma, Li Fu, Manyu Xu, Ke Pukyong Natl Univ Dept Ind Design Busan 48513 South Korea Shanxi Meteorol Informat Ctr Taiyuan 030006 Peoples R China

The paper proposes a path planning method for meteorological detection of the unmanned aerial vehicle (UAV) in complex obstacle environments based on an enhanced multi-strategy whale optimization algorithm. In the design of the modeling, a weight coefficient method and coordinate system-based single-objective path planning model are developed to solve the UAV's flight path. It has the shortest trajectory, minimum threat, flight altitude, flight angle, and obstacles as constraints. In the algorithm design, quasi-opposition-based learning, real-time boundary processing, and enhanced search mechanisms are introduced into the basic whale optimization algorithm. This increases population diversity and improves the algorithm's global optimization ability and convergence performance. Through theoretical analysis, it has been proven that the time complexity of the improved algorithm is the same as the basic whale optimization algorithm, and the convergence of the improved algorithm has been theoretically analyzed. The multi-dimensional and multi-algorithm extreme value optimization of the CEC2017 test function, as well as the optimization solution to the three-dimensional path planning problem for meteorological detection of the unmanned aerial vehicle in four complex scenarios, have been carried out. The experimental results clearly show that, considering both the maneuverability and turning performance of the UAV, the planned path can safely and efficiently avoid hazards. Compared with other algorithms, in complex environments, the path planning scheme obtained from the path planning model can, to some extent, alleviate the impact of increasing the number of trajectory points on solving performance. The coverage of the path planning method for the improved algorithm is 100%, and the success rate of path planning is increased by about 50%. The improved algorithm has excellent optimization ability and generates a higher-quality path.

关键词： UAV path planning weight coefficient method flight angle whale optimization algorithm

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A Sequential-Multi-Decision Scheme for WiFi Localization Using Vision-Based Refinement

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第3期23卷 2321-2336页

作者： Tang, Chenjun Sun, Wei Zhang, Xing Zheng, Jin Sun, Jian Liu, Chongpei Hunan Univ Coll Elect & Informat Engn Natl Engn Res Ctr Robot Visual Percept & Control T Changsha 410012 Hunan Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha 410012 Hunan Peoples R China Hunan Univ Shenzhen Res Inst Changsha 410012 Hunan Peoples R China Cent South Univ Sch Architecture & Art Changsha 410012 Peoples R China

Currently, most mobile devices have WIFI and camera modules to locate their position. However, there are two main challenges in large, highly similar indoor environments (localization accuracy and localization time). Aiming to balance these problems, we propose a sequential-multi-decision integrated system that combines WIFI and vision to acquire users' locations. This system has two phases: sequential fusion localization and adaptive multi-decision fusion localization. The former employs WIFI-based localization first, then image-based localization and fusion localization are used within the constraints of WIFI-based localization. In the WIFI-based localization phase, the gaussian process regression (GPR) model is used to construct a WIFI indoor map. Subsequently, we propose to apply the hybrid whale optimization algorithm (HWOA) to WIFI-based localization to improve its accuracy and stability. The latter uses an adaptive multi-decision fusion mechanism that integrates WIFI-based localization, image-based localization, and fusion localization to obtain the users' location finally. The experiments show the effectiveness of HWOA applied to WIFI-based localization. We also experimentally evaluate the proposed fusion algorithm with other state-of-the-art fusion algorithms (e.g., accuracy and time) in a real environment (an area larger than 10,000$m<^>algorithm$m2). The experimental results show that the proposed fusion system is competitive.

关键词： Image indoor localization WIFI whale optimization algorithm

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A novel sensorless control method for SRMs based on gain-optimized SMO

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ELECTRICAL ENGINEERING 2024年第2期106卷 2011-2019页

作者： Ge, Lefei Zhang, Dongpeng Huang, Jiale Fu, Zhaoyang Song, Shoujun Northwestern Polytech Univ Xian 710072 Peoples R China

To achieve high precision and robust control of switched reluctance machines (SRMs), this paper proposes a novel rotor position estimation method based on gain-optimized sliding mode observer (SMO) with neural networks and whale optimization algorithm. For the SMO, this paper uses the easily measured phase current to constitute the error function of the SMO, which requires less pre-stored data. Using the neural networks' powerful nonlinear mapping capability, the relationship between the sliding mode gains and speed estimation error and the position estimation error is obtained, and then the whale optimization algorithm is used to find the optimal sliding mode gains in the restricted range. The simulation and experiment results show that the accuracy of the SMO control system is significantly improved.

关键词： Switched reluctance machine Sensorless control Neural networks whale optimization algorithm Sliding mode observer

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A Health Assessment Method for Lithium-Ion Batteries Based on Evidence Reasoning Rules with Dynamic Reference Values

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BATTERIES-BASEL 2024年第1期10卷 26页

作者： Yang, Zijiang Zhao, Xiaofeng Zhang, Hongquan Heilongjiang Univ Heilongjiang Prov Key Lab Micronano Sensit Devices Harbin 150080 Peoples R China Heilongjiang Agr Engn Vocat Coll Sch Intelligent Engn Harbin 150088 Peoples R China Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China

The health assessment of lithium-ion batteries holds great research significance in various areas such as battery management systems, battery usage and maintenance, and battery economic evaluation. However, because environmental perturbations are not taken into account during the assessment, the accuracy and reliability of the assessment are limited. Thus, a health assessment model for lithium-ion batteries based on evidence reasoning rules with dynamic reference value (ER-DRV) is proposed in this paper. Firstly, considering that the data are subject to changes, dynamic reference values, real-time weights, and real-time reliability were utilized in the model to ensure the effectiveness and accuracy of the assessment. Moreover, an enhanced optimization method based on the whale optimization algorithm (WOA) was developed to improve the accuracy of the assessment model. In addition, the robustness of the ER-DRV model was studied with perturbation analysis methods. Finally, the proposed method was validated on two open lithium-ion battery datasets. The experimental results show that the health assessment method proposed in this article not only has higher accuracy and transparent reasoning process but also has strong robustness and good generalization ability.

