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Sannasi Chakravarthy SR, Bharanidharan N, Vinoth Kumar V, Mahesh TR, Alqahtani MS, Guluwadi S. Deep transfer learning with fuzzy ensemble approach for the early detection of breast cancer. BMC Med Imaging 2024; 24:82. [PMID: 38589813 DOI: 10.1186/s12880-024-01267-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 03/30/2024] [Indexed: 04/10/2024] Open
Abstract
Breast Cancer is a significant global health challenge, particularly affecting women with higher mortality compared with other cancer types. Timely detection of such cancer types is crucial, and recent research, employing deep learning techniques, shows promise in earlier detection. The research focuses on the early detection of such tumors using mammogram images with deep-learning models. The paper utilized four public databases where a similar amount of 986 mammograms each for three classes (normal, benign, malignant) are taken for evaluation. Herein, three deep CNN models such as VGG-11, Inception v3, and ResNet50 are employed as base classifiers. The research adopts an ensemble method where the proposed approach makes use of the modified Gompertz function for building a fuzzy ranking of the base classification models and their decision scores are integrated in an adaptive manner for constructing the final prediction of results. The classification results of the proposed fuzzy ensemble approach outperform transfer learning models and other ensemble approaches such as weighted average and Sugeno integral techniques. The proposed ResNet50 ensemble network using the modified Gompertz function-based fuzzy ranking approach provides a superior classification accuracy of 98.986%.
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Affiliation(s)
- S R Sannasi Chakravarthy
- Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Sathyamangalam, India
| | - N Bharanidharan
- School of Computer Science Engineering and Information systems, Vellore Institute of Technology, Vellore, 632014, India
| | - V Vinoth Kumar
- School of Computer Science Engineering and Information systems, Vellore Institute of Technology, Vellore, 632014, India
| | - T R Mahesh
- Department of Computer Science and Engineering JAIN (Deemed-to-be University), Bengaluru, 562112, India
| | - Mohammed S Alqahtani
- Radiological Sciences Department, College of Applied Medical Sciences, King Khalid University, Abha, 61421, Saudi Arabia
| | - Suresh Guluwadi
- Adama Science and Technology University, Adama, 302120, Ethiopia.
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Zhao X, Liu L, Heidari AA, Chen Y, Ma BJ, Chen H, Quan S. An enhanced ant colony optimizer with Cauchy-Gaussian fusion and novel movement strategy for multi-threshold COVID-19 X-ray image segmentation. Front Neuroinform 2023; 17:1126783. [PMID: 37006638 PMCID: PMC10064065 DOI: 10.3389/fninf.2023.1126783] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 02/16/2023] [Indexed: 03/19/2023] Open
Abstract
The novel coronavirus pneumonia (COVID-19) is a respiratory disease of great concern in terms of its dissemination and severity, for which X-ray imaging-based diagnosis is one of the effective complementary diagnostic methods. It is essential to be able to separate and identify lesions from their pathology images regardless of the computer-aided diagnosis techniques. Therefore, image segmentation in the pre-processing stage of COVID-19 pathology images would be more helpful for effective analysis. In this paper, to achieve highly effective pre-processing of COVID-19 pathological images by using multi-threshold image segmentation (MIS), an enhanced version of ant colony optimization for continuous domains (MGACO) is first proposed. In MGACO, not only a new move strategy is introduced, but also the Cauchy-Gaussian fusion strategy is incorporated. It has been accelerated in terms of convergence speed and has significantly enhanced its ability to jump out of the local optimum. Furthermore, an MIS method (MGACO-MIS) based on MGACO is developed, where it applies the non-local means, 2D histogram as the basis, and employs 2D Kapur’s entropy as the fitness function. To demonstrate the performance of MGACO, we qualitatively analyze it in detail and compare it with other peers on 30 benchmark functions from IEEE CEC2014, which proves that it has a stronger capability of solving problems over the original ant colony optimization for continuous domains. To verify the segmentation effect of MGACO-MIS, we conducted a comparison experiment with eight other similar segmentation methods based on real pathology images of COVID-19 at different threshold levels. The final evaluation and analysis results fully demonstrate that the developed MGACO-MIS is sufficient to obtain high-quality segmentation results in the COVID-19 image segmentation and has stronger adaptability to different threshold levels than other methods. Therefore, it has been well-proven that MGACO is an excellent swarm intelligence optimization algorithm, and MGACO-MIS is also an excellent segmentation method.
