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Dynamic spiral updating whale optimization algorithm for solving optimal power flow problem

JOURNAL OF SUPERCOMPUTING 2023年第17期79卷 19959-20000页

作者： Wang, Fengxian Feng, Shaozhi Pan, Youmei Zhang, Huanlong Bi, Senlin Zhang, Jiaxiang Zhengzhou Univ Light Ind Coll Elect & Informat Engn Ke Xue Ave Zhengzhou 450002 Henan Peoples R China

To address the problems of slow convergence of the traditional whale optimization algorithm and the tendency to fall into local optimal solutions, an improved algorithm based on a combination of elite disturbance opposition-based learning and dynamic spiral updating (OWOA) is proposed, which improves the convergence speed of the algorithm and increases the probability of the algorithm jumping out of the local optimum to find the global optimum. First, an elite disturbance opposition-based learning strategy is used to initialize the whale population to ensure diversity of the population, and then, random perturbation of the elite whales increases the exploratory capacity of the population to avoid falling into local optimal solutions. Secondly, a dynamic spiral update strategy is used to dynamically adjust the shape of the spiral with the number of iterations, avoiding the algorithm from falling into a local optimum at the beginning of the iteration and improving the convergence speed of the algorithm at a later stage, which improves the ability of the algorithm to find the global optimum. The experimental results show that the OWOA algorithm, due to the introduction of the lite disturbance opposition-based learning strategy and dynamic spiral updating strategy, has higher convergence speed and accuracy on single-peak functions, multi-peak functions and multi-dimensional functions compared to other algorithms. Furthermore, good results have been obtained in the practical application of OPF. This shows that our improvements are effective.

关键词： whale optimization algorithm Opposition-based learning Dynamic spiral updating Intelligent optimization algorithm Optimum power flow

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Multi-objective whale optimization algorithm and multi-objective grey wolf optimizer for solving next release problem with developing fairness and uncertainty quality indicators

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APPLIED INTELLIGENCE 2021年第8期51卷 5358-5387页

作者： Ghasemi, Mohsen Bagherifard, Karamollah Parvin, Hamid Nejatian, Samad Pho, Kim-Hung Islamic Azad Univ Yasooj Branch Dept Comp Engn Yasuj Iran Islamic Azad Univ Yasooj Branch Young Researchers & Elite Club Yasuj Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Duy Tan Univ Fac Informat Technol Da Nang 550000 Vietnam Islamic Azad Univ Dept Comp Engn Nourabad Mamasani Branch Nourabad Mamasani Iran Islamic Azad Univ Yasooj Branch Dept Elect Engn Yasuj Iran Ton Duc Thang Univ Fract Calculus Optimizat & Algebra Res Grp Fac Math & Stat Ho Chi Minh City Vietnam

Selecting a set of requirements to implement in the next software release is an NP-Hard problem known as NRP. We propose multi-objective versions of grey wolf optimizer and whale optimization algorithm for solving bi-objective NRP. We used these two algorithms and three other evolutionary algorithms to solve NRP problem instances from four datasets. The cost-to-score ratio and the roulette wheel are used to satisfy constraints of the NRP problem. We compare obtained Pareto fronts based on eight quality indicators. In addition to four general multi-objective optimization quality indicators, the three aspects of fairness among clients and also uncertainty are reconfigured as quality indicators. These quality indicators are computed for a Pareto front. Results show that MOWOA performs better than others and makes requirement selection fairer. MOGWO works better than the rest when budget constraints are reduced.

关键词： Next release problem Grey wolf optimizer whale optimization algorithm Fairness quality indicator Uncertainty size

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HPCP-QCWOA: High Performance Clustering Protocol based on Quantum Clone whale optimization algorithm in Integrated Energy System

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2022年 135卷 315-332页

作者： Liu, Yang Li, Chaoqun Zhang, Yao Xu, Mengying Xiao, Jing Zhou, Jie Shihezi Univ Coll Informat Sci & Technol Shihezi 832000 Peoples R China Univ Cordilleras Baguio 2600 Philippines Beijing Univ Posts & Telecommun Beijing Peoples R China Xinjiang Tianfu Informat Technol Co Ltd E Shihezi 832000 Peoples R China

