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Multi-Strategy Assisted Multi-Objective whale optimization algorithm for Feature Selection

Computer Modeling in Engineering & Sciences 2024年第8期140卷 1563-1593页

作者： Deng Yang Chong Zhou Xuemeng Wei Zhikun Chen Zheng Zhang School of Information Engineering Hebei GEO UniversityShijiazhuang050031China Faculty of Science University of AlbertaEdmontonT2N1N4Canada College of Resources and Environment Beibu Gulf UniversityQinzhou535011China Clinical Laboratory The Frist Hospital of Hebei Medical UniversityShijiazhuang050000China

In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate *** fact,irrelevant features may even hinder classification *** selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error ***-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter,but the results obtained depend on the value of the *** eliminate this parameter’s influence,the problem can be reformulated as a multi-objective optimization *** whale optimization algorithm(WOA)is widely used in optimization problems because of its simplicity and easy *** this paper,we propose a multi-strategy assisted multi-objective WOA(MSMOWOA)to address feature *** enhance the algorithm’s search ability,we integrate multiple strategies such as Levy flight,Grey Wolf Optimizer,and adaptive mutation into ***,we utilize an external repository to store non-dominant solution sets and grid technology is used to maintain *** on fourteen University of California Irvine(UCI)datasets demonstrate that our proposed method effectively removes redundant features and improves classification *** source code can be accessed from the website:https://***/zc0315/MSMOWOA.

关键词： Multi-objective optimization whale optimization algorithm multi-strategy feature selection

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Clustering based on whale optimization algorithm for IoT over wireless nodes

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SOFT COMPUTING 2021年第7期25卷 5663-5682页

作者： Bozorgi, Seyed Mostafa Hajiabadi, Mahdi Rohani Hosseinabadi, Ali Asghar Rahmani Sangaiah, Arun Kumar Islamic Azad Univ Dept Comp Engn Tehran North Branch Tehran Iran Islamic Azad Univ Fac Comp & Informat Technol Engn Qazvin Branch Qazvin Iran Univ Regina Dept Comp Sci Regina SK Canada Vellore Inst Technol VIT Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India

IoT or Internet of Things can improve the possibility of interaction between various smart components in real time. In the infrastructure of IoT, wireless sensors can be used in order to reduce communication costs. Despite having positive effects, using wireless nodes add some challenges to the system. Limited resources, such as energy, CPU power and memory, are the main concerns in this technology. Energy consumption is the most challenging one. Designing an optimized routing pattern through heuristic algorithms is a common way to tackle this problem. Therefore, in the proposed algorithm, a WOA-based method has been proposed to expand the life span of the system. Also, a novel fitness function is defined for reducing the energy consumption of the network, load balancing and node coverage. Clustering is done unequally;it means that cluster heads (CHs) nearer to the base station (BS) have more energy for data relay. In this paper, for reducing the number of messages, a clustering stage is added at the beginning of each metaround. The number of rounds in a metaround is variable. The status of each node is analyzed by BS before each round. Low energy level causes a new metaround. Moreover, the CH-BS interaction is implemented through multi-hop pattern. Results suggest that there is an enhancement instability, energy-saving, throughput and lifespan.

关键词： Internet of Things Wireless sensor networks whale optimization algorithm Unequal clustering Network lifetime

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optimization of biodiesel production of spirulina-platensis algae biomass using RSM integrated with whale optimization algorithm

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INDIAN JOURNAL OF ENGINEERING AND MATERIALS SCIENCES 2024年第5期31卷 750-758页

作者： Chhikara, Ashish Dhingra, Ashwani Kumar Chhabra, Deepak Kumar, Pardeep Maharshi Dayanand Univ Univ Inst Engn & Technol Dept Mech Engn Rohtak 124001 Haryana India