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Affiliation(s)
- Xiuzhi Zhao
- College of Artificial Intelligence, Zhejiang Industry & Trade Vocational College, Wenzhou, Zhejiang, China
| | - Lei Liu
- College of Computer Science, Sichuan University, Chengdu, Sichuan, China
- *Correspondence: Lei Liu,
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Yi Chen
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou, China
| | - Benedict Jun Ma
- Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Huiling Chen
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou, China
- Huiling Chen,
| | - Shichao Quan
- Department of Big Data in Health Science, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Key Laboratory of Intelligent Treatment and Life Support for Critical Diseases of Zhejiang Province, Wenzhou, China
- Zhejiang Engineering Research Center for Hospital Emergency and Process Digitization, Wenzhou, China
- Shichao Quan,
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Xu B, Heidari AA, Cai Z, Chen H. Dimensional decision covariance colony predation algorithm: global optimization and high−dimensional feature selection. Artif Intell Rev 2023. [DOI: 10.1007/s10462-023-10412-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
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4
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Globally automatic fuzzy clustering for probability density functions and its application for image data. APPL INTELL 2023. [DOI: 10.1007/s10489-023-04470-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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Wang M, Chen L, Heidari AA, Chen H. Fireworks explosion boosted Harris Hawks optimization for numerical optimization: Case of classifying the severity of COVID-19. Front Neuroinform 2023; 16:1055241. [PMID: 36760338 PMCID: PMC9905796 DOI: 10.3389/fninf.2022.1055241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 12/13/2022] [Indexed: 01/26/2023] Open
Abstract
Harris Hawks optimization (HHO) is a swarm optimization approach capable of handling a broad range of optimization problems. HHO, on the other hand, is commonly plagued by inadequate exploitation and a sluggish rate of convergence for certain numerical optimization. This study combines the fireworks algorithm's explosion search mechanism into HHO and proposes a framework for fireworks explosion-based HHo to address this issue (FWHHO). More specifically, the proposed FWHHO structure is comprised of two search phases: harris hawk search and fireworks explosion search. A search for fireworks explosion is done to identify locations where superior hawk solutions may be developed. On the CEC2014 benchmark functions, the FWHHO approach outperforms the most advanced algorithms currently available. Moreover, the new FWHHO framework is compared to four existing HHO and fireworks algorithms, and the experimental results suggest that FWHHO significantly outperforms existing HHO and fireworks algorithms. Finally, the proposed FWHHO is employed to evolve a kernel extreme learning machine for diagnosing COVID-19 utilizing biochemical indices. The statistical results suggest that the proposed FWHHO can discriminate and classify the severity of COVID-19, implying that it may be a computer-aided approach capable of providing adequate early warning for COVID-19 therapy and diagnosis.
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Affiliation(s)
- Mingjing Wang
- School of Computer Science and Engineering, Southeast University, Nanjing, China,The Key Laboratory of Computer Network and Information Integration, Southeast University, Ministry of Education, Nanjing, China
| | - Long Chen
- School of Computer Science and Engineering, Southeast University, Nanjing, China,The Key Laboratory of Computer Network and Information Integration, Southeast University, Ministry of Education, Nanjing, China,*Correspondence: Long Chen ✉
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Huiling Chen
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China,Huiling Chen ✉
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Long W, Jiao J, Liang X, Xu M, Wu T, Tang M, Cai S. A velocity-guided Harris hawks optimizer for function optimization and fault diagnosis of wind turbine. Artif Intell Rev 2023; 56:2563-2605. [PMID: 35909648 PMCID: PMC9309607 DOI: 10.1007/s10462-022-10233-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/04/2022] [Indexed: 01/08/2023]
Abstract
Harris hawks optimizer (HHO) is a relatively novel meta-heuristic approach that mimics the behavior of Harris hawk over the process of predating the rabbits. The simplicity and easy implementation of HHO have attracted extensive attention of many researchers. However, owing to its capability to balance between exploration and exploitation is weak, HHO suffers from low precision and premature convergence. To tackle these disadvantages, an improved HHO called VGHHO is proposed by embedding three modifications. Firstly, a novel modified position search equation in exploitation phase is designed by introducing velocity operator and inertia weight to guide the search process. Then, a nonlinear escaping energy parameter E based on cosine function is presented to achieve a good transition from exploration phase to exploitation phase. Thereafter, a refraction-opposition-based learning mechanism is introduced to generate the promising solutions and helps the swarm to flee from the local optimal solution. The performance of VGHHO is evaluated on 18 classic benchmarks, 30 latest benchmark tests from CEC2017, 21 benchmark feature selection problems, fault diagnosis problem of wind turbine and PV model parameter estimation problem, respectively. The simulation results indicate that VHHO has higher solution quality and faster convergence speed than basic HHO and some well-known algorithms in the literature on most of the benchmark and real-world problems.
