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A band selection approach based on wavelet support vector machine ensemble model and membrane whale optimization algorithm for hyperspectral image

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APPLIED INTELLIGENCE 2021年第11期51卷 7766-7780页

作者： Wang, Mingwei Yan, Ziqi Luo, Jianwei Ye, Zhiwei He, Peipei China Univ Geosci Inst Geol Survey Wuhan 430074 Peoples R China Natl Adm Surveying Mapping & Geoinformat Key Lab Natl Geog Census & Monitoring Wuhan 430079 Peoples R China Huazhong Univ Sci & Technol Hubei Canc Hosp Tongji Med Coll Wuhan 430079 Peoples R China Hubei Univ Technol Sch Comp Sci Wuhan 430068 Peoples R China North China Univ Water Resources & Elect Power Coll Surveying & Geoinformat Zhengzhou 450045 Peoples R China

Hyperspectral Image (HSI) has become one of the important remote sensing sources for object interpretation by its abundant band information. Among them, band selection is considered as the main theme in HSI classification to reduce the data dimension, and it is a combinatorial optimization problem and difficult to be completely solved by previous techniques. whale optimization algorithm (WOA) is a newly proposed swarm intelligence algorithm that imitates the predatory strategy of humpback whales, and membrane computing is able to decompose the band information into a series of elementary membranes that decreases the coding length. In addition, Support Vector Machine (SVM) combined with wavelet kernel is adapted to HSI datasets with high dimension and small samples, ensemble learning is an effective tool that synthesizes multiple sub-classifiers to solve the same problem and obtains accurate category label for each sample. In the paper, a band selection approach based on wavelet SVM (WSVM) ensemble model and membrane WOA (MWOA) is proposed, experimental results indicate that the proposed HSI classification technique is superior to other corresponding and newly proposed methods, achieves the optimal band subset with a fast convergence speed, and the overall classification accuracy has reached 93% for HSIs.

关键词： Hyperspectral image Band selection whale optimization algorithm Membrane computing Classifier ensemble Wavelet support vector machine

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Battlefield Target Grouping by a Hybridization of an Improved whale optimization algorithm and Affinity Propagation

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IEEE ACCESS 2021年 9卷 46448-46461页

作者： Duan, Yuxian Liu, Changyun Li, Song Air Force Engn Univ Air & Missile Def Coll Xian 710051 Peoples R China Air Force Engn Univ Grad Coll Xian 710051 Peoples R China

Target grouping, which is essentially a data clustering problem, is a research hotspot in the field of battlefield situation assessment. To address an unknown battlefield environment, affinity propagation based on an improved whale optimization algorithm (APBWOA) is proposed from the perspective of clustering. First, we propose a whale optimization algorithm based on a chaotic map and nonlinear inertia weight improvement called CPIW-WOA, which uses an improved circle map to generate initial populations and introduces nonlinear inertia weights to improve its convergence efficiency. The test results on nine benchmark functions show that the CPIW-WOA algorithm has superior performance to existing methods. Second, based on the fact that it fully considers the weights of the attributes in a given sample, the weighted Mahalanobis distance is adopted to replace the Euclidean distance. In addition, the silhouette index is introduced to determine the optimal number of clusters. By iteratively updating through CPIW-WOA to search for the optimal settings, the limitation of manually entering specified parameters can be overcome. Test results on real-world datasets show that the new method is more accurate and effective than other methods;therefore, it can provide effective solutions with respect to battlefield target grouping.

关键词： Clustering algorithms optimization algorithms Statistics Sociology Convergence whales Clustering algorithm affinity propagation whale optimization algorithm target grouping situation assessment

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An Improved whale optimization algorithm Based PIDF-(1+PI) Cascade Automatic Generation Control for Multi-Area Multi-Source Power System With Capacitive Energy Storage

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IEEE ACCESS 2023年 11卷 72418-72435页

作者： Mao, Jingfeng Liu, Runda Wu, Aihua Wu, Shang He, Jianjun Nantong Univ Sch Elect Engn Nantong 226019 Peoples R China Nantong Offshore Construct & Engn Co Ltd Nantong 226019 Peoples R China

