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Retinal fundus vasculature multilevel segmentation using whale optimization algorithm

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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Hybrid whale optimization algorithm based on symbiosis strategy for global optimization

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APPLIED INTELLIGENCE 2023年第13期53卷 16663-16705页

作者： Li, Maodong Xu, Guang-hui Zeng, Liang Lai, Qiang Hubei Univ Technol Sch Elect & Elect Engn Hubei Key Lab High efficiency Utilizat Solar Energ Wuhan 430068 Peoples R China East China Jiaotong Univ Sch Elect & Automation Engn Nanchang 330013 Peoples R China

The whale optimization algorithm (WOA) is a simple structured and easily implemented swarm-based algorithm inspired by the unique bubble-net feeding method of humpback whales. Past studies have shown that WOA performs well in a number of optimization problems. However, it is difficult for WOA to completely free itself from the problems of insufficient convergence accuracy and premature convergence when solving global optimization problems. To address these issues, a hybrid whale optimization algorithm based on symbiotic strategy (HWOAMS) is proposed in this paper. The main idea of the proposed method is to combine the improved symbiotic organisms search algorithm (SOS) with the whale optimization algorithm thus enhancing the search ability of WOA. First, an improved symbiotic phase based on Levy flight and chaos strategy is introduced into the exploration process to enhance the global search capability;Second, an improved mutualism phase based on Brownian motion is used instead of the original shrinking encircling phase to achieve better local exploitation. Third, an improved parasitic phase based on a modified global optimal spiral operator strategy is embedded in the spiral updating position phase to help the algorithm further improve the exploitation efficiency and convergence accuracy. Finally, a global search strategy is proposed to help the algorithm better balance exploration and exploitation. To establish the effectiveness of the new algorithm, extensive simulation experiments are conducted on HWOAMS using the classical function test set, the CEC 2019 function set and four classical engineering problems. Numerical evaluation results indicate that HWOAMS outperforms 18 other algorithms in terms of local optimum avoidance ability and convergence accuracy in a majority of cases, and has better search performance.

关键词： whale optimization algorithm Symbiotic organisms search algorithm Diversity Global optimization problem

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Joint Compressed Sensing and Enhanced whale optimization algorithm for Pilot Allocation in Underwater Acoustic OFDM Systems

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IEEE ACCESS 2019年 7卷 95779-95796页

作者： Jiang, Rongkun Wang, Xuetian Cao, Shan Zhao, Jiafei Li, Xiaoran Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Shanghai Univ Dept Commun & Informat Engn Shanghai 200444 Peoples R China

In underwater acoustic-orthogonal frequency division multiplexing (UWA-OFDM) systems, the performance of channel estimation is significantly affected by pilot allocation in the framework of compressed sensing (CS). However, for optimizing the pilot allocation, an exhaustive search method over all possible allocations is computationally prohibitive and random search method may not ensure convergence accuracy. In this paper, the meta-heuristic algorithm of the whale optimization algorithm (WOA) is employed to address this issue. For reinforcing the capability of balancing exploration and exploitation, an enhanced variant of WOA termed EWOA is presented with four optimization strategies. After that, a joint algorithm combining CS with EWOA (CS-EWOA) is proposed for pilot allocation in UWA-OFDM systems. Through extensive simulations, the improvement of EWOA is demonstrated on the majority of benchmark functions over other well-known meta-heuristic algorithms. With regard to bit error rate (BER) and mean square error (MSE) for channel estimation, the proposed CS-EWOA algorithm outperforms the equispaced, random, genetic algorithm (GA), particle swarm optimization (PSO), and WOA-based pilot allocation methods. Moreover, it is robust with varying system subcarriers and channel models. Furthermore, the CS-EWOA exhibits superior convergence performance without increasing the computational complexity compared with the GA-, PSO-, and WOA-based methods in the iteration process of pilot allocation optimization. It can be concluded from the simulation results that the proposed CS-EWOA algorithm is competitive to optimize pilot allocation for channel estimation in UWA-OFDM systems.

