1
|
Premkumar M, Sinha G, Ramasamy MD, Sahu S, Subramanyam CB, Sowmya R, Abualigah L, Derebew B. Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems. Sci Rep 2024; 14:5434. [PMID: 38443569 PMCID: PMC10914809 DOI: 10.1038/s41598-024-55619-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 02/26/2024] [Indexed: 03/07/2024] Open
Abstract
This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve the optimization capabilities of the conventional grey wolf optimizer in order to address the problem of data clustering. The process that groups similar items within a dataset into non-overlapping groups. Grey wolf hunting behaviour served as the model for grey wolf optimizer, however, it frequently lacks the exploration and exploitation capabilities that are essential for efficient data clustering. This work mainly focuses on enhancing the grey wolf optimizer using a new weight factor and the K-means algorithm concepts in order to increase variety and avoid premature convergence. Using a partitional clustering-inspired fitness function, the K-means clustering-based grey wolf optimizer was extensively evaluated on ten numerical functions and multiple real-world datasets with varying levels of complexity and dimensionality. The methodology is based on incorporating the K-means algorithm concept for the purpose of refining initial solutions and adding a weight factor to increase the diversity of solutions during the optimization phase. The results show that the K-means clustering-based grey wolf optimizer performs much better than the standard grey wolf optimizer in discovering optimal clustering solutions, indicating a higher capacity for effective exploration and exploitation of the solution space. The study found that the K-means clustering-based grey wolf optimizer was able to produce high-quality cluster centres in fewer iterations, demonstrating its efficacy and efficiency on various datasets. Finally, the study demonstrates the robustness and dependability of the K-means clustering-based grey wolf optimizer in resolving data clustering issues, which represents a significant advancement over conventional techniques. In addition to addressing the shortcomings of the initial algorithm, the incorporation of K-means and the innovative weight factor into the grey wolf optimizer establishes a new standard for further study in metaheuristic clustering algorithms. The performance of the K-means clustering-based grey wolf optimizer is around 34% better than the original grey wolf optimizer algorithm for both numerical test problems and data clustering problems.
Collapse
Affiliation(s)
- Manoharan Premkumar
- Department of Electrical & Electronics Engineering, Dayananda Sagar College of Engineering, Kumaraswamy Layout, Bengaluru, Karnataka, 560078, India.
| | - Garima Sinha
- Department of Computer Science and Engineering, Jain University, Ramanagaram, Bengaluru, Karnataka, India
| | - Manjula Devi Ramasamy
- Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India
| | - Santhoshini Sahu
- Department of Computer Science & Engineering, GMR Institute of Technology, Rajam, Srikakulam, Andhra Pradesh, India
| | | | - Ravichandran Sowmya
- Department of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Laith Abualigah
- Computer Science Department, Al al-Bayt University, Mafraq, 25113, Jordan
- Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, 71491, Tabuk, Saudi Arabia
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan
- MEU Research Unit, Middle East University, Amman, 11831, Jordan
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon
- School of Engineering and Technology, Sunway University Malaysia, 27500, Petaling Jaya, Malaysia
- College of Engineering, Yuan Ze University, Taoyuan, Taiwan
- Department of Statistics, College of Natural and Computational Science, Mizan-Tepi University, Tepi Bushira, Ethiopia
| | - Bizuwork Derebew
- Applied science research center, Applied science private university, Amman, 11931, Jordan.
| |
Collapse
|
2
|
Jazaeri SS, Asghari P, Jabbehdari S, Javadi HHS. Composition of caching and classification in edge computing based on quality optimization for SDN-based IoT healthcare solutions. THE JOURNAL OF SUPERCOMPUTING 2023; 79:1-51. [PMID: 37359340 PMCID: PMC10169185 DOI: 10.1007/s11227-023-05332-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 04/21/2023] [Indexed: 06/28/2023]
Abstract
This paper proposes a novel approach that uses a spectral clustering method to cluster patients with e-health IoT devices based on their similarity and distance and connect each cluster to an SDN edge node for efficient caching. The proposed MFO-Edge Caching algorithm is considered for selecting the near-optimal data options for caching based on considered criteria and improving QoS. Experimental results demonstrate that the proposed approach outperforms other methods in terms of performance, achieving decrease in average time between data retrieval delays and the cache hit rate of 76%. Emergency and on-demand requests are prioritized for caching response packets, while periodic requests have a lower cache hit ratio of 35%. The approach shows improvement in performance compared to other methods, highlighting the effectiveness of SDN-Edge caching and clustering for optimizing e-health network resources.
