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State of health prediction based on multi-kernel relevance vector machine and whale optimization algorithm for lithium-ion battery

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2021年

作者： Wang, Shuai Zhang, Xiaochen Chen, Wengxiang Han, Wei Zhou, Shoubin Pecht, Michael China Univ Min & Technol Sch Elect & Power Engn Jiangsu Prov Engn Lab High Efficient Energy Stora Beijing Peoples R China Fujian Normal Univ Coll Math & Informat Digital Fujian Internet Of Things Lab Environm Mo Fuzhou 350117 Peoples R China Huafu High Technol Energy Storage Co Yangzhou Jiangsu Peoples R China Univ Maryland Ctr Adv Life Cycle Engn College Pk MD 20742 USA

The state of health (SOH) reflects the health status of the lithium-ion battery and is expected to accurately predicted, so as the corresponding maintenance measures can be taken to ensure the safe operation of the battery. This paper proposed a SOH prediction method based on multi-kernel relevance vector machine (RVM) and whale optimization algorithm (WOA). Firstly, the original features were obtained from the battery voltage and temperature data in charging and discharging phases. Secondly, the minimal-redundancy-maximal-relevance (mRMR) algorithm was introduced to select the optimal feature set. Then, the online model and offline model based on multi-kernel RVM and WOA were constructed. Finally, a hybrid model which combines the online model and offline model was proposed to prediction the SOH of the lithium-ion battery. The performance of the proposed method was evaluated with two kinds of data sets. The experimental results showed that the proposed method obtained higher prediction accuracy in both long-term and short-term periods than other methods.

关键词： State of health prediction relevance vector machine whale optimization algorithm health indicator lithium-ion battery

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Using the whale optimization algorithm to Solve the Optimal Reactive Power Dispatch Problem

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PROCESSES 2023年第5期11卷 1513页

作者： Zhang, Jinzhong Zhang, Tan Zhang, Gang Wang, Duansong Kong, Min West Anhui Univ Sch Elect & Optoelect Engn Luan 237012 Peoples R China

The optimal reactive power dispatch (ORPD) is a complex, optimal non-meritorious control issue with continuous and discontinuous control variables. This article exhibits a whale optimization algorithm (WOA) motivated by the whale's bubble-net hunting tactic to resolve ORPD. The intention is to comply with certain constraints to promote the voltage transmission quality by adequately altering the parameters. The WOA not only equalizes exploitation and exploration to maximize the overall performance and eliminate search stagnation but also has remarkable sustainability and robustness to accomplish superior convergence speed and computation accuracy. The WOA is contrasted with MFO, BA, GOA, GWO, MDWA, SMA, SPBO and SSA by diminishing the fitness value to highlight the superiority and stability. The experimental results reveal that WOA exhibits a superior convergence level and computation precision to accomplish the minimum active power loss and superior control variables.

关键词： optimal reactive dispatch whale optimization algorithm active power loss voltage transmission quality experimental results

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Indoor Robot Path Planning Using an Improved whale optimization algorithm

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SENSORS 2023年第8期23卷 3988-3988页

作者： Si, Qing Li, Changyong Xinjiang Univ Sch Mech Engn Urumqi 830039 Peoples R China

An improved whale optimization algorithm is proposed to solve the problems of the original algorithm in indoor robot path planning, which has slow convergence speed, poor path finding ability, low efficiency, and is easily prone to falling into the local shortest path problem. First, an improved logistic chaotic mapping is applied to enrich the initial population of whales and improve the global search capability of the algorithm. Second, a nonlinear convergence factor is introduced, and the equilibrium parameter A is changed to balance the global and local search capabilities of the algorithm and improve the search efficiency. Finally, the fused Corsi variance and weighting strategy perturbs the location of the whales to improve the path quality. The improved logical whale optimization algorithm (ILWOA) is compared with the WOA and four other improved whale optimization algorithms through eight test functions and three raster map environments for experiments. The results show that ILWOA has better convergence and merit-seeking ability in the test function. In the path planning experiments, the results are better than other algorithms when comparing three evaluation criteria, which verifies that the path quality, merit-seeking ability, and robustness of ILWOA in path planning are improved.

关键词： indoor robotics path planning whale optimization algorithm nonlinear convergence factor Corsi variance

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Reconfiguration of Distribution Networks with Simultaneous Allocation of Distributed Generation Using the whale optimization algorithm

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ENERGIES 2023年第12期16卷 4560页

作者： Mahdavi, Elham Asadpour, Seifollah Macedo, Leonardo H. Romero, Ruben Sao Paulo State Univ Dept Elect Engn Brasil Ave BR-15385000 Ilha Solteira Brazil Sao Paulo State Univ Dept Engn Barrageiros Ave BR-19274000 Rosana Brazil

The economic interest in power loss minimization and regulatory requirements regarding voltage levels in distribution systems are considered. In this paper, a computational technique to assist in the optimization of the power losses and voltage characteristic in the steady state through distribution network reconfiguration and the location and size of the distributed generators is addressed. The whale optimization algorithm (WOA) is chosen to perform this task since it can explore the sizeable combinatorial search space of the problem, which is also nonlinear and nonconvex. The purpose of this study is to mitigate power losses;voltage ranges are borne in mind as the problem restrictions. The proposals for solving the issue are evaluated using a specialized power flow algorithm. The algorithm is implemented in MATLAB and the 33-bus and 69-bus grids are employed to assess the performance of the approach. The results indicate that the WOA method outperforms regarding power loss reduction and voltage characteristic improvement in the concurrent integration of distribution network reconfiguration and distributed generators compared with the four metaheuristics shown in the results section.

