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A novel data clustering approach based on whale optimization algorithm

EXPERT SYSTEMS 2021年第3期38卷 e12657-e12657页

作者： Singh, Tribhuvan Siksha O Anusandhan Deemed Univ Dept Comp Sci & Engn Bhubaneswar Odisha India

Data clustering is an important technique of data mining in which the objective is to partition N data objects into K clusters that minimize the sum of intra-cluster distances between each data object to its nearest centroid. This is an optimization problem, and various optimization algorithms have been suggested for capturing the position vectors of optimal centroids. However, in these approaches, the problem of local entrapment is very common due to weak exploration mechanism. In this paper, a novel approach based on a whale optimization algorithm (WOA) is suggested for data clustering. The performance of the suggested approach is validated using 14 benchmark datasets of the UCI machine learning repository. The experimental results and various statistical tests have justified the efficacy of the suggested approach.

关键词： data clustering data mining k‐ means optimization problems whale optimization algorithm

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Estimation of rainfed maize transpiration under various mulching methods using modified Jarvis-Stewart model and hybrid support vector machine model with whale optimization algorithm

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AGRICULTURAL WATER MANAGEMENT 2021年 249卷 106799-106799页

作者： Zheng, Jing Fan, Junliang Zhang, Fucang Wu, Lifeng Zou, Yufeng Zhuang, Qianlai Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid & Semiarid Are Minist Educ Yangling 712100 Shaanxi Peoples R China Purdue Univ Dept Earth Atmospher & Planetary Sci W Lafayette IN 47907 USA Purdue Univ Dept Agron W Lafayette IN 47907 USA Nanchang Inst Technol Sch Hydraul & Ecol Engn Nanchang 330099 Jiangxi Peoples R China

Soil mulching can effectively modify the crop growth environment and increase crop productivity in rainfed agriculture. Accurate estimation of crop evapotranspiration (ET), especially its transpiration (T) component, is crucial for understanding the crop water use and predicting crop yield in agricultural ecosystems. Nevertheless, direct measurement of T in the field is often difficult, expensive, destructive and time-consuming. Daily rainfed maize T under four mulching methods (NM: non-mulching, SM: straw mulching, RPBF: plastic-mulched ridge with bare furrow, and RPSF: plastic-mulched ridge with straw-mulched furrow) was obtained from sap flow measurements over four maize growing seasons (2015-2018) in Northwest China. A modified Jarvis-Stewart model (MJS) and a support vector machine model optimized by the whale optimization algorithm (SVM-WOA) were further proposed to estimate daily maize T based on solar radiation (R-s), vapor pressure deficit (VPD), soil water content (SWC) and leaf area index (LAI), which were compared to the simple multiple linear regression model (MLR). The three models were calibrated using data obtained in 2015 and 2017, and validated using data from 2016 and 2018. The measured seasonal T under SM, RPBF and RPSF was increased by 6.9-19.1%, 12.1-31.3% and 15.3-36.7% compared to that under NM, respectively. The SVM-WOA model (R-2 = 0.83-0.89, RMSE = 0.55-0.73 mm d(-1), MAE = 0.42-0.53 mm d(-1)) was superior to the MJS model (R-2 = 0.61-0.79, RMSE = 0.75-1.12 mm d(-1), MAE = 0.58-0.88 mm d(-1)) during validation, both of which greatly outperformed the MLR model (R-2 = 0.57-0.60, RMSE = 1.28-1.41 mm d(-1), MAE = 0.99-1.09 mm d(-1)) under various mulching methods. The prediction accuracy of the SVM-WOA and MJS models was improved by 47-57% and 19-41% in terms of RMSE compared with that of the MLR model, respectively. Although the physically-based MJS model satisfactorily described the dynamics of rainfed maize T under various mulching methods,

关键词： Transpiration Mulching Modified Jarvis-Stewart model Support vector machine 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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WOAMSA: whale optimization algorithm for Multiple Sequence Alignment of Protein Sequence 1

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International Conference on Computational Vision and Bio-Inspired Computing (ICCVBIC)