Collapse
Affiliation(s)
- Seyedeh Shabnam Jazaeri
- Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Parvaneh Asghari
- Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Sam Jabbehdari
- Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran
| | | |
Collapse
|
3
|
Sun J, Liu J, Miao M, Lin H. Research on Parameter Optimization Method of Sliding Mode Controller for the Grid-Connected Composite Device Based on IMFO Algorithm. SENSORS (BASEL, SWITZERLAND) 2022; 23:149. [PMID: 36616744 PMCID: PMC9823455 DOI: 10.3390/s23010149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 12/19/2022] [Accepted: 12/21/2022] [Indexed: 06/17/2023]
Abstract
In order to make the grid-connected composite device (GCCD) controller meet the requirements of different operating modes and complex working conditions of power grid, this paper proposes to introduce sliding mode control (SMC) into GCCD controller. Firstly, the mathematical model of MMC converter is established, and the sliding mode controller is designed based on the SMC principle. Then, aiming at the problems of complex controller structure and difficult parameter tuning in multiple modes of the GCCD, this paper proposes a controller parameter optimization method based on improved Month Flame optimization (IMFO) algorithm. This method improves the MFO algorithm by introducing good point set (GPS) initialization and Levy flight strategy, which accelerates the convergence speed of the algorithm while avoiding falling into local optimization, and realizes the optimization of converter controller parameters. Under a variety of standard test functions, the advantages of the proposed IMFO algorithm are verified by comparing it with the traditional algorithm. Finally, in order to realize the automatic tuning of control parameters, the Python-PSCAD joint simulation method is studied and implemented. Taking the comprehensive integral of time and absolute error (CITAE) index as the objective function, the parameters of the sliding mode controller are optimized. The simulation results show that the controller parameters optimized by the IMFO algorithm can make the GCCD have better dynamic performance.
Collapse
Affiliation(s)
- Ji Sun
- Correspondence: (J.S.); (J.L.); Tel.: +86-185-2174-4707 (J.S.)
| | - Jiajun Liu
- Correspondence: (J.S.); (J.L.); Tel.: +86-185-2174-4707 (J.S.)
| | | | | |
Collapse
|
4
|
Zorarpacı E. Data clustering using leaders and followers optimization and differential evolution. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
5
|
Sahoo SK, Saha AK, Ezugwu AE, Agushaka JO, Abuhaija B, Alsoud AR, Abualigah L. Moth Flame Optimization: Theory, Modifications, Hybridizations, and Applications. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2022; 30:391-426. [PMID: 36059575 PMCID: PMC9422949 DOI: 10.1007/s11831-022-09801-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 07/27/2022] [Indexed: 05/29/2023]
Abstract
The Moth flame optimization (MFO) algorithm belongs to the swarm intelligence family and is applied to solve complex real-world optimization problems in numerous domains. MFO and its variants are easy to understand and simple to operate. However, these algorithms have successfully solved optimization problems in different areas such as power and energy systems, engineering design, economic dispatch, image processing, and medical applications. A comprehensive review of MFO variants is presented in this context, including the classic version, binary types, modified versions, hybrid versions, multi-objective versions, and application part of the MFO algorithm in various sectors. Finally, the evaluation of the MFO algorithm is presented to measure its performance compared to other algorithms. The main focus of this literature is to present a survey and review the MFO and its applications. Also, the concluding remark section discusses some possible future research directions of the MFO algorithm and its variants.