关键词： Compressed sensing OFDM whale optimization algorithm underwater acoustic communication channel estimation pilot allocation

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Sunflower whale optimization algorithm for Resource Allocation Strategy in Cloud Computing Platform

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WIRELESS PERSONAL COMMUNICATIONS 2021年第4期116卷 3061-3080页

作者： Subhash, Ligade Sunil Udayakumar, R. Bharath Univ Bharath Inst Higher Educ & Res Dept Comp Sci & Engn Chennai Tamil Nadu India

Cloud computing environment supply the computing resources based on the demand of cloud user requirements. It builds the resource allocation model through distributed computing and virtualization to emphasize the scalability of cloud services. However, to manage the demand of user creates a complex issue in the on-demand resource allocation framework. Therefore, an effective optimization algorithm named Sunflower whale optimization algorithm (SFWOA) is proposed to solve the issues in the resource allocation model. The concept of virtualization helps to execute the tasks based on the availability of resources and reduces the response time. The tasks are allocated to the virtual machine in a distributed manner to balance the workload in cloud. The proposed SFWOA uses the hunting strategy and the foraging behavior of humpback whale along with the peculiar behavior of sunflower to achieve the effective resource allocation. The performance enhancement of the proposed SFWOA is revealed through the performance measures such that the proposed method attained a maximum resource utilization of 0.942 using 20 virtual machines, maximum memory utilization of 0.215, and maximum CPU utilization of 0.269 using 15 virtual machines, and minimum skewness of 0.001 using 25 virtual machines.

关键词： Virtualization Cloud computing Sunflower optimization whale optimization algorithm Cloud services

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Parameter Identification of Jiles-Atherton Model Based on Levy whale optimization algorithm

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IEEE ACCESS 2022年 10卷 66711-66721页

作者： Chen, Zhigang Yu, Yue Wang, Yanxue Beijing Univ Civil Engn & Architecture Dept Mech & Elect & Vehicle Engn Beijing 100044 Peoples R China Beijing Engn Res Ctr Monitoring Construct Safety Beijing 100044 Peoples R China

The Jiles-Atherton model is key to researching the hysteresis loop. The focus of scholars across various countries has always been the parameter identification of the Jiles-Atherton model. This paper on the Levy whale optimization algorithm (LWOA), based on the whale optimization algorithm (WOA), proposes to overcome the disadvantage that WOA tends to involve the local optimum. The recommended algorithm uses the Levy flight strategy instead of the encircling prey policy since the former improves the global search. Therefore, the new algorithm is better at stability and calculation accuracy. To substantiate the efficacy of the proposed algorithm, it is tested against six benchmark functions and compared with the WOA, particle swarm optimization (PSO), grey wolf algorithm (GWO), and shuffled frog leaping algorithm (SFLA). In addition, the proposed algorithm is applied to realize two classical engineering problems, such as the tension/compression spring and welded beam design issues. The experimental findings reveal that the proposed algorithm is highly competitive with metaheuristic optimizers and improves the algorithm's performance. To address the poor stability of the J-A model parameter identification, an improved calculation method for parameter k and the reduced parameter ranges of the model parameters a and alpha were combined with LWOA. The proposed algorithm is called C-LWOA, which is compared with LWOA, PSO, GWO, SFLA, and the cuckoo search (CS) based on the data reported in the literature. Moreover, the simulation results demonstrate that the stability and calculation accuracy of the parameter identification by the C-LWOA was significantly strengthened. Equally important, the calculation error was within 0.2%. Finally, the proposed algorithm was subsequently used to fit the actual measurements of the hysteresis loop of permalloy.

关键词： Jiles-Atherton model hysteresis loop levy flight whale optimization algorithm

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Enhancing whale optimization algorithm with Chaotic Theory for Permutation Flow Shop Scheduling Problem

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2021年第1期14卷 651-675页

作者： Li, Jiang Guo, Lihong Li, Yan Liu, Chang Wang, Lijuan Hu, Hui Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China Jiangsu Univ Sci & Technol Sch Comp Zhenjiang 212003 Jiangsu Peoples R China Huizhou Univ Dept Iformat Sci & Technol Huizhou 516007 Guangdong Peoples R China

The permutation flow shop scheduling problem (PFSSP) is a typical production scheduling problem and it has been proved to be a nondeterministic polynomial (NP-hard) problem when its scale is larger than 3. The whale optimization algorithm (WOA) is a new swarm intelligence algorithm which performs well for PFSSP. But the stability is still low, and the optimization results are not too good. On this basis, we optimize the parameters of WOA through chaos theory, and put forward a chaotic whale algorithm (CWA). Firstly, in this paper, the proposed CWA is combined with Nawaz-Ensco-Ham (NEH) and largest-rank-value (LRV) rule to initialize the population. Next, chaos theory is applied to WOA algorithm to improve its convergence speed and stability. On this basis, we also use cross operator and reversal-insertion operator to enhance the search ability of the algorithm. Finally, the improved local search algorithm is used to optimize the job sequence to find the minimum makespan. In several experiments, different benchmarks are used to investigate the performance of CWA. The experimental results show that CWA has better performance than other scheduling algorithms. (C) 2021 The Authors. Published by Atlantis Press B.V.