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Affiliation(s)
- Wen Long
- Guizhou Key Laboratory of Big Data Statistical, Guizhou University of Finance and Economics, Guiyang, 550025 China
- Guizhou Key Laboratory of Economics System Simulation, Guizhou University of Finance and Economics, Guiyang, 550025 China
| | - Jianjun Jiao
- School of Mathematics and Statistics, Guizhou University of Finance and Economics, Guiyang, 550025 China
| | - Ximing Liang
- School of Science, Beijing University of Civil Engineering and Architecture, Beijing, 100044 China
| | - Ming Xu
- School of Mathematics and Statistics, Guizhou University of Finance and Economics, Guiyang, 550025 China
| | - Tiebin Wu
- School of Energy and Electrical Engineering, Hunan University of Humanities Science and Technology, Loudi, 417000 China
| | - Mingzhu Tang
- School of Energy Power and Engineering, Changsha University of Science and Technology, Changsha, 410114 China
| | - Shaohong Cai
- Guizhou Key Laboratory of Big Data Statistical, Guizhou University of Finance and Economics, Guiyang, 550025 China
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7
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Gao H, Zhang Z, Wang S, Sun H. Underdetermined blind source separation method based on quantum Archimedes optimization algorithm. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03962-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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8
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Zhao X, Zhu L, Wu B. An improved mayfly algorithm based on Kapur entropy for multilevel thresholding color image segmentation. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-221161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Multilevel thresholding segmentation of color images plays an important role in many fields. The pivotal procedure of this technique is determining the specific threshold of the images. In this paper, an improved mayfly algorithm (IMA)-based color image segmentation method is proposed. Tent mapping initializes the female mayfly population to increase population diversity. Lévy flight is introduced in the wedding dance iterative formulation to make IMA jump from the local optimal solution quickly. Two nonlinear coefficients were designed to speed up the convergence of the algorithm. To better verify the effectiveness, eight benchmark functions are used to test the performance of IMA. The average fitness value, standard deviation, and Wilcoxon rank sum test are used as evaluation metrics. The results show that IMA outperforms the comparison algorithm in terms of search accuracy. Furthermore, Kapur entropy is used as the fitness function of IMA to determine the segmentation threshold. 10 Berkeley images are segmented. The best fitness value, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and other indexes are used to evaluate the effect of segmented images. The results show that the IMA segmentation method improves the segmentation accuracy of color images and obtains higher quality segmented images.
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Affiliation(s)
- Xiaohan Zhao
- College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, Heilongjiang, China
- School of Electrical Engineering, Suihua University, Suihua, Heilongjiang, China
| | - Liangkuan Zhu
- College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, Heilongjiang, China
| | - Bowen Wu
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China
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Zou L, Zhou S, Li X. An Efficient Improved Greedy Harris Hawks Optimizer and Its Application to Feature Selection. ENTROPY 2022; 24:e24081065. [PMID: 36010729 PMCID: PMC9407072 DOI: 10.3390/e24081065] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/21/2022] [Accepted: 07/30/2022] [Indexed: 01/27/2023]
Abstract
To overcome the lack of flexibility of Harris Hawks Optimization (HHO) in switching between exploration and exploitation, and the low efficiency of its exploitation phase, an efficient improved greedy Harris Hawks Optimizer (IGHHO) is proposed and applied to the feature selection (FS) problem. IGHHO uses a new transformation strategy that enables flexible switching between search and development, enabling it to jump out of local optima. We replace the original HHO exploitation process with improved differential perturbation and a greedy strategy to improve its global search capability. We tested it in experiments against seven algorithms using single-peaked, multi-peaked, hybrid, and composite CEC2017 benchmark functions, and IGHHO outperformed them on optimization problems with different feature functions. We propose new objective functions for the problem of data imbalance in FS and apply IGHHO to it. IGHHO outperformed comparison algorithms in terms of classification accuracy and feature subset length. The results show that IGHHO applies not only to global optimization of different feature functions but also to practical optimization problems.
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11
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Li S, Wang JS, Song HM, Zheng Y, Zhang XY. Buoyancy energy driven archimedes optimization algorithm based on Lévy flight and tangent flight. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-221039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Archimedes optimization algorithm (AOA) is a metaheuristic algorithm inspired by the Archimedes physical law. It simulates the principle of buoyancy applied upward to partially or completely submerged objects. The decay energy of buoyancy, Lévy flight and Tangent flight are introduced into AOA. The buoyancy energy is adopted as the judgment condition of global search and local search. Then the location updating methods based on Lévy flight and Tangent flight are proposed so as to enhance its ergodicity and unrepeatable, improve the convergence speed and accuracy. Finally, through a large number of simulation experiments on 25 benchmark functions in CEC-BC-2017, the improved AOAs are compared to show their advantages and disadvantages. On the other hand, two engineering design problems (pressure vessel design and spring design problem) are optimized. The experimental results show that the AOA based on buoyancy energy mixed Lévy flight and Tangent flight can solve the function optimization and engineering optimization problems well. It has the strong balance between exploration and exploitation, fast convergence speed and high search accuracy.
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Affiliation(s)
- Song Li
- School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, China
| | - Jie-Sheng Wang
- School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, China
| | - Hao-Ming Song
- School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, China
| | - Yue Zheng
- School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, China
| | - Xing-Yue Zhang
- School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, China
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12
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A Novel Ensemble of Arithmetic Optimization Algorithm and Harris Hawks Optimization for Solving Industrial Engineering Optimization Problems. MACHINES 2022. [DOI: 10.3390/machines10080602] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Recently, numerous new meta-heuristic algorithms have been proposed for solving optimization problems. According to the Non-Free Lunch theorem, we learn that no single algorithm can solve all optimization problems. In order to solve industrial engineering design problems more efficiently, we, inspired by the algorithm framework of the Arithmetic Optimization Algorithm (AOA) and the Harris Hawks Optimization (HHO), propose a novel hybrid algorithm based on these two algorithms, named EAOAHHO in this paper. The pinhole imaging opposition-based learning is introduced into the proposed algorithm to increase the original population diversity and the capability to escape from local optima. Furthermore, the introduction of composite mutation strategy enhances the proposed EAOAHHO exploitation and exploration to obtain better convergence accuracy. The performance of EAOAHHO is verified on 23 benchmark functions, the IEEE CEC2017 test suite. Finally, we verify the superiority of the proposed EAOAHHO over the other advanced meta-heuristic algorithms for solving four industrial engineering design problems.