Collapse
Affiliation(s)
- Saroj Kumar Sahoo
- Department of Mathematics, National Institute of Technology Agartala, Agartala, Tripura 799046 India
| | - Apu Kumar Saha
- Department of Mathematics, National Institute of Technology Agartala, Agartala, Tripura 799046 India
| | - Absalom E. Ezugwu
- School of Computer Science, University of KwaZulu-Natal, King Edward Road, Pietermaritzburg Campus, Pietermaritzburg, 3201 KwaZulu-Natal South Africa
| | - Jeffrey O. Agushaka
- School of Computer Science, University of KwaZulu-Natal, King Edward Road, Pietermaritzburg Campus, Pietermaritzburg, 3201 KwaZulu-Natal South Africa
| | - Belal Abuhaija
- Department of Computer Science, Wenzhou - Kean University, Wenzhou, China
| | - Anas Ratib Alsoud
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328 Jordan
| | - Laith Abualigah
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328 Jordan
- Faculty of Information Technology, Middle East University, Amman, 11831 Jordan
| |
Collapse
|
6
|
A Normal Distributed Dwarf Mongoose Optimization Algorithm for Global Optimization and Data Clustering Applications. Symmetry (Basel) 2022. [DOI: 10.3390/sym14051021] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
As data volumes have increased and difficulty in tackling vast and complicated problems has emerged, the need for innovative and intelligent solutions to handle these difficulties has become essential. Data clustering is a data mining approach that clusters a huge amount of data into a number of clusters; in other words, it finds symmetric and asymmetric objects. In this study, we developed a novel strategy that uses intelligent optimization algorithms to tackle a group of issues requiring sophisticated methods to solve. Three primary components are employed in the suggested technique, named GNDDMOA: Dwarf Mongoose Optimization Algorithm (DMOA), Generalized Normal Distribution (GNF), and Opposition-based Learning Strategy (OBL). These parts are used to organize the executions of the proposed method during the optimization process based on a unique transition mechanism to address the critical limitations of the original methods. Twenty-three test functions and eight data clustering tasks were utilized to evaluate the performance of the suggested method. The suggested method’s findings were compared to other well-known approaches. In all of the benchmark functions examined, the suggested GNDDMOA approach produced the best results. It performed very well in data clustering applications showing promising performance.
Collapse
|
7
|
Controlling Agronomic Variables of Saffron Crop Using IoT for Sustainable Agriculture. SUSTAINABILITY 2022. [DOI: 10.3390/su14095607] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Saffron, also known as “the golden spice”, is one of the most expensive crops in the world. The expensiveness of saffron comes from its rarity, the tedious harvesting process, and its nutritional and medicinal value. Different countries of the world are making great economic growth due to saffron export. In India, it is cultivated mostly in regions of Kashmir owing to its climate and soil composition. The economic value generated by saffron export can be increased manyfold by studying the agronomical factors of saffron and developing a model for artificial cultivation of saffron in any season and anywhere by monitoring and controlling the conditions of its growth. This paper presents a detailed study of all the agronomical variables of saffron that have a direct or indirect impact on its growth. It was found that, out of all the agronomical variables, the important ones having an impact on growth include corm size, temperature, water availability, and minerals. It was also observed that the use of IoT for the sustainable cultivation of saffron in smart cities has been discussed only by very few research papers. An IoT-based framework has also been proposed, which can be used for controlling and monitoring all the important growth parameters of saffron for its cultivation.
Collapse
|
8
|
|
9
|
Selecting Some Variables to Update-Based Algorithm for Solving Optimization Problems. SENSORS 2022; 22:s22051795. [PMID: 35270941 PMCID: PMC8914702 DOI: 10.3390/s22051795] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 02/16/2022] [Accepted: 02/21/2022] [Indexed: 11/17/2022]
Abstract
With the advancement of science and technology, new complex optimization problems have emerged, and the achievement of optimal solutions has become increasingly important. Many of these problems have features and difficulties such as non-convex, nonlinear, discrete search space, and a non-differentiable objective function. Achieving the optimal solution to such problems has become a major challenge. To address this challenge and provide a solution to deal with the complexities and difficulties of optimization applications, a new stochastic-based optimization algorithm is proposed in this study. Optimization algorithms are a type of stochastic approach for addressing optimization issues that use random scanning of the search space to produce quasi-optimal answers. The Selecting Some Variables to Update-Based Algorithm (SSVUBA) is a new optimization algorithm developed in this study to handle optimization issues in various fields. The suggested algorithm’s key principles are to make better use of the information provided by different members of the population and to adjust the number of variables used to update the algorithm population during the iterations of the algorithm. The theory of the proposed SSVUBA is described, and then its mathematical model is offered for use in solving optimization issues. Fifty-three objective functions, including unimodal, multimodal, and CEC 2017 test functions, are utilized to assess the ability and usefulness of the proposed SSVUBA in addressing optimization issues. SSVUBA’s performance in optimizing real-world applications is evaluated on four engineering design issues. Furthermore, the performance of SSVUBA in optimization was compared to the performance of eight well-known algorithms to further evaluate its quality. The simulation results reveal that the proposed SSVUBA has a significant ability to handle various optimization issues and that it outperforms other competitor algorithms by giving appropriate quasi-optimal solutions that are closer to the global optima.