作者： Kumar, Manish Kumar, Ranjeet Nidhya, R. MITS Dept CSE Madanapalle India

ISBN: (数字)9783030372187

ISBN: (纸本)9783030372187;9783030372170

In the past few years, the Multiple Sequence Alignment (MSA) related problems has gained wide attractions of scientists and biologists as it is one of the major tools to find the structural and behavioral nature of Biomolecules. Furthermore, MSA can also be utilized for gene regulation networks, protein structure prediction, homology searches, genomic annotation or functional genomics. In this paper, we purpose a new nature and bio-inspired algorithm, known as the whale optimization algorithm for MSA (WOAMSA). The algorithm works on the principle of bubble-net hunting nature of the whale with the help of objective function we tried to solve the MSA problems of protein sequences. In order to focus on the effectiveness of the presented approach, we used BALiBASE benchmarks dataset. At the last, we have compared the obtained result for WOAMSA with other standard methods mentioned in the literature. After comparison, it was concluded that the presented approach is better (in terms of obtained scores) when compared with other methods available in the considered datasets.

关键词： Proteins Bioinformatics whale optimization algorithm Multiple Sequence Alignment

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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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The whale optimization algorithm Approach for Deep Neural Networks

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SENSORS 2021年第23期21卷 8003页

作者： Brodzicki, Andrzej Piekarski, Michal Jaworek-Korjakowska, Joanna AGH Univ Sci & Technol Dept Automat Control & Robot PL-30059 Krakow Poland

One of the biggest challenge in the field of deep learning is the parameter selection and optimization process. In recent years different algorithms have been proposed including bio-inspired solutions to solve this problem, however, there are many challenges including local minima, saddle points, and vanishing gradients. In this paper, we introduce the whale Optimisation algorithm (WOA) based on the swarm foraging behavior of humpback whales to optimise neural network hyperparameters. We wish to stress that to the best of our knowledge this is the first attempt that uses whale Optimisation algorithm for the optimisation task of hyperparameters. After a detailed description of the WOA algorithm we formulate and explain the application in deep learning, present the implementation, and compare the proposed algorithm with other well-known algorithms including widely used Grid and Random Search methods. Additionally, we have implemented a third dimension feature analysis to the original WOA algorithm to utilize 3D search space (3D-WOA). Simulations show that the proposed algorithm can be successfully used for hyperparameters optimization, achieving accuracy of 89.85% and 80.60% for Fashion MNIST and Reuters datasets, respectively.

关键词： whale optimization algorithm optimization deep learning neural networks hyperparameters

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A many-objective whale optimization algorithm to perform robust distributed clustering in wireless sensor network

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APPLIED SOFT COMPUTING 2021年 110卷 107650-107650页

作者： Kotary, Dinesh Kumar Nanda, Satyasai Jagannath Gupta, Rachana Malaviya Natl Inst Technol Dept Elect & Commun Engn Jaipur 302017 Rajasthan India

The substantial increase in the usage of wireless sensor networks (WSNs) encourages to develop data clustering in event monitoring applications. Many centralized algorithms with single objective optimization are employed to solve this problem. However privacy, security and technical constraints are key issues in traditional centralized approach. Moreover, many WSN applications like condition monitoring and target tracking require more than three objectives for effective partitioning of dataset. This paper proposes many-objective whale optimization algorithm to handle robust distributed clustering in WSN. Initially, a swarm based many-objective whale optimization (MaOWOA) is discussed where reference point based leader selection method is utilized in updating the solutions instead of grid based leader selection as in multi-objective approach. This method gives better convergence and diversity. The simulation result of proposed approach is evaluated on many-objective DTLZ test problems against existing many-objective methods which is faster in terms of simulation time and gives competitive results in terms of generational distance (GD), inverse generational distance (IGD), spacing (SP) and hyper volume difference (HVD). Further, the encouraging results of the proposed MaOWOA are applied to perform robust distributed clustering in WSNs which is termed as distributed many-objective clustering using whale optimization algorithm (DMaOWOA). In this approach, a weight based method is incorporated to detect and remove the outliers and diffusion method of cooperation is used for distributed clustering. The proposed DMaOWOA is tested on one synthetic and three practical WSN datasets. It is observed that DMaOWOA based clustering performs up to 6% and 8% improvement in terms of Silhouette index as compared to particle swarm optimization based many objective distributed clustering (DMaOPSO) and distributed K-Means (DK-Means) clustering algorithm, respectively. (C) 2021 Elsevier B