Modern power system has complex composition structure and high stability operation requirements. While the emergence of various new energy sources and the uncertainty of external disturbances bring a great challenge to the Automatic Generation Control (AGC) of power system. In order to improve the robustness of the AGC and facilitate the practical engineering application, this paper proposes a novel structure multistage Proportional Integral Derivative (PID) cascade automatic generation controller as well as an improved more effective control parameter optimization algorithm. Firstly, a two-area multi-unit multi-source hydro/thermal power system containing with capacitive energy storage unit is modeled. And using double closed-loops control method, a PID controller with derivative Filter and 1+Proportional Integral unit (PIDF-(1+PI)) cascade automatic generation controller is proposed. Secondly, by introducing a nonlinear time-varying adaptive weight factor, an improved whale optimization algorithm (WOA-w) is proposed to accelerate the convergence speed and enhance the solution accuracy. Then, based on the integral of time multiplied absolute error (ITAE) objective function, the proposed PIDF-(1+PI) controller parameters are optimized by WOA-w. Finally, MATLAB/Simulink software is used to implement the control system multi-case simulation. Compared with other three control strategies, the multi-scenario cases simulation results verify the correctness and effectiveness of the proposed control strategy.

关键词： INDEX TERMS Automatic generation control cascade control hydro thermal system whale optimization algorithm capacitive energy storage

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A novel discrete whale optimization algorithm for solving knapsack problems

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APPLIED INTELLIGENCE 2020年第10期50卷 3350-3366页

作者： Li, Ya He, Yichao Liu, Xuejing Guo, Xiaohu Li, Zewen Hebei GEO Univ Coll Informat & Engn Shijiazhuang 050031 Hebei Peoples R China

whale optimization algorithm (WOA) is a recently proposed meta-heuristic algorithm which imitates the hunting behavior of humpback whales. Due to its characteristic advantages, it has found its place in the mature population-based methods in many scientific and engineering fields. Because WOA was proposed for continuous optimization, it cannot be directly used to solve discrete optimization problems. For this purpose, we first give a new V -shaped function by drawing lesson from the existing discretization methods, which transfer a real vector to an integer vector. On this basis, we propose a novel discrete whale optimization algorithm (DWOA). DWOA uses the new proposed V -shaped function to generate an integer vector, and it can be used to solve discrete optimization problems with solution space {0,1, horizontal ellipsis ,m(1)}x{0,1, horizontal ellipsis ,m(2)}x horizontal ellipsis x{0,1, horizontal ellipsis ,m(n)}. To verify effectiveness of DWOA for the 0-1 knapsack problem and the discount {0-1} knapsack problem, we solve their benchmark instances from published literature and compare with the state-of-the-art algorithms. The comparison results show that the DWOA has more superiority than existing algorithms for the two kinds of knapsack problems.

关键词： whale optimization algorithm Meta-heuristic algorithm V -shaped function 0-1knapsack problem Discounted {0-1} knapsack problem

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Dynamic software rejuvenation in web services: a whale optimization algorithm-based approach

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2020年第2期28卷 890-903页

作者： Rezaei Kalantari, Kimia Ebrahimnejad, Ali Motameni, Homayun Islamic Azad Univ Dept Comp Engn Babol Branch Babol Sar Iran Islamic Azad Univ Dept Math Qaemshahr Branch Qaemshahr Iran Islamic Azad Univ Dept Comp Engn Sari Branch Sari Iran

In this paper, we suggest a method for determining the restarting time for web services to increase availability, known as rejuvenation. We consider different parameters such as number of users, maximum service request number, response time, and throughput of a web service to determine its restarting time. Software rejuvenation is an effective technique to counteract software aging in continuously running applications such as web service-based systems. In these systems, web services are allocated based on the needs of the receivers and facilities of servers. One of the challenges while assigning web services is selecting the appropriate server to reduce faults. Since the selection of a server among candidates while maintaining the optimal quality of service is an NP-hard problem, metaheuristics seem to be suitable. In this paper, we propose dynamic software rejuvenation as a proactive fault-tolerance technique based on the whale optimization algorithm. The threshold for the rejuvenation of each of the web services is considered and training is done based on the features of the service providers as well as the needs of the receivers. The whale optimization algorithm with the criterion of movement radius is utilized for flexibility of web service provider selection. Here, we detect and rejuvenate systems that required rejuvenation before the occurrence of a fault. The simulation results reveal that our strategy can decrease the failure rate by an average of 30 percent in comparison with state-of-the-art strategies and improve the system availability in web services.

