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Seismic Response Prediction and Velocity Model Building Inversion by the whale optimization algorithm

PURE AND APPLIED GEOPHYSICS 2023年第6期180卷 2087-2109页

作者： Routa, Alok Kumar Mohanty, Priya Ranjan Indian Inst Technol Indian Sch Mines Dept Appl Geophys Dhanbad 826004 India

The whale optimization algorithm (WOA) is a popular swarm intelligence algorithm that is based on the bubble-net hunting strategy used by humpback whales. The objective of this study is to assess the utilization of the WOA to perform the inversion of seismic data. The primary focus of this optimization is to first target an objective function for inversion that leads to minimizing the RMS error between field observed data and whale predicted data. The algorithm maintains a balance between exploration and exploitation phases that results in a better solution to the desired problem. The application of this technique to 12 benchmark models and validation through a true Vp model helps to reconstruct P-wave velocity and true model parameter estimation. The performance of the algorithm is compared with the gray wolf optimization (GWO) algorithm. The experimental results show that the final model after a reasonable number of iterations is able to provide optimum solutions within 10 different randomly generated search populations. As a consequence, it can be stated that the approach has higher reliability to reveal the P-wave imaging with a good convergence rate and minimum uncertainty over any complex geology.

关键词： Inversion Meta-heuristic algorithm RMS error Velocity model whale optimization algorithm

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Energy-aware disjoint dominating sets-based whale optimization algorithm for data collection in WSNs

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JOURNAL OF SUPERCOMPUTING 2023年第4期79卷 4318-4350页

作者： Elsway, Ahmed A. Khedr, Ahmed M. Alfawaz, Oruba Osamy, Walid Benha Univ Fac Comp & Artificial Intelligence Comp Sci Dept Banha Egypt Univ Sharjah Comp Sci Dept Sharjah 27272 U Arab Emirates Zagazig Univ Fac Sci Zagazig Egypt Univ Sharjah Res Inst Sci & Engn Sharjah U Arab Emirates

One of the major challenges in realizing a reliable wireless sensor network (WSN) that can survive under the emerging applications is the constrained energy of the sensors. Hence, extending the lifetime of WSN is a major concern, which directly impacts the performance of various WSN-based applications. In this regard, various methods have been developed that either investigate the energy consumption or lifetime enhancement of WSN. A promising method to conserve the energy of the sensors is to use sleep-awake scheduling by choosing disjoint groups of nodes called dominating set (DS). By distributing the data collection duties among these DSs, one DS handles these tasks for a specified period of time before being replaced by another group, extending the lifespan of the network. This problem becomes challenging in WSN with heterogeneous energy. Despite the success of the algorithms in determining the DS, none of the existing methods consider the node's energy while creation or selection of DS. This motivates us to utilize the DSs concept to control and maintain sleep/awake schedule of WSN nodes with heterogeneous energy. Toward this goal, we propose an energy-aware algorithm known as proposed initializer for whale optimization algorithm-based operator (PI-WOA-BO) to construct disjoint DSs that work as collector nodes for data gathering in each round and extend the total WSN lifetime. An energy-aware fitness function is introduced for selecting the best DSs that can maximize the WSN lifetime. Simulation results reveal that PI-WOA-BO exhibits enhanced performance over baseline techniques under various metrics including energy, stability, reliability and lifetime of WSN. PI-WOA-BO outperforms FUZZY-DS-ACO, CDS-FOR, BEE-VBC and CDS-LEACH by (17.4%, 40.1%, 31.1% and 53.6%), (7.7%, 33.5%, 23.4% and 48.5%) and (7.9%, 33.5%, 22.9% and 47.8%) in terms of First, Half and Last node dies, respectively.