关键词： whale optimization algorithm Many-objective optimization Reference points Distributed clustering

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Multi-Objective whale optimization algorithm for Computation Offloading optimization in Mobile Edge Computing

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SENSORS 2021年第8期21卷 2628页

作者： Huang, Mengxing Zhai, Qianhao Chen, Yinjie Feng, Siling Shu, Feng Hainan Univ Sch Informat & Commun Engn 58 Renmin Ave Haikou 570228 Hainan Peoples R China Hainan Univ State Key Lab Marine Resource Utilizat South Chin 58 Renmin Ave Haikou 570228 Hainan Peoples R China Hainan Univ Sch Sci 58 Renmin Ave Haikou 570228 Hainan Peoples R China

Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it is very important to decide quickly how many tasks should be executed on servers and how many should be executed locally. Only computation tasks that are properly offloaded can improve the Quality of Service (QoS). Some existing methods only focus on a single objection, and of the others some have high computational complexity. There still have no method that could balance the targets and complexity for universal application. In this study, a Multi-Objective whale optimization algorithm (MOWOA) based on time and energy consumption is proposed to solve the optimal offloading mechanism of computation offloading in mobile edge computing. It is the first time that MOWOA has been applied in this area. For improving the quality of the solution set, crowding degrees are introduced and all solutions are sorted by crowding degrees. Additionally, an improved MOWOA (MOWOA2) by using the gravity reference point method is proposed to obtain better diversity of the solution set. Compared with some typical approaches, such as the Grid-Based Evolutionary algorithm (GrEA), Cluster-Gradient-based Artificial Immune System algorithm (CGbAIS), Non-dominated Sorting Genetic algorithm III (NSGA-III), etc., the MOWOA2 performs better in terms of the quality of the final solutions.

关键词： edge computing computation offloading multi-objective whale optimization algorithm

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Extending the whale optimization algorithm with Chaotic Local Search 7

Extending the Whale Optimization Algorithm with Chaotic Loca...

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7th International Conference on Soft Computing and Machine Intelligence (ISCMI)

作者： Rohr, Nicolas Ruggli, Oliver Hanne, Thomas Dornberger, Rolf Univ Appl Sci & Arts Northwestern Switzerland Olten Switzerland

ISBN: (纸本)9781728175591

The whale optimization algorithm (WOA) is a recently introduced metaheuristic that has shown a promising performance in solving optimization problems. To further improve the search performance of WOA, the Chaotic Local Search (CLS) mechanism is incorporated into WOA. Ten different chaotic maps are investigated to give insights into the influence of CIS in the WOA. The goal is to compare th e performance of the whale optimization algorithm with Chaotic Local Search (WOACLS) with the standard WOA and the Chaotic WOA (CWOA) by applying them to twenty wellknown benchmark functions. Results show that the performance of WOACLS depends on both, the characteristics of the embedded chaotic map and the properties of the search landscape of the optimization problems considered.

关键词： whale optimization algorithm chaos map: chaotic local search WOA CWOA WOACLS

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Image Enhancement based on whale optimization algorithm 15

Image Enhancement based on Whale Optimization Algorithm

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15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET)

作者： Ye, Zhiwei Wang, Fengwen Kochan, Roman Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China Univ Bielsko Biala Dept Comp Sci & Automat Bielsko Biala Poland Lviv Polytech Natl Univ Dept Specialized Comp Syst Lvov Ukraine

ISBN: (纸本)9781728155661

The digital image technology is the most widely used in various fields, especially in medical imaging. Digital image technology can effectively improve the efficiency of medical image processing and ensure the accuracy of clinical diagnosis. This paper studies whale optimization algorithm (WOA) to optimize the optimal value of Gamma correction in image enhancement. In the process of searching for the optimal solution, the WOA is an efficient algorithm, which has the characteristics of fast convergence and high precision. In order to present the detailed information of medical image, WOA is applied to search the optimal value of Gamma correction.

关键词： whale optimization algorithm Ant Lion algorithm Gamma correction Image enhancement

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