关键词： distributed generation distribution network reconfiguration whale optimization algorithm

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Optimal placement and sizing of battery energy storage system for losses reduction using whale optimization algorithm

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JOURNAL OF ENERGY STORAGE 2019年 26卷

作者： Wong, Ling Ai Ramachandaramurthy, Vigna K. Walker, Sara L. Taylor, Phil Sanjari, Mohammad Javad Univ Tenaga Nas Coll Engn Inst Power Engn Dept Elect Power Engn Jalan IKRAM UNITEN Kajang 43000 Selangor Malaysia Univ Coll Technol Sarawak Sch Engn & Technol Sibu 96000 Sarawak Malaysia Newcastle Univ Sch Engn Newcastle Upon Tyne NE1 7RU Tyne & Wear England Griffith Univ Sch Engn Gold Coast Qld 4222 Australia

This paper proposes an approach for optimal placement and sizing of battery energy storage system (BESS) to reduce the power losses in the distribution grid. A meta-heuristic optimization algorithm known as whale optimization algorithm (WOA) is introduced to perform the optimization. In this paper, two different approaches are presented to achieve the optimal allocation of the BESS. The first approach is to obtain the optimal location and sizing in two steps while the second approach optimizes both location and sizing simultaneously. The performance of the proposed technique has been validated by comparing with two other algorithms namely firefly algorithm and particle swarm optimization. The results show that WOA has outstanding performance in attaining the optimal location and sizing of BESS in the distribution network for power losses reduction.

关键词： Meta-heuristic optimization algorithm whale optimization algorithm BESS

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Unified whale optimization algorithm based multi-kernel SVR ensemble learning for wind speed forecasting

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APPLIED SOFT COMPUTING 2022年 130卷

作者： Xian, Huafeng Che, Jinxing Nanchang Inst Technol Sch Informat Engn Nanchang Jiangxi Peoples R China Nanchang Inst Technol Sch Sci Nanchang 330099 Jiangxi Peoples R China Nanchang Inst Technol Jiangxi Prov Key Lab Water Informat Cooperat Sensi Nanchang Jiangxi Peoples R China

Support vector regression (SVR) is widely used in the field of wind speed forecasting because of its excellent nonlinear learning ability. However, the drawback of SVR is the model selection problem, which has the high complexity O(K x m3) including kernel function selection and parameter selection. To solve this problem, this paper proposes a multi-kernel SVR ensemble (MKSVRE) model based on unified optimization and whale optimization algorithm (WOA), where the MKSVRE model is used to solve the kernel function selection problem, and the unified optimization and the WOA are used to solve the parameter selection problem. The proposed model provides an alternative without the need to specifically select a kernel function and thus enhances the adaptability of SVR to diverse data. In addition, the unified optimization takes into account the interactions between models and achieves a global parameter selection. The proposed model is tested by simulations on wind speed data from Shandong Province, China. By comparing the prediction results of the proposed model, the single kernel SVR models, the models before and after optimization, and six other models, the effectiveness of the proposed model is confirmed.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Wind speed forecasting Support vector regression Model selection Unified optimization whale optimization algorithm

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An improved whale optimization algorithm based on multilevel threshold image segmentation using the Otsu method

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期113卷

作者： Ma, Guoyuan Yue, Xiaofeng Changchun Univ Technol Sch Mechatron Engn Changchun 130012 Peoples R China

In this paper, an improved multithreshold image segmentation method based on the whale optimization algorithm (RAV-WOA) is proposed, with the between-class variance (Otsu method) as the objective function. The proposed RAV-WOA is able to select satisfactory optimal thresholds while ensuring high efficiency and quality when performing image segmentation on grayscale and color images In the current work, a reverse learning strategy was introduced into the initialization of RAV-WOA populations to improve the quality of the initial population of whales. An adaptive weighting strategy was introduced into the RAV-WOA algorithm, which is influenced by the fitness value and the number of iterations, to balance the global search capability of the algorithm with the local exploitation capability. The proposed RAV-WOA is then applied to the Otsu method to solve the multilevel thresholding image segmentation problem. To better verify the effectiveness of the proposed method, this paper compares the RAV-WOA with some classical heuristic algorithms and performs image segmentation experiments on a set of benchmark images with low and high thresholds. The experimental results show that the convergence speed and convergence accuracy of RAV-WOA are significantly better than other algorithms, and the segmentation results of RAV-WOA in multithreshold image segmentation have better quality and stability than other algorithms.