关键词： whale optimization algorithm Dominating set Energy consumption Network lifetime Heterogeneous wireless sensor networks

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whale optimization algorithm based on lateral inhibition for image matching and vision-guided AUV docking

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第3期40卷 4027-4038页

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

The accuracy and stability of relative pose estimation of an autonomous underwater vehicle (AUV) and a target depend on whether the characteristics of the underwater image can be accurately and quickly extracted. In this paper, a whale optimization algorithm (WOA) based on lateral inhibition (LI) is proposed to solve the image matching and vision-guided AUV docking problem. The proposed method is named the LI-WOA. The WOA is motivated by the behavior of humpback whales, and it mainly imitates encircling prey, bubble-net attacking and searching for prey to obtain the globally optimal solution in the search space. The WOA not only balances exploration and exploitation but also has a faster convergence speed, higher calculation accuracy and stronger robustness than other approaches. The lateral inhibition mechanism can effectively perform image enhancement and image edge extraction to improve the accuracy and stability of image matching. The LI-WOA combines the optimization efficiency of theWOA and the matching accuracy of the LI mechanism to improve convergence accuracy and the correct matching rate. To verify its effectiveness and feasibility, the WOA is compared with other algorithms by maximizing the similarity between the original image and the template image. The experimental results show that the LI-WOA has a better average value, a higher correct rate, less execution time and stronger robustness than other algorithms. The LI-WOA is an effective and stable method for solving the image matching and vision-guided AUV docking problem.

关键词： whale optimization algorithm lateral inhibition image matching AUV docking

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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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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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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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Design and Development of a B-Type Inverter for Harmonic Mitigation in a Grid Integrated System Using whale optimization algorithm

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2023年第2期51卷 151-170页

作者： Raj, Devesh Venkatraman, Thiyagarajan Balasubramanian, Muthuselvan Vanaja, Dishore Shunmugham Sri Sivasubramaniya Nadar Coll Engn Elect & Elect Engn Kalavakkam Tamil Nadu India Mar Baselios Coll Engn & Technol Elect & Elect Engn Thiruvananthapuram Kerala India

This paper introduces a B-type inverter and an improved Y source DC-DC converter that use a selective harmonic elimination (SHE) method based on whale optimization to inject power into the grid with reduced harmonic content. The suggested topology contains four unidirectional switches, two bidirectional switches and three DC sources to produce the required output voltage level. The proposed topology can be functioned with equal and unequal DC sources. The developed structure can generate the negative voltages without the use of an H-bridge. It has more redundant states, and it is partially fault tolerant in nature. The proposed structure provides a high value of level to switch ratio. A single input multiple output high gain modular Y-source DC-DC converter is implemented for effective utilization of the PV modules in the suggested system. The triggering angles for the suggested system is generated using the SHE based whale optimization algorithm (WOA). Using the SHE-WOA method a total harmonic distortion (THD) of 4.31% is obtained. The developed system is integrated with the grid and for grid synchronization PI based WOA method is used. Simulation study has identified the presence of only 2.47% of harmonics in the grid current and 2.03% in experimental analysis. Both these harmonic distortions are less than the IEEE-519 standards.

关键词： B-type modular multilevel inverter modular-Y source converter whale optimization algorithm selective harmonic elimination total harmonic distortion (THD)

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A whale optimization algorithm with Distributed Collaboration and Reverse Learning Ability

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Computers, Materials & Continua 2023年第6期75卷 5965-5986页

作者： Zhedong Xu Yongbo Su Fang Yang Ming Zhang School of Architecture and Civil Engineering Anhui Polytechnic UniversityWuhu241000China

Due to the development of digital transformation,intelligent algorithms are getting more and more *** whale optimization algorithm(WOA)is one of swarm intelligence optimization algorithms and is widely used to solve practical engineering optimization ***,with the increased dimensions,higher requirements are put forward for algorithm *** double population whale optimization algorithm with distributed collaboration and reverse learning ability(DCRWOA)is proposed to solve the slow convergence speed and unstable search accuracy of the WOA algorithm in optimization *** the DCRWOA algorithm,the novel double population search strategy is ***,the reverse learning strategy is adopted in the population search process to help individuals quickly jump out of the non-ideal search *** experi-ments are carried out using standard test functions with different dimensions(10,50,100,200).The optimization case of shield construction parameters is also used to test the practical application performance of the proposed *** results show that the DCRWOA algorithm has higher optimization accuracy and stability,and the convergence speed is significantly ***,the proposed DCRWOA algorithm provides a better method for solving practical optimization problems.