Combined with highly developed technologies such as cloud computing and the Internet of Things, the integrated energy system (IES) has realized the coordinated scheduling and utilization of various energy resources with its superiority of comprehensive control and high applicability. Meanwhile, approaches to enhance the quality of service (QoS), e.g. network lifetime, energy distribution, and data transmission delay, of an indispensable component of IES, i.e., integrated energy system wireless sensor networks (IESWSNs), have become an urgent research issue. To tackle the issue, a high-performance clustering protocol termed quantum clone whale optimization algorithm (HPCP-QCWOA) is proposed for IESWSNs. By designing a new quantum operator and a new layering clone operator, HPCP-QCWOA can find an approximate optimal clustering scheme to improve the QoS performance of IESWSNs. Unlike other population-based clustering protocols, a new objective function that embraces remaining energy, intra-cluster distance, BS distance, and data transmission delay is designed in the HPCP-QCWOA, which renders its clustering performance more desirable than some other state-of-the-art clustering protocols, i.e., O-LEACH, LDIWPSO, and ARSH-FATI-CHS. Empirically, simulation experiments under different scenarios illustrated that the lifetime of the HPCP-QCWOA-based clustering has increased by 28.78%, 25.50% and 11.22% respectively compared with O-LEACH, LDIWPSO, and ARSH-FATI-CHS. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Integrated energy system Wireless sensor network Clustering Quality of service whale optimization algorithm Quantum optimization

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Automatic breast cancer detection based on optimized neural network using whale optimization algorithm

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2021年第1期31卷 425-438页

作者： Fang, Hong Fan, Hongyu Lin, Shan Qing, Zhang Sheykhahmad, Fatima Rashid Dalian Univ Affiliated Zhongshan Hosp Dept Breast Dalian Peoples R China Islamic Azad Univ Ardabil Branch Young Researchers & Elite Club Ardebil Iran

Breast cancer is the second deadliest type of cancer. Early detection of breast cancer can considerably improve the effectiveness of treatment. A significant early sign of breast cancer is the mass. However, separating the cancerous masses from the normal portions of the breast tissue is usually a challenge for radiologists. Recently, because of the availability of high-accuracy computing, computer-aided detection systems based on image processing have become capable of accurately diagnosing the various types of cancers. The main purpose of this study is to utilize a powerful image segmentation method for the diagnosis of cancerous regions through mammography, based on a new configuration of the multilayer perceptron (MLP) neural network. The most popular method for minimizing the errors in an MLP neural network is backpropagation. However, this method has certain drawbacks, such as a low convergence speed and becoming trapped at the local minimum. In this study, a new training algorithm based on the whale optimization algorithm is proposed for the MLP network. This algorithm is capable of solving various problems toward the current algorithms for the analyzed systems. The proposed method is validated on the Mammographic Image Analysis Society database, which contains 322 digitized mammography images, and the Digital Database for Screening Mammography, which contains approximately 2500 digitized mammography images. To assess the detection performance of the proposed system, the correct detection rate, percentage of identification with false acceptance, and percentage of identification with false rejection were evaluated and compared using various methods. The results indicate that the proposed method is highly efficient and yields significantly better accuracy compared with other methods.

关键词： breast cancer computer-aided diagnosis image segmentation neural networks whale optimization algorithm

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Refraction-learning-based whale optimization algorithm for high-dimensional problems and parameter estimation of PV model

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2020年第0期89卷 103457-000页

作者： Long, Wen Wu, Tiebin Jiao, Jianjun Tang, Mingzhu Xu, Ming Guizhou Univ Finance & Econ Key Lab Econ Syst Simulat Guiyang 550025 Guizhou Peoples R China Hunan Univ Humanities Sci Technol Dept Energy & Elect Engn Loudi 417000 Hunan Peoples R China Guizhou Univ Finance & Econ Sch Math & Stat Guiyang 550025 Guizhou Peoples R China Changsha Univ Sci & Technol Sch Energy Power & Engn Changsha 410114 Hunan Peoples R China

whale optimization algorithm (WOA) is a relatively new meta-heuristic optimization algorithm which mimics the hunting behavior of humpback whales. This paper presents a modified version of WOA, called RLWOA, for solving high-dimensional optimization problems. The proposed RLWOA adopts a modified conversion parameter update rule that relies on Logistic model to balance between diversity and convergence during the search process, and a new refraction-learning strategy based on the principle of refraction of light is proposed to help the population jump out of a local optimum. The experiments on a set of benchmark test functions with various features, i.e., 12 widely used benchmark functions with 100, 1000, and 10000 dimensions, two practical engineering design problems, and parameter estimation problem of photovoltaic model. The comparisons demonstrate that the proposed RLWOA shows better or at least competitive performance against the standard WOA, WOA variants and other state-of-the-art meta-heuristic algorithms for solving high-dimensional numerical optimization, practical engineering design optimization, and photovoltaic model parameter estimation problems.