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13
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Competitive teaching–learning-based optimization for multimodal optimization problems. Soft comput 2022. [DOI: 10.1007/s00500-022-07283-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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14
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Abstract
The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based optimizer performs the optimization procedure using a novel way of exploration and exploitation and the multiphases of search. In this review research, we focused on the applications and developments of the recent well-established robust optimizer Harris hawk optimizer (HHO) as one of the most popular swarm-based techniques of 2020. Moreover, several experiments were carried out to prove the powerfulness and effectivness of HHO compared with nine other state-of-art algorithms using Congress on Evolutionary Computation (CEC2005) and CEC2017. The literature review paper includes deep insight about possible future directions and possible ideas worth investigations regarding the new variants of the HHO algorithm and its widespread applications.
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Kassaymeh S, Abdullah S, Al-Betar MA, Alweshah M. Salp swarm optimizer for modeling the software fault prediction problem. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2022. [DOI: 10.1016/j.jksuci.2021.01.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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16
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Vehicle physical parameter identification based on an improved Harris hawks optimization and the transfer matrix method for multibody systems. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03704-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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17
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Liu J, Liu X, Wu Y, Yang Z, Xu J. Dynamic multi-swarm differential learning harris hawks optimizer and its application to optimal dispatch problem of cascade hydropower stations. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108281] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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Samma H, Sama ASB. Rules embedded harris hawks optimizer for large-scale optimization problems. Neural Comput Appl 2022; 34:13599-13624. [PMID: 35378781 PMCID: PMC8967692 DOI: 10.1007/s00521-022-07146-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 02/28/2022] [Indexed: 11/19/2022]
Abstract
Harris Hawks Optimizer (HHO) is a recent optimizer that was successfully applied for various real-world problems. However, working under large-scale problems requires an efficient exploration/exploitation balancing scheme that helps HHO to escape from possible local optima stagnation. To achieve this objective and boost the search efficiency of HHO, this study develops embedded rules used to make adaptive switching between exploration/exploitation based on search performances. These embedded rules were formulated based on several parameters such as population status, success rate, and the number of consumed search iterations. To verify the effectiveness of these embedded rules in improving HHO performances, a total of six standard high-dimensional functions ranging from 1000-D to 10,000-D and CEC’2010 large-scale benchmark were employed in this study. In addition, the proposed Rules Embedded Harris Hawks Optimizer (REHHO) applied for one real-world high dimensional wavelength selection problem. Conducted experiments showed that these embedded rules significantly improve HHO in terms of accuracy and convergence curve. In particular, REHHO was able to achieve superior performances against HHO in all conducted benchmark problems. Besides that, results showed that faster convergence was obtained from the embedded rules. Furthermore, REHHO was able to outperform several recent and state-of-the-art optimization algorithms.
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Abstract
AbstractComplex Event Processing (CEP) is a modern software technology for the dynamic analysis of continuous data streams. CEP is able of searching extremely large data streams in real time for the presence of event patterns. So far, specifying event patterns of CEP rules is still a manual task based on the expertise of domain experts. This paper presents a novel bat-inspired swarm algorithm for automatically mining CEP rule patterns that express the relevant causal and temporal relations hidden in data streams. The basic suitability and performance of the approach is proven by extensive evaluation with both synthetically generated data and real data from the traffic domain.
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Shi H, Lu F, Wu L, Yang G. Optimal trajectories of multi-UAVs with approaching formation for target tracking using improved Harris Hawks optimizer. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03270-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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21
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A Novel Biologically Inspired Approach for Clustering and Multi-Level Image Thresholding: Modified Harris Hawks Optimizer. Cognit Comput 2022. [DOI: 10.1007/s12559-022-09998-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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A hybrid extreme learning machine model with harris hawks optimisation algorithm: an optimised model for product demand forecasting applications. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03251-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
AbstractAccurate and real-time product demand forecasting is the need of the hour in the world of supply chain management. Predicting future product demand from historical sales data is a highly non-linear problem, subject to various external and environmental factors. In this work, we propose an optimised forecasting model - an extreme learning machine (ELM) model coupled with the Harris Hawks optimisation (HHO) algorithm to forecast product demand in an e-commerce company. ELM is preferred over traditional neural networks mainly due to its fast computational speed, which allows efficient demand forecasting in real-time. Our ELM-HHO model performed significantly better than ARIMA models that are commonly used in industries to forecast product demand. The performance of the proposed ELM-HHO model was also compared with traditional ELM, ELM auto-tuned using Bayesian Optimisation (ELM-BO), Gated Recurrent Unit (GRU) based recurrent neural network and Long Short Term Memory (LSTM) recurrent neural network models. Different performance metrics, i.e., Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Percentage Error (MPE) were used for the comparison of the selected models. Horizon forecasting at 3 days and 7 days ahead was also performed using the proposed approach. The results revealed that the proposed approach is superior to traditional product demand forecasting models in terms of prediction accuracy and it can be applied in real-time to predict future product demand based on the previous week’s sales data. In particular, considering RMSE of forecasting, the proposed ELM-HHO model performed 62.73% better than the statistical ARIMA(7,1,0) model, 40.73% better than the neural network based GRU model, 34.05% better than the neural network based LSTM model, 27.16% better than the traditional non-optimised ELM model with 100 hidden nodes and 11.63% better than the ELM-BO model in forecasting product demand for future 3 months. The novelty of the proposed approach lies in the way the fast computational speed of ELMs has been combined with the accuracy gained by tuning hyperparameters using HHO. An increased number of hyperparameters has been optimised in our methodology compared to available models. The majority of approaches to improve the accuracy of ELM so far have only focused on tuning the weights and the biases of the hidden layer. In our hybrid model, we tune the number of hidden nodes, the number of input time lags and even the type of activation function used in the hidden layer in addition to tuning the weights and the biases. This has resulted in a significant increase in accuracy over previous methods. Our work presents an original way of performing product demand forecasting in real-time in industry with highly accurate results which are much better than pre-existing demand forecasting models.