关键词： Software aging software rejuvenation web service whale optimization algorithm

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Short-Term Hydro Generation Scheduling of the Three Gorges Hydropower Station Using Improver Binary-coded whale optimization algorithm

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WATER RESOURCES MANAGEMENT 2021年第11期35卷 3771-3790页

作者： Yang, Kun Yang, Kan Hohai Univ Coll Hydrol & Water Resources 1 Xikang Rd Nanjing 210098 Peoples R China

The short-term hydropower generation scheduling (STHGS) is a complicated problem in the utilization of hydropower and water resources. An improved binary-coded whale optimization algorithm (IBWOA) is proposed in this paper to solve the complex nonlinear problem. The STHGS problem is divided into unit combination (UC) subproblem and economic load distribution (ELD) subproblem. For the UC subproblem, we use the sigmoid function (SF) to generate a binary array representing the start/stop state of the unit. The whale algorithm's search mechanism is optimized, and the inertia weight and perturbation variation strategy are introduced to improve the algorithm's optimization ability. Each generation solution is optimized by repairing the minimum uptime/downtime constraint and the spinning reserve capacity constraint. For ELD subproblem, the optimal stable load distribution table (OSLDT) is used to distribute the load quickly. The Mutation mechanism and the Locally balanced dynamic search mechanism compensate for the non-convex problems caused by start-stop constraints and stable optimal table methods. Finally, the proposal is applied to solve the STHGS of the Three Gorges Hydropower Station. When the water head is 75 m,88 m, and 107 m, the minimum water consumption calculated by the IBWOA algorithm is 1,058,323,464 m(3), 892,524,696 m(3), and 745,272,216 m(3), respectively. Compared with the traditional whale optimization algorithm, the water consumption of the IBOWA algorithm corresponding to 75 m, 88 m, and 107 m water heads is reduced by 0.76%, 0.26%, and 0.05%, respectively. The comparison between the IBWOA algorithm and other heuristic algorithms shows that the IBWOA has good feasibility and high optimization accuracy.

关键词： Short-term hydropower generation scheduling whale optimization algorithm Adaptive inertia weight Perturbation mutation strategy Optimal stable load distribution table

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ANWOA: an adaptive nonlinear whale optimization algorithm for high-dimensional optimization problems

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NEURAL COMPUTING & APPLICATIONS 2023年第30期35卷 22671-22686页

作者： Elmogy, Ahmed Miqrish, Haitham Elawady, Wael El-Ghaish, Hany Prince Sattam Ibn Abdelaziz Univ Comp Engn Dept Alkharj 16273 Saudi Arabia Tanta Univ Comp & Control Engn Dept Tanta 31527 Egypt

One of the most competitive nature-inspired metaheuristic optimization algorithms is the whale optimization algorithm (WOA). This algorithm is proven awesome in solving complex and constrained multi-objective problems. It is also popularly used as a feature selection algorithm while solving non-deterministic polynomial-time hardness (NP-hard) problems. Many enhancements have been introduced in the literature for the WOA resulting in better optimization algorithms. Differently from these research efforts, this paper presents a novel version of the WOA called ANWOA. ANWOA considers producing two types of discrete chaotic maps that have suitable period states, and the highest sensitivity to initial conditions, randomness, and stability which in turn leads to optimal initial population selection and thus global optimality. The presented ANWOA uses two nonlinear parameters instead of the two linear ones which permeate both the exploration and exploitation phases of WOA, leading to accelerated convergence, better accuracy, and influential improvement in the spiral updating position. Additionally, a dynamic inertia weight coefficient is utilized to attain a suitable balance between the exploration and exploitation phases meanwhile improving the convergence speed. Furthermore, ANWOA uses circle map values that influence each random factor in the WOA and consequently ensuring not trapped in local optima with a promoted global optimum search. The empirical analysis is conducted in thirty-three benchmark functions, and the results show that the introduced novel algorithm is the most competitive one.