关键词： Multithreshold image segmentation Otsu method whale optimization algorithm Adaptive weights Horizontal crossover and vertical crossover

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Monophenolase assay using excitation-emission matrix fluorescence and ELMAN neural network assisted by whale optimization algorithm

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ANALYTICAL BIOCHEMISTRY 2022年 655卷 114838页

作者： Guo, Zhenyu Zhang, Ling Chen, Qinfei Han, Mengqi Liu, Wenbin Sichuan Univ Sch Chem Engn Dept Pharmaceut & Biol Engn Chengdu 610065 Peoples R China

Tyrosinase plays a vital role for melanogenesis and inherently involves both monophenolase activity and diphenolase activity. Monophenolase catalyzes hydroxylation of tyrosine to L-DOPA (L-3,4-dihydroxyphenylalanine). Real-time monophenolase assay method is of outstanding interest for both scientific research and industrial application. A combined strategy of three-dimensional excitation-emission matrix (EEM) fluorescence spectra and artificial neural network was developed to determine monophenolase activity. A quantitation system for tyrosine in presence of L-DOPA was designed based on ELMAN neural network. Principal component analysis (PCA) was conducted to reduce the dimensionality of fluorescence spectra. Four principal components was used as input variables. whale optimization algorithm (WOA) was implemented to optimize the initial weights and threshold network. Real-time concentration of tyrosine in monophenolase reaction was monitored to calculate the initial velocity for tyrosine consumption. The exclusive monophenolase activity without interference from diphenolase reaction was determined. Limit of detection (LOD) for monophenolase assay is 0.0113 U mL(-1). Using the proposed method, enzyme kinetics for monophenolase was investigate. K-m was calculated as 14.16 mu M. Inhibitor for monophenolase was screened by using model molecule kojic acid with IC50 of 3.49 mu M. The assay method exhibited a promising prospect to characterize the kinetics and inhibitor of monophenolase.

关键词： Monophenolase Tyrosine Excitation -emission matrix fluorescence ELMAN neural Network Principal component analysis whale optimization algorithm

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Two-dimensional optimal path planning for autonomous underwater vehicle using a whale optimization algorithm

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第9期33卷 e6140-e6140页

作者： Yan, Zheping Zhang, Jinzhong Yang, Zewen Tang, Jialing Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China

This paper presents a whale optimization algorithm (WOA) based on forward looking sonar to achieve two-dimensional optimal path planning for an autonomous underwater vehicle. The purpose of path planning is not only to effectively avoid threat regions and safely reach the intended target with minimum fuel cost but also to obtain an optimal or near-optimal path in a complex ocean battlefield environment. The WOA, based on the bubble-net attacking behavior of humpback whales, mimics encircling the prey, attacks with a bubble-net method, and search for prey to effectively determine the global optimal solution in the search space. The WOA not only has fast convergence speed and high calculation accuracy but can also effectively balance exploration and exploitation to avoid falling into a local optimum and obtain the global optimal solution. Five sets of experiments are applied to verify the superiority and stability of the WOA. Compared with other algorithms, such as artificial bee colony, bat algorithm, cuckoo search, flower pollination algorithm, moth-flame optimization algorithm, particle swarm optimization, and water wave optimization, the WOA exhibits better optimization performance and stronger robustness. The experimental results reveal that the WOA can find the shortest path compared with all the other algorithms, and it is an effective and feasible method for solving the path planning problem.

关键词： autonomous underwater vehicle forward looking sonar path planning whale optimization algorithm

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Improved whale optimization algorithm via the Inertia Weight Method Based on the Cosine Function

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JOURNAL OF INTERNET TECHNOLOGY 2022年第7期23卷 1623-1632页

作者： Shi, Xiaoming Li, Kun Jia, Liwei Henan Med Coll Dept Publ Infrastruct Comp Teaching & Res Sect Kaifeng Henan Peoples R China

whale optimization algorithm (WOA) is a new metaheuristic algorithm proposed by Australian scholar Mirjalili Seyedali in 2016 based on the feeding behavior of whales in the ocean. In response to the disadvantages of this algorithm, such as low solution accuracy, slow convergence speed and easy to fall into local optimum, an improved whale optimization algorithm (IWOA) is proposed in this paper. We introduce chaotic mapping in the initialization of the algorithm to keep the whale population with diversity;introduce adaptive inertia weights in the spiral position update of humpback whales to prevent the algorithm from falling into local optimum;and introduce Levy flight in the random search for food of humpback whales to improve the global search ability of the algorithm. In the simulation experiments, we compare the algorithm of this paper with other metaheuristic algorithms in seven classical benchmark test functions, and the numerical results of four indexes, minimum, maximum, mean and standard deviation, in different dimensions, illustrate that the algorithm of this paper has better performance results.

关键词： whale optimization algorithm Chaos mapping Inertia weights

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