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Harris Hawks Sparse Auto-Encoder Networks for Automatic Speech Recognition System. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12031091] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Automatic speech recognition (ASR) is an effective technique that can convert human speech into text format or computer actions. ASR systems are widely used in smart appliances, smart homes, and biometric systems. Signal processing and machine learning techniques are incorporated to recognize speech. However, traditional systems have low performance due to a noisy environment. In addition to this, accents and local differences negatively affect the ASR system’s performance while analyzing speech signals. A precise speech recognition system was developed to improve the system performance to overcome these issues. This paper uses speech information from jim-schwoebel voice datasets processed by Mel-frequency cepstral coefficients (MFCCs). The MFCC algorithm extracts the valuable features that are used to recognize speech. Here, a sparse auto-encoder (SAE) neural network is used to classify the model, and the hidden Markov model (HMM) is used to decide on the speech recognition. The network performance is optimized by applying the Harris Hawks optimization (HHO) algorithm to fine-tune the network parameter. The fine-tuned network can effectively recognize speech in a noisy environment.
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Chen C, Wang X, Heidari AA, Yu H, Chen H. Multi-Threshold Image Segmentation of Maize Diseases Based on Elite Comprehensive Particle Swarm Optimization and Otsu. FRONTIERS IN PLANT SCIENCE 2021; 12:789911. [PMID: 34966405 PMCID: PMC8710579 DOI: 10.3389/fpls.2021.789911] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 11/01/2021] [Indexed: 06/14/2023]
Abstract
Maize is a major global food crop and as one of the most productive grain crops, it can be eaten; it is also a good feed for the development of animal husbandry and essential raw material for light industry, chemical industry, medicine, and health. Diseases are the main factor limiting the high and stable yield of maize. Scientific and practical identification is a vital link to reduce the damage of diseases and accurate segmentation of disease spots is one of the fundamental techniques for disease identification. However, one single method cannot achieve a good segmentation effect to meet the diversity and complexity of disease spots. In order to solve the shortcomings of noise interference and oversegmentation in the Otsu segmentation method, a non-local mean filtered two-dimensional histogram was used to remove the noise in disease images and a new elite strategy improved comprehensive particle swarm optimization (PSO) method was used to find the optimal segmentation threshold of the objective function in this study. The experimental results of segmenting three kinds of maize foliar disease images show that the segmentation effect of this method is better than other similar algorithms and it has better convergence and stability.
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Affiliation(s)
- Chengcheng Chen
- College of Computer Science and Technology, Jilin University, Changchun, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Changchun, China
| | - Xianchang Wang
- College of Computer Science and Technology, Jilin University, Changchun, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Changchun, China
- Chengdu Kestrel Artificial Intelligence Institute, Chengdu, China
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Helong Yu
- College of Information Technology, Jilin Agricultural University, Changchun, China
| | - Huiling Chen
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China
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Fetanat M, Stevens M, Jain P, Hayward C, Meijering E, Lovell NH. Fully Elman Neural Network: A Novel Deep Recurrent Neural Network Optimized by an Improved Harris Hawks Algorithm for Classification of Pulmonary Arterial Wedge Pressure. IEEE Trans Biomed Eng 2021; 69:1733-1744. [PMID: 34813462 DOI: 10.1109/tbme.2021.3129459] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Heart failure (HF) is one of the most prevalent life-threatening cardiovascular diseases in which 6.5 million people are suffering in the USA and more than 23 million worldwide. Mechanical circulatory support of HF patients can be achieved by implanting a left ventricular assist device (LVAD) into HF patients as a bridge to transplant, recovery or destination therapy and can be controlled by measurement of normal and abnormal pulmonary arterial wedge pressure (PAWP). While there are no commercial long-term implantable pressure sensors to measure PAWP, real-time non-invasive estimation of abnormal and normal PAWP becomes vital. In this work, first an improved Harris Hawks optimizer algorithm called HHO+ is presented and tested on 24 unimodal and multimodal benchmark functions. Second, a novel fully Elman neural network (FENN) is proposed to improve the classification performance. Finally, four novel 18-layer deep learning methods of convolutional neural networks (CNNs) with multi-layer perceptron (CNN-MLP), CNN with Elman neural networks (CNN-ENN), CNN with fully Elman neural networks (CNN-FENN), and CNN with fully Elman neural networks optimized by HHO+ algorithm (CNN-FENN-HHO+) for classification of abnormal and normal PAWP using estimated HVAD pump flow were developed and compared. The estimated pump flow was derived by a non-invasive method embedded into the commercial HVAD controller. The proposed methods are evaluated on an imbalanced clinical dataset using 5-fold cross-validation. The proposed CNN-FENN-HHO+ method outperforms the proposed CNN-MLP, CNN-ENN and CNN-FENN methods and improved the classification performance metrics across 5-fold cross-validation with an average sensitivity of 79%, accuracy of 78% and specificity of 76%. The proposed methods can reduce the likelihood of hazardous events like pulmonary congestion and ventricular suction for HF patients and notify identified abnormal cases to the hospital, clinician and cardiologist for emergency action, which can diminish the mortality rate of patients with HF.