关键词： whale optimization algorithm Refraction learning High-dimensional optimization Engineering design optimization Photovoltaic model Parameter estimation

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Multilevel thresholding image segmentation based on improved volleyball premier league algorithm using whale optimization algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第8期80卷 12435-12468页

作者： Abd Elaziz, Mohamed Nabil, Neggaz Moghdani, Reza Ewees, Ahmed A. Cuevas, Erik Lu, Songfeng Zagazig Univ Dept Math Fac Sci Zagazig Egypt Univ Sci & Technol Oran Mohammed Boudiaf USTO MB Fac Math & Informat Dept InformatLab SIMPA BP 1505 El Mnaouer 31000 Oran Algeria Persian Gulf Univ Ind Management Dept Boushehr Iran Damietta Univ Dept Comp Dumyat Egypt Univ Guadalajara CUCEI Dept Elect Av Revoluc 1500 Guadalajara 44430 Jalisco Mexico Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Wuhan 430074 Peoples R China

Multilevel thresholding image segmentation has received considerable attention in several image processing applications. However, the process of determining the optimal threshold values (as the preprocessing step) is time-consuming when traditional methods are used. Although these limitations can be addressed by applying metaheuristic methods, such approaches may be idle with a local solution. This study proposed an alternative multilevel thresholding image segmentation method called VPLWOA, which is an improved version of the volleyball premier league (VPL) algorithm using the whale optimization algorithm (WOA). In VPLWOA, the WOA is used as a local search system to improve the learning phase of the VPL algorithm. A set of experimental series is performed using two different image datasets to assess the performance of the VPLWOA in determining the values that may be optimal threshold, and the performance of this algorithm is compared with other approaches. Experimental results show that the proposed VPLWOA outperforms the other approaches in terms of several performance measures, such as signal-to-noise ratio and structural similarity index.

关键词： Image segmentation Multilevel thresholding Swarm algorithm Volleyball premier league algorithm whale optimization algorithm

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Application of improved hybrid whale optimization algorithm to optimization problems

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NEURAL COMPUTING & APPLICATIONS 2023年第17期35卷 12433-12451页

作者： Uzer, Mustafa Serter Inan, Onur Selcuk Univ Ilgin Vocat Sch Elect & Automat Konya Turkiye Selcuk Univ Fac Technol Comp Engn Konya Turkiye

The whale optimization algorithm (WOA) is one of the recent meta-heuristic algorithms. WOA has advantages such as an exploration mechanism that leads towards the global optimum, a suitable balance between exploration and exploitation that avoids the local optimum, and a very good exploitation capability. In this study, five new hybrid algorithms are proposed to develop these advantages. Two of them are developed by combining WOA and Particle Swarm optimization (PSO) algorithms, and three of them are developed by adding the Levy flight algorithm to this combination in different ways. The proposed algorithms have been tested with 23 mathematical optimization problems, and in order to make a more accurate comparison, the average optimization results and corresponding standard deviation results are calculated by running these algorithms 30 times for each optimization problem. The proposed algorithms' performances were evaluated among themselves, and the WOALFVWPSO algorithm performed better among these algorithms. This proposed algorithm has been first compared with WOA and PSO, then with other algorithms in the literature. According to WOA and PSO, the proposed algorithm performs better in 19 of 23 mathematical optimization problems, and according to other literature, it performs better in 15 of 23 problems. Also, the proposed algorithm has been applied to the pressure vessel design engineering problem and achieved the best result compared to other algorithms in the literature. It has been proven that the WOALFVWPSO algorithm provides competitive solutions for most optimization problems when compared to meta-heuristic algorithms in the literature.

关键词： whale optimization algorithm Particle swarm optimization Levy flight optimization problems Benchmark functions

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Retinal fundus vasculature multilevel segmentation using whale optimization algorithm

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SIGNAL IMAGE AND VIDEO PROCESSING 2018年第2期12卷 263-270页

作者： Hassan, Gehad Hassanien, Aboul Ella Fayoum Univ Fac Comp & Informat Faiyum Egypt Cairo Univ Fac Comp & Informat Giza Egypt

The aim was to present a novel automated approach for extracting the vasculature of retinal fundus images. The proposed vasculature extraction method on retinal fundus images consists of two phases: preprocessing phase and segmentation phase. In the first phase, brightness enhancement is applied for the retinal fundus images. For the vessel segmentation phase, a hybrid model of multi-level thresholding along with whale optimization algorithm (WOA) is performed. WOA is used to improve the segmentation accuracy through finding the n-1 optimal n-level threshold on the fundus image. To evaluate the accuracy, sensitivity, specificity, accuracy, receiver operating characteristic (ROC) curve analysis measurements are used. The proposed approach achieved an overall accuracy of 97.8%, sensitivity of 88.9%, and specificity of 98.7% for the identification of retinal blood vessels by using a dataset that was collected from Bostan diagnostic center in Fayoum city. The area under the ROC curve reached a value of 0.967. Automated identification of retinal blood vessels based on whale algorithm seems highly successful through a comprehensive optimization process of operational parameters.