关键词： whale optimization algorithm double population cooperation distribution reverse learning convergence speed

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Two-dimensional Otsu multi-threshold image segmentation based on hybrid whale optimization algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第10期82卷 15007-15026页

作者： Ning, Guiying Liuzhou Inst Technol Liuzhou 545616 Guangxi Peoples R China

Threshold segmentation is a commonly used method to deal with image segmentation problems. Aiming at the problems of the traditional maximum inter-class variance method (Otsu) in multi-threshold image segmentation, such as large amount of computation, long computation time and low segmentation accuracy. This paper proposes a two-dimensional Otsu multi-threshold image segmentation algorithm based on hybrid whale optimization algorithm. Firstly, the two-dimensional Otsu single-threshold segmentation method is extended to the two-dimensional Otsu multi-threshold segmentation method to improve the segmentation effect. At the same time, in order to reduce the calculation time and improve the solution accuracy, the new hybrid whale optimization algorithm proposed in this paper is used to calculate the threshold. The test is carried out through a set of classical image threshold segmentation sets, and the widely used image segmentation evaluation standards PSNR and SSIM are used for judgment. The results of this paper are also compared with the results of other novel algorithms, including the results of one-dimensional Otsu multi-threshold segmentation method. The results show that the proposed two-dimensional Otsu single-threshold segmentation improves the segmentation efficiency and quality, it is an effective image segmentation method.

关键词： Maximum inter-class variance algorithm Two-dimensional Otsu Image segmentation Nonlinear convergence factor whale optimization algorithm

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Grey Wolf Optimizer and whale optimization algorithm for Stochastic Inventory Management of Reusable Products in a Two-Level Supply Chain

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IEEE ACCESS 2023年 11卷 40278-40297页

作者： Sadeghi, Amir Hossein Bani, Erfan Amani Fallahi, Ali Handfield, Robert North Carolina State Univ Dept Ind & Syst Engn Raleigh NC 27606 USA Sharif Univ Technol Dept Ind Engn Tehran *** Iran North Carolina State Univ Dept Business Management Raleigh NC 27607 USA

Product reuse and recovery is an efficient tool that helps companies to simultaneously address economic and environmental dimensions of sustainability. This paper presents a novel problem for stock management of reusable products in a single-vendor, multi-product, multi-retailer network. Several constraints, such as the maximum budget, storage capacity, number of orders, etc., are considered in their stochastic form to establish a more realistic problem. The presented problem is formulated using a nonlinear programming mathematical model. The chance-constrained approach is suggested to deal with the constraints' uncertainty. Regarding the nonlinearity of the model, grey wolf optimizer (GWO) and whale optimization algorithm (WOA) as two novel metaheuristics are presented as solution approaches, and the sequential quadratic programming (SQP) exact algorithm validates their performance. The parameters of algorithms are calibrated using the Taguchi method for the design of experiments. Extensive analysis is established by solving several numerical results in different sizes and utilizing several comparison measures. Also, the results are compared statistically using proper parametric and non-parametric tests. The analysis of the results shows a significant difference between the algorithms, and GWO has a better performance for solving the presented problem. In addition, both algorithms perform well in searching the solution space, where the GWO and WOA differences with the optimal solution of the SQP algorithm are negligible.

关键词： Supply chains Inventory management Uncertainty Stochastic processes Metaheuristics whale optimization algorithms Programming Reuse and recovery chance-constrained programming grey wolf optimizer whale optimization algorithm Taguchi method

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