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Zhang Q, Wang Z, Heidari AA, Gui W, Shao Q, Chen H, Zaguia A, Turabieh H, Chen M. Gaussian Barebone Salp Swarm Algorithm with Stochastic Fractal Search for medical image segmentation: A COVID-19 case study. Comput Biol Med 2021; 139:104941. [PMID: 34801864 DOI: 10.1016/j.compbiomed.2021.104941] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 10/11/2021] [Accepted: 10/11/2021] [Indexed: 01/11/2023]
Abstract
An appropriate threshold is a key to using the multi-threshold segmentation method to solve image segmentation problems, and the swarm intelligence (SI) optimization algorithm is one of the popular methods to obtain the optimal threshold. Moreover, Salp Swarm Algorithm (SSA) is a recently released swarm intelligent optimization algorithm. Compared with other SI optimization algorithms, the optimization solution strategy of the SSA still needs to be improved to enhance further the solution accuracy and optimization efficiency of the algorithm. Accordingly, this paper designs an effective segmentation method based on a non-local mean 2D histogram and 2D Kapur's entropy called SSA with Gaussian Barebone and Stochastic Fractal Search (GBSFSSSA) by combining Gaussian Barebone and Stochastic Fractal Search mechanism. In GBSFSSSA, the Gaussian Barebone and Stochastic Fractal Search mechanism effectively balance the global search ability and local search ability of the basic SSA. The CEC2017 competition data set is used to prove the algorithm's performance, and GBSFSSSA shows an absolute advantage over some typical competitive algorithms. Furthermore, the algorithm is applied in image segmentation of COVID-19 CT images, and the results are analyzed based on three different metrics: peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and feature similarity (FSIM), which can lead to the conclusion that the overall performance of GBSFSSSA is better than the comparison algorithm and can effectively improve the segmentation of medical images. Therefore, it is justified that GBSFSSSA is a reliable and effective method in solving the multi-threshold image segmentation problem.
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Affiliation(s)
- Qian Zhang
- Wenzhou University of Technology, Wenzhou, 325035, China.
| | - Zhiyan Wang
- School of Artificial Intelligence, Jilin International Studies University, Changchun, 130000, China.
| | - Ali Asghar Heidari
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China.
| | - Wenyong Gui
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China.
| | - Qike Shao
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China.
| | - Huiling Chen
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China.
| | - Atef Zaguia
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. BOX 11099, Taif, 21944, Saudi Arabia.
| | - Hamza Turabieh
- Department of Information Technology, College of Computers and Information Technology, PO Box 11099, Taif, 21944, Saudi Arabia.
| | - Mayun Chen
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
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28
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Hu J, Heidari AA, Zhang L, Xue X, Gui W, Chen H, Pan Z. Chaotic diffusion‐limited aggregation enhanced grey wolf optimizer: Insights, analysis, binarization, and feature selection. INT J INTELL SYST 2021. [DOI: 10.1002/int.22744] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
- Jiao Hu
- Department of Computer Science and Artificial Intelligence Wenzhou University Wenzhou China
| | - Ali Asghar Heidari
- Department of Computer Science and Artificial Intelligence Wenzhou University Wenzhou China
| | - Lejun Zhang
- College of Information Engineering Yangzhou University Yangzhou China
| | - Xiao Xue
- College of Computer Science and Technology Henan Polytechnic University Zhengzhou China
| | - Wenyong Gui
- Department of Computer Science and Artificial Intelligence Wenzhou University Wenzhou China
| | - Huiling Chen
- Department of Computer Science and Artificial Intelligence Wenzhou University Wenzhou China
| | - Zhifang Pan
- Zhejiang Engineering Research Center of Intelligent Medicine The First Affiliated Hospital of Wenzhou Medical University Wenzhou China
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29
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Improved differential evolution based on multi-armed bandit for multimodal optimization problems. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02261-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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30
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Performance optimization of differential evolution with slime mould algorithm for multilevel breast cancer image segmentation. Comput Biol Med 2021; 138:104910. [PMID: 34638022 DOI: 10.1016/j.compbiomed.2021.104910] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 09/23/2021] [Accepted: 09/24/2021] [Indexed: 01/11/2023]
Abstract
Breast cancer is one of the most dangerous diseases for women's health, and it is imperative to provide the necessary diagnostic assistance for it. The medical image processing technology is one of the most critical of all complementary diagnostic technologies. Image segmentation is the core step of image processing, where multilevel image segmentation is considered one of the most efficient and straightforward methods. Many multilevel image segmentation methods based on evolutionary and population-based methods have been proposed in recent years, but many have the fatal weakness of poor convergence accuracy and the tendency to fall into local optimum. Therefore, to overcome these weaknesses, this paper proposes a modified differential evolution (MDE) algorithm with a vision based on the slime mould foraging behavior, where the recently proposed slime mould algorithm (SMA) inspires it. Besides, to obtain high-quality breast cancer image segmentation results, this paper also develops an excellent MDE-based multilevel image segmentation model, the core of which is based on non-local means 2D histogram and 2D Kapur's entropy. To effectively validate the performance of the proposed method, a comparison experiment between MDE and its similar algorithms was first carried out on IEEE CEC 2014. Then, an initial validation of the MDE-based multilevel image segmentation model was performed by utilizing a reference image set. Finally, the MDE-based multilevel image segmentation model was compared with peers using breast invasive ductal carcinoma images. A series of experimental results have proved that MDE is an evolutionary algorithm with high convergence accuracy and the ability to jump out of the local optimum, as well as effectively demonstrated that the developed model is a high-quality segmentation method that can provide practical support for further research of breast invasive ductal carcinoma pathological image processing.