关键词： whale optimization algorithm Vessel segmentation Swarm optimization Multilevel segmentation Threshold

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The effects on classifier performance of 2D discrete wavelet transform analysis and whale optimization algorithm for recognition of power quality disturbances

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COGNITIVE SYSTEMS RESEARCH 2022年 75卷 1-15页

作者： Karasu, Seckin Sarac, Zehra Zonguldak Bulent Ecevit Univ Elect & Elect Engn Dept TR-67100 Zonguldak Turkey Istanbul Medeniyet Univ Elect & Elect Engn Dept TR-34700 Istanbul Turkey

Power Quality (PQ) is becoming more and more important day by day in the electric network. Signal processing, pattern recognition and machine learning are increasingly being studied for the automatic recognition of any disturbances that may occur during the generation, transmission, and distribution of electricity. There are three main steps to identify the PQ disturbances. These include the use of signal processing methods to calculate the features representing the disturbances, the selection of those that are more useful than these feature sets to prevent the creation of a complex classification model, the creating a classification model that recognizes multiple classes using the selected feature subsets. In this study, one-dimensional (1D) PQ disturbances signals are transformed into two-dimensional (2D) signals, 2D discrete wavelet transforms (2D-DWT) are used to extract the features. The features are extracted by using the wavelet families such as Daubechies, Biorthogonal, Symlets, Coiflets and Fejer-Korovkin in 2D-DWT to analyze PQ disturbances. whale optimization algorithm (WOA) and k-nearest neighbor (KNN) classifier determine the feature subsets. Then, WOA and k nearest neighbor (KNN) classifier are used to determine the feature group. By using KNN and Support Vector Machines (SVM) classifi-cation methods, Classifier models that distinguish PQ disturbances are formed. The main aim of the study is to determine the features derived from 2D wavelet coefficients for different wavelet families and to determine which of them has a better classification performance to distinguish PQ disturbances signals. At the same time, different classification methods are simulated and a model which can classify PQ disturbances signals with high performance is created. Also, the generated models are analysed for their performance in terms of different noise levels (40 dB, 30 dB, 20 dB). The result of this simulation study shows that the model developed to classify PQ disturbanc

关键词： Power quality disturbances 2D discrete wavelet transform Wavelet families whale optimization algorithm 2D signal processing Evolutionary feature selection

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CWOA: A novel complex-valued encoding whale optimization algorithm

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MATHEMATICS AND COMPUTERS IN SIMULATION 2023年第1期207卷 151-188页

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

The whale optimization algorithm (WOA) is inspired by humpback whales' bubble-net assaulting mechanism and imitates behaviors such as shrinking and enveloping prey, spiral bubble-net assaulting prey, and variational searching for prey to determine the best solution. However, the basic WOA has the disadvantages of low computation precision, slow convergence rate and easily falling into search stagnation. To strengthen the optimization quality and search reliability, this paper presents a distinctive complex-valued encoding WOA (CWOA) to satisfy the function optimization and engineering design. The complex -valued methodology utilizes a diploid structure to encode individual whales, and the real and imaginary components are added to the basic WOA to revise the position of each humpback whale, which converts the two-dimensional encoding area to a one-dimensional expression area and utilizes the real and imaginary components to illustrate a feasible solution with inherent parallelism. This methodology enriches the population diversity, furthers the individual information, elevates the general search ability, avoids premature convergence and promotes convergence efficiency. The CWOA not only utilizes the characteristics of the complex-valued methodology to avoid slipping into local optima but also exhibits excellent adaptability and robustness to determine the accurate value. Sixteen benchmark test functions and eight engineering designs are used to verify the applicability and practicability of the CWOA. The experimental results demonstrate that the optimization productivity and search performance of the CWOA are superior to those of other algorithms. In addition, the CWOA is a more successful and efficacious method that completely stabilizes exploration and exploitation to establish a quicker convergence rate, higher computation precision, greater resilience and stability.(c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published

关键词： whale optimization algorithm Complex-valued encoding Benchmark test functions Engineering design

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