Sustainable sources for biodiesel production from non-edible feedstocks are gaining attention due to limited sources of crude petroleum and concern related to the harmful emission of fossil fuels. In this study, Spirulina-Platensis algae biomass has been used for biodiesel production. The effect of methanol to algae biomass ratio, temperature, time, catalyst concentration, and stirring speed for optimization of algae biomass biodiesel using the transesterification process has been studied. The proposed work has implemented the quadratic regression equation obtained by RSM with the whale optimization algorithm. The result shows maximum production of algae biomass biodiesel (89.87%) obtained at the algal biomass/methanol ratio of 5.56, temperature of 54.64 degrees C, time of 78 minutes, H2SO4 of 77.06% w/w, and stirring speed of 450 rpm. The RSM and WOA prediction models have been compared for more. The experimental result shows that WOA optimization can accurately predict the optimal point with a small error. The fundamental properties of algae biomass biodiesel have been checked and compared with fossil diesel.

关键词： whale optimization algorithm RSM Spirulina platensis Transesterification

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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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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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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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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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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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Small signal stability enhancement of a large scale power system using a bio-inspired whale optimization algorithm

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Protection and Control of Modern Power Systems 2021年第1期6卷 443-459页

作者： Butti Dasu Sivakumar Mangipudi Srinivasarao Rayapudi Department of EEE Gudlavalleru Engineering CollegeGudlavlleruKrishna(Dt.)Andhra Pradesh521301India Department of EEE University College of EngineeringJNTUKKakinadaEast Godavari(Dt.)Andhra Pradesh533001India

A whale optimization algorithm(WOA)-based power system stabilizer(PSS)design methodology on modified single machine infinite bus(MSMIB)and multi-machine systems to enhance the small-signal stability(SSS)of the power system is *** PSS design methodology is implemented using an eigenvalue(EV)-based objective *** performance of the WOA is tested with several CEC14 and CEC17 test functions to investigate its potential in optimizing the complex mathematical *** New England 10-generator 39-bus system and the MSMIB system operating at various loading conditions are considered as the test systems to examine the proposed *** simulation results are obtained which validate the effectiveness of the proposed WOA method when compared with other algorithms.

关键词： Small-signal stability Power system stabilizer Stability Modified single machine infinite bus system Multi-machine system whale optimization algorithm Eigenvalues

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Two-stage damage identification method based on fractal theory and whale optimization algorithm

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ADVANCES IN STRUCTURAL ENGINEERING 2022年第11期25卷 2364-2381页

作者： Huang, Minshui Wan, Zihao Cheng, Xihao Xu, Zian Lei, Yongzhi Pan, Deng Wuhan Inst Technol Sch Civil Engn & Architecture 693 Xiongchu Ave Wuhan 430073 Peoples R China

Structural damage identification based on time domain method of vibration response has been widely developed in the recent decades, however, it still confronts some difficulties, such as measurement noise and model error. This paper proposes a novel two-stage damage identification method based on fractal dimension and whale optimization algorithm (WOA). In this study, based on vibration data, the difference in curvature of fractal dimension (DCFD) is used as the damage index to identify the location of suspicious damage elements in the first stage. A new objective function is proposed based on the curvature of fractal dimension (CFD) of acceleration signal, and the WOA is used to estimate the severity of the suspicious damaged element in the second stage. Firstly, the validity of the proposed method is verified by a numerical simply supported beam, and the results exhibit good damage identification ability. Then different noise levels (5% similar to 20%) are introduced into the dynamic responses to verify its robustness, the result shows that the method is of good anti-noise ability in the first stage. Although the second stage is slightly sensitive to noise, it can still effectively identify the severity of damage. Secondly, the vibration testing of a steel I-beam is designed to verify the rationality of the method in the application of actual structure. Finally, based on the simulated vibration test data of the I-40 Bridge, the applicability of the method to complex civil structure is verified, which shows that the method still has good ability to identify the location and severity of damage in complex structure and is of great significance in practical application.

关键词： Damage identification time domain fractal dimension whale optimization algorithm two-stage damage identification method

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