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31
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Qiao Z, Ke L, Zhang G, Wang X. Adaptive collaborative optimization of traffic network signal timing based on immune-fireworks algorithm and hierarchical strategy. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02256-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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32
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Adaptive collaborative optimization of traffic network signal timing based on immune-fireworks algorithm and hierarchical strategy. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02256-y 10.1007/s10489-021-02256-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/29/2022]
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33
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Towards Precision Fertilization: Multi-Strategy Grey Wolf Optimizer Based Model Evaluation and Yield Estimation. ELECTRONICS 2021. [DOI: 10.3390/electronics10182183] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Precision fertilization is a major constraint in consistently balancing the contradiction between land resources, ecological environment, and population increase. Even more, it is a popular technology used to maintain sustainable development. Nitrogen (N), phosphorus (P), and potassium (K) are the main sources of nutrient income on farmland. The traditional fertilizer effect function cannot meet the conditional agrochemical theory’s conditional extremes because the soil is influenced by various factors and statistical errors in harvest and yield. In order to find more accurate scientific ratios, it has been proposed a multi-strategy-based grey wolf optimization algorithm (SLEGWO) to solve the fertilizer effect function in this paper, using the “3414” experimental field design scheme, taking the experimental field in Nongan County, Jilin Province as the experimental site to obtain experimental data, and using the residuals of the ternary fertilizer effect function of Nitrogen, phosphorus, and potassium as the target function. The experimental results showed that the SLEGWO algorithm could improve the fitting degree of the fertilizer effect equation and then reasonably predict the accurate fertilizer application ratio and improve the yield. It is a more accurate precision fertilization modeling method. It provides a new means to solve the problem of precision fertilizer and soil testing and fertilization.
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Ehsaeyan E, Zolghadrasli A. Multilevel Image Thresholding Based on Improved Expectation Maximization (EM) and Differential Evolution Algorithm. INT J HUM ROBOT 2021. [DOI: 10.1142/s0219843621500134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Multilevel image thresholding is an essential step in the image segmentation process. Expectation Maximization (EM) is a powerful technique to find thresholds but is sensitive to the initial points. Differential Evolution (DE) is a robust metaheuristic algorithm that can find thresholds rapidly. However, it may be trapped in the local optimums and premature convergence occurs. In this paper, we incorporate EM algorithm to DE and introduce a novel algorithm called EM+DE which overcomes these shortages and can segment images better than EM and DE algorithms. In the proposed method, EM estimates Gaussian Mixture Model (GMM) coefficients of the histogram and DE tries to provide good volunteer solutions to EM algorithm when EM converges in local areas. Finally, DE fits GMM parameters based on Root Mean Square Error (RMSE) to reach the fittest curve. Ten standard test images and six famous metaheuristic algorithms are considered and result on global fitness. PSNR, SSIM, FSIM criteria and the computational time are given. The experimental results prove that the proposed algorithm outperforms the EM and DE as well as EM+ other natural-inspired algorithms in terms of segmentation criteria.