关键词： whale optimization algorithm Circle and tent maps Constrained optimization problems Convergence factor

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Inversion of Bubble Size Distribution Based on whale optimization algorithm

IEEE PHOTONICS JOURNAL

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IEEE PHOTONICS JOURNAL 2024年第4期16卷 1页

作者： Lu, Zhenzhong Hu, Ziqi Ma, Lin Huang, Shangtao Peng, Tao Liu, Min Han, Biao Liao, Jiali Wang, Zihao Sun, Yanling Xidian Univ Sch Optoeletron Engn Xian 710071 Peoples R China

A particle size inversion method based on whale optimization algorithm (WOA) is presented. In the experiment, the small angle forward scattering measurement system is used to conduct experimental research on the inversion of bubble particle size. WOA is used to invert and simulate the bubbles that follow a certain distribution, adding different levels of random noise to verify the stability of the algorithm. The results verify the feasibility and stability of applying WOA to particle size inversion.

关键词： Noise Size measurement whales Particle measurements Atmospheric measurements Search problems Noise level whale optimization algorithm small angle forward scattering method bubble size distribution

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Levy flight trajectory-based whale optimization algorithm for engineering optimization

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ENGINEERING COMPUTATIONS 2018年第7期35卷 2406-2428页

作者： Zhou, Yongquan Ling, Ying Luo, Qifang Guangxi Univ Nationalities Coll Informat Sci & Engn Nanning Peoples R China Key Labs Guangxi High Sch Complex Syst & Computat Nanning Peoples R China

Purpose This paper aims to represent an improved whale optimization algorithm (WOA) based on a Levy flight trajectory and called the LWOA algorithm to solve engineering optimization problems. The LWOA makes the WOA faster, more robust and significantly enhances the WOA. In the LWOA, the Levy flight trajectory enhances the capability of jumping out of the local optima and is helpful for smoothly balancing exploration and exploitation of the WOA. It has been successfully applied to five standard engineering optimization problems. The simulation results of the classical engineering design problems and real application exhibit the superiority of the LWOA algorithm in solving challenging problems with constrained and unknown search spaces when compared to the basic WOA algorithm or other available solutions. Design/methodology/approach In this paper, an improved WOA based on a Levy flight trajectory and called the LWOA algorithm is represented to solve engineering optimization problems. Findings It has been successfully applied to five standard engineering optimization problems. The simulation results of the classical engineering design problems and real application exhibit the superiority of the LWOA algorithm in solving challenging problems with constrained and unknown search spaces when compared to the basic WOA algorithm or other available solutions. Originality value An improved WOA based on a Levy flight trajectory and called the LWOA algorithm is first proposed.

关键词： Engineering optimization Levy flight trajectory Meta-heuristic algorithm whale optimization algorithm

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WOA-DBSCAN: Application of whale optimization algorithm in DBSCAN Parameter Adaption

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IEEE ACCESS 2023年 11卷 91861-91878页

作者： Zhang, Xinliang Zhou, Shibo Jimei Univ Coll Nav Xiamen 361021 Peoples R China

Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a classic density-based clustering method that can identify clusters of arbitrary shapes in noisy datasets. However, DBSCAN requires two input parameters: the neighborhood distance value (Eps) and the minimum number of sample points in its neighborhood (MinPts), to perform clustering on a dataset. The quality of clustering is highly sensitive to these two parameters. This paper introduces a parameter-adaptive DBSCAN clustering algorithm based on the whale optimization algorithm (WOA-DBSCAN) to tackle this issue. The algorithm determines the parameter range based on the dataset distribution and utilizes the silhouette coefficient as the objective function. It iteratively selects the two input parameters of DBSCAN within the parameter range using the WOA. This approach ultimately achieves adaptive clustering of DBSCAN. Experimental results on five typical artificial datasets and six real UCI datasets demonstrate the effectiveness of the proposed WOA-DBSCAN algorithm. Compared with DBSCAN and its related optimization algorithms, WOA-DBSCAN shows significant improvements. The F-values of WOA-DBSCAN increased by 9.8%, 13.2%, and 2%, respectively, in two-dimensional artificial datasets. Additionally, the accuracy values on low to medium dimensional real datasets increased by 22.3%, 10%, and 23.3%. Hence, WOA-DBSCAN can maintain the clustering ability of DBSCAN while achieving adaptive parameter clustering.

关键词： DBSCAN algoritm parameter adaptive whale optimization algorithm data mining

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