关键词： whale optimization algorithm Chaotic maps Flow shop scheduling Makespan Local search Cross selection

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Fuzzy Automatic Disturbance Rejection Control of Quadrotor UAV Based on Improved whale optimization algorithm

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IEEE ACCESS 2023年 11卷 69117-69130页

作者： Shan, Zebiao Wang, Yuhang Liu, Xiaosong Wei, Changbin Changchun Univ Sci & Technol Sch Elect & Informat Engn Changchun 130022 Peoples R China Jilin Univ Coll Commun Engn Changchun 130022 Peoples R China Changchun Meteorol Instrument Res Inst Changchun 130102 Peoples R China

With the aim of improving the rapidity and accuracy of quadrotor UAV positional trajectory tracking, this paper proposes a fuzzy automatic disturbance rejection control of quadrotor UAV based on improved whale optimization algorithm (IWOA-Fuzzy-ADRC). (1) To address the problems of low accuracy and slow convergence of the traditional whale algorithm, a combination of logistic chaos mapping and skew tent mapping is applied to improve the diversity of the initial population. (2) The global search capability is strengthened to avoid falling into local optimum through introducing the cross operator and Gaussian variational operators. (3) The IWOA is employed to iteratively optimize the adjustment coefficients of the fuzzy active disturbance rejection controller, the calculus gain of the nonlinear state error feedback control (NLSEF), and the error correction coefficients of the expansive state observer (ESO). The simulation results demonstrate that compared with the traditional ADRC controller and PID controller, the proposed IWOA-Fuzzy-ADRC controller can effectively improve the control performance of the system, accurately track the desired flight trajectory, accelerate the dynamic response of the control system, reduce the steady-state error, and enhance the anti-interference capability.

关键词： Quadrotors Control systems Heuristic algorithms Observers Autonomous aerial vehicles whale optimization algorithms Rotors Fuzzy systems Chaotic communication INDEX TERMS Gaussian processes UAV trajectory tracking fuzzy ADRC whale optimization algorithm chaotic mapping cross-operator Gaussian variant operators

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Extreme learning machine optimized by whale optimization algorithm using insulated gate bipolar transistor module aging degree evaluation

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 127卷 58-67页

作者： Li, Ling-Ling Sun, Jin Tseng, Ming-Lang Li, Zhi-Gang Hebei Univ Technol Sch Elect Engn State Key Lab Reliabil & Intelligence Elect Equip Tianjin 300130 Peoples R China Hebei Univ Technol Sch Elect Engn Key Lab Electromagnet Field & Elect Apparatus Rel Tianjin 300130 Peoples R China Asia Univ Inst Innovat & Circular Econ Suwon South Korea China Med Univ Dept Med Res Taichung Taiwan

This study focuses on the aging evaluation of Insulated gate bipolar transistor (IGBT) modules to ensure their stability during operation. An aging degree evaluation model is proposed based on whale optimization algorithm optimized extreme learning machine (WOA-ELM) algorithm. This study is mainly concentrated on two aspects. One is to use WOA to optimize the input weights and hidden layer biases of ELM to improve its prediction performance. This study tested the performance of WOA-ELM on several benchmark datasets. The results show that the prediction performance of WOA-ELM is better than ELM, genetic algorithm optimized ELM, cuckoo search optimized ELM, and dandelion algorithm optimized ELM. The other is to measure the electrical and thermal characteristic data of IGBT module under different aging conditions by accelerated aging test. Based on the analysis of the experimental data under different aging degrees, a method for evaluating the aging degree of IGBT modules based on WOA-ELM is proposed. Simulation results based on experimental data show that WOA-ELM still has better accuracy and generalization performance than others. In summary, the WOA-ELM algorithm is applicable to the aging evaluation method of IGBT modules proposed in this study which has good practical value. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Insulated gate bipolar transistor module Aging degree evaluation Extreme learning machine whale optimization algorithm

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