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Affiliation(s)
- Ehsan Ehsaeyan
- Department of Communication and Electronic Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz 71946 85115, Iran
| | - Alireza Zolghadrasli
- Department of Communication and Electronic Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz 71946 85115, Iran
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35
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36
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Application of ameliorated Harris Hawks optimizer for designing of low-power signed floating-point MAC architecture. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05637-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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37
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39
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Abd Elaziz M, Yousri D, Mirjalili S. A hybrid Harris hawks-moth-flame optimization algorithm including fractional-order chaos maps and evolutionary population dynamics. ADVANCES IN ENGINEERING SOFTWARE 2021; 154:102973. [DOI: 10.1016/j.advengsoft.2021.102973] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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40
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Double adaptive weights for stabilization of moth flame optimizer: Balance analysis, engineering cases, and medical diagnosis. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2020.106728] [Citation(s) in RCA: 112] [Impact Index Per Article: 37.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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41
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Ye H, Wu P, Zhu T, Xiao Z, Zhang X, Zheng L, Zheng R, Sun Y, Zhou W, Fu Q, Ye X, Chen A, Zheng S, Heidari AA, Wang M, Zhu J, Chen H, Li J. Diagnosing Coronavirus Disease 2019 (COVID-19): Efficient Harris Hawks-Inspired Fuzzy K-Nearest Neighbor Prediction Methods. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:17787-17802. [PMID: 34786302 PMCID: PMC8545238 DOI: 10.1109/access.2021.3052835] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 01/15/2021] [Indexed: 05/26/2023]
Abstract
This study is devoted to proposing a useful intelligent prediction model to distinguish the severity of COVID-19, to provide a more fair and reasonable reference for assisting clinical diagnostic decision-making. Based on patients' necessary information, pre-existing diseases, symptoms, immune indexes, and complications, this article proposes a prediction model using the Harris hawks optimization (HHO) to optimize the Fuzzy K-nearest neighbor (FKNN), which is called HHO-FKNN. This model is utilized to distinguish the severity of COVID-19. In HHO-FKNN, the purpose of introducing HHO is to optimize the FKNN's optimal parameters and feature subsets simultaneously. Also, based on actual COVID-19 data, we conducted a comparative experiment between HHO-FKNN and several well-known machine learning algorithms, which result shows that not only the proposed HHO-FKNN can obtain better classification performance and higher stability on the four indexes but also screen out the key features that distinguish severe COVID-19 from mild COVID-19. Therefore, we can conclude that the proposed HHO-FKNN model is expected to become a useful tool for COVID-19 prediction.
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Affiliation(s)
- Hua Ye
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Peiliang Wu
- Department of Pulmonary and Critical Care MedicineThe 1st Affiliated Hospital, Wenzhou Medical UniversityWenzhou325000China
| | - Tianru Zhu
- The Second Clinical CollegeWenzhou Medical UniversityWenzhou325000China
| | - Zhongxiang Xiao
- Department of PharmacyAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Xie Zhang
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Long Zheng
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Rongwei Zheng
- Department of UrologyAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Yangjie Sun
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Weilong Zhou
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Qinlei Fu
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Xinxin Ye
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Ali Chen
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Shuang Zheng
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of EngineeringUniversity of TehranTehran1417466191Iran
- Department of Computer ScienceSchool of ComputingNational University of SingaporeSingapore117417
| | - Mingjing Wang
- Institute of Research and Development, Duy Tan UniversityDa Nang550000Vietnam
| | - Jiandong Zhu
- Department of Surgical OncologyAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Huiling Chen
- College of Computer Science and Artificial IntelligenceWenzhou UniversityWenzhou325035China
| | - Jifa Li
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
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42
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Abd Elaziz M, Nabil N, Moghdani R, Ewees AA, Cuevas E, Lu S. Multilevel thresholding image segmentation based on improved volleyball premier league algorithm using whale optimization algorithm. MULTIMEDIA TOOLS AND APPLICATIONS 2021; 80:12435-12468. [PMID: 33456315 PMCID: PMC7797715 DOI: 10.1007/s11042-020-10313-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 08/26/2020] [Accepted: 12/22/2020] [Indexed: 05/31/2023]
Abstract
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.
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Affiliation(s)
- Mohamed Abd Elaziz
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt
| | - Neggaz Nabil
- Faculté des mathématiques et informatique - Département d’Informatique- Laboratoire SIMPA, Université des Sciences et de la Technologie d’Oran Mohammed Boudiaf, USTO-MB, BP 1505, El M’naouer, 31000 Oran, Algeria
| | - Reza Moghdani
- Industrial Management Department, Persian Gulf University, Boushehr, Iran
| | - Ahmed A. Ewees
- Department of Computer, Damietta University, Damietta, Egypt
| | - Erik Cuevas
- Departamento de Electrónica, Universidad de Guadalajara, CUCEI Av. Revolución 1500, 44430 Guadalajara, Mexico
| | - Songfeng Lu
- School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074 China
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43
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Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. APPL INTELL 2020. [DOI: 10.1007/s10489-020-01893-z] [Citation(s) in RCA: 217] [Impact Index Per Article: 54.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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44
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Abstract
Scheduling can be described as a decision-making process. It is applied in various applications, such as manufacturing, airports, and information processing systems. More so, the presence of symmetry is common in certain types of scheduling problems. There are three types of parallel machine scheduling problems (PMSP): uniform, identical, and unrelated parallel machine scheduling problems (UPMSPs). Recently, UPMSPs with setup time had attracted more attention due to its applications in different industries and services. In this study, we present an efficient method to address the UPMSPs while using a modified harris hawks optimizer (HHO). The new method, called MHHO, uses the salp swarm algorithm (SSA) as a local search for HHO in order to enhance its performance and to decrease its computation time. To test the performance of MHHO, several experiments are implemented using small and large problem instances. Moreover, the proposed method is compared to several state-of-art approaches used for UPMSPs. The MHHO shows better performance in both small and large problem cases.
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