关键词： health assessment lithium-ion battery evidence reasoning rule whale optimization algorithm

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A novel multiple kernel extreme learning machine model for remaining useful life prediction of lithium-ion batteries

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JOURNAL OF POWER SOURCES 2024年 613卷

作者： Sun, Haohan Jiang, Haobin Gu, Zhengjian Li, Huanhuan Wang, Tiansi Rao, Weining Wang, Yaping Pei, Lei Yuan, Chaochun Chen, Long Jiangsu Univ Automot Engn Res Inst 301 Xuefu Rd Zhenjiang 212013 Peoples R China Wuxi Inst Inspect Testing & Certificat 8 Chunxin East Rd Wuxi 214125 Peoples R China Jiangsu Univ Sch Mat Sci & Engn Zhenjiang 212013 Peoples R China

The safety and decommissioning of lithium-ion batteries (LIBs) are closely related to the remaining useful life (RUL). Accurate life prediction of LIBs can prevent safety issues and recycling at the end of life. This paper proposes a method based on the multiple kernel extreme learning machine (MKELM) to improve the stability and accuracy of LIBs life prediction. According to the measured cyclic aging data of the 18650 battery, in-depth analysis of charging curves, extract the equal voltage rise charging time, the constant voltage charging time and the equal time voltage drop in the static state as the health factors, using MKELM to predict the remaining cycle life of LIBs, and using the whale algorithm to optimize the nuclear parameters of MKELM. The proposed method is simulated and verified by the actual LIBs data. When the training set is the first 50% of the data, the whale optimization algorithm-MKELM (WOA-MKELM) algorithm predicts the average error cycle number of the three batteries is 15.6 cycles. After adding 10% of the training set, the average number of forecast error cycles reduced to 5 cycles, and the results show that the method has high stability and accuracy.

关键词： Lithium-ion battery Remaining useful life Health factor Multiple kernel extreme learning machine whale optimization algorithm

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Residual life prediction of lithium battery based on hybrid model of WOA-VMD and attention mechanism

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INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES 2024年第19期19卷 798-806页

作者： Tao, Qiong Lv, Jie Wu, Jia Wuxi Inst Technol Sch Control Technol 1600 Gaolang West Rd Wuxi 214121 Jiangsu Peoples R China

In order to improve the long-term reliability of lithium battery and ensure the safe operation of the system, a forecasting method combining whale optimization algorithm (WOA), variational mode decomposition (VMD) and the Gated Recurrent Unit model with Attention mechanism (GRU-Attention) was proposed. WOA-VMD was used to decompose the battery capacity data into the intrinsic mode component and residual component, which were, respectively, predicted by GRU-Attention, and finally effectively integrated to obtain accurate capacity prediction results. Through the experiments of the Li-ion battery data set of the Advanced Life Cycle Engineering Center of the University of Maryland, the proposed Li-ion battery combination prediction model has high prediction accuracy and stability. This method is a novel combination of advanced technologies and algorithms, and its effectiveness in improving the long-term reliability of lithium batteries has been verified by experiments. This research is of great significance for promoting the development and application of lithium battery technology.

关键词： lithium battery remaining service life whale optimization algorithm attention mechanism gated recurrent unit

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Chaotic oppositional-based whale optimization to train a feed forward neural network

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SOFT COMPUTING 2022年第22期26卷 12421-12443页

作者： Chatterjee, Rajesh Mukherjee, Ranapratap Roy, Provas Kumar Pradhan, Dinesh Kumar Usha Martin Univ Block OffRanchi Purulia Rd Angara 835103 Jharkhand India Kalyani Govt Engn Coll Kalyani W Bengal India Dr BC Roy Engn Coll Durgapur W Bengal India

The feed forward neural network (FNN) has a significant breakthrough in recent times in solving different real-life complex problems. The success of FNN is primarily attributed to its architecture, the optimization technique used, and the tuning of hyper-parameters to identify different patterns in data. This study tries to find an optimization technique that will play a vital role in an FNN to get better results. whale optimization algorithm (WOA) is one of the popular nature-inspired optimization techniques used to solve real-time sciences and engineering problems. A new optimization approach namely chaotic oppositional-based whale optimization algorithm (COWOA) is developed in this paper, for the first time, to discuss the FNN problems of four different dataset by integrating chaotic function and oppositional-based learning with WOA. Simulation results are presented to validate the success of the suggested COWOA approach and demonstrate its dominance over basic WOA, chaotic WOA (CWOA), particle swarm optimization (PSO), Adam optimization algorithm (AOA), and stochastic gradient descent (SGD) for both unimodal and multimodal dataset problems. The comparative analysis with other optimization techniques validates its superiority and robustness on four standard dataset of FNN problem. Furthermore, to validate the performance of the proposed algorithm, a statistical analysis is performed. The outcome of the statistical results validates the superiority and acceptability of COWOA over other optimization methods.

关键词： Chaos theory Feed forward neural network whale optimization algorithm Artificial neural network Oppositional-based learning

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