Collapse
|
10
|
Data Security-Based Routing in MANETs Using Key Management Mechanism. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12031041] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
A Mobile Ad Hoc Network (MANET) is an autonomous network developed using wireless mobile nodes without the support of any kind of infrastructure. In a MANET, nodes can communicate with each other freely and dynamically. However, MANETs are prone to serious security threats that are difficult to resist using the existing security approaches. Therefore, various secure routing protocols have been developed to strengthen the security of MANETs. In this paper, a secure and energy-efficient routing protocol is proposed by using group key management. Asymmetric key cryptography is used, which involves two specialized nodes, labeled the Calculator Key (CK) and the Distribution Key (DK). These two nodes are responsible for the generation, verification, and distribution of secret keys. As a result, other nodes need not perform any kind of additional computation for building the secret keys. These nodes are selected using the energy consumption and trust values of nodes. In most of the existing routing protocols, each node is responsible for the generation and distribution of its own secret keys, which results in more energy dissemination. Moreover, if any node is compromised, security breaches should occur. When nodes other than the CK and DK are compromised, the entire network’s security is not jeopardized. Extensive experiments are performed by considering the existing and the proposed protocols. Performance analyses reveal that the proposed protocol outperforms the competitive protocols.
Collapse
|
11
|
Multivariate and Online Prediction of Closing Price Using Kernel Adaptive Filtering. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:6400045. [PMID: 34956352 PMCID: PMC8709756 DOI: 10.1155/2021/6400045] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 11/05/2021] [Accepted: 11/26/2021] [Indexed: 11/18/2022]
Abstract
This paper proposes a multivariate and online prediction of stock prices via the paradigm of kernel adaptive filtering (KAF). The prediction of stock prices in traditional classification and regression problems needs independent and batch-oriented nature of training. In this article, we challenge this existing notion of the literature and propose an online kernel adaptive filtering-based approach to predict stock prices. We experiment with ten different KAF algorithms to analyze stocks' performance and show the efficacy of the work presented here. In addition to this, and in contrast to the current literature, we look at granular level data. The experiments are performed with quotes gathered at the window of one minute, five minutes, ten minutes, fifteen minutes, twenty minutes, thirty minutes, one hour, and one day. These time windows represent some of the common windows frequently used by traders. The proposed framework is tested on 50 different stocks making up the Indian stock index: Nifty-50. The experimental results show that online learning and KAF is not only a good option, but practically speaking, they can be deployed in high-frequency trading as well.
Collapse
|
12
|
Abstract
Moth–flame optimization (MFO) is a prominent swarm intelligence algorithm that demonstrates sufficient efficiency in tackling various optimization tasks. However, MFO cannot provide competitive results for complex optimization problems. The algorithm sinks into the local optimum due to the rapid dropping of population diversity and poor exploration. Hence, in this article, a migration-based moth–flame optimization (M-MFO) algorithm is proposed to address the mentioned issues. In M-MFO, the main focus is on improving the position of unlucky moths by migrating them stochastically in the early iterations using a random migration (RM) operator, maintaining the solution diversification by storing new qualified solutions separately in a guiding archive, and, finally, exploiting around the positions saved in the guiding archive using a guided migration (GM) operator. The dimensionally aware switch between these two operators guarantees the convergence of the population toward the promising zones. The proposed M-MFO was evaluated on the CEC 2018 benchmark suite on dimension 30 and compared against seven well-known variants of MFO, including LMFO, WCMFO, CMFO, CLSGMFO, LGCMFO, SMFO, and ODSFMFO. Then, the top four latest high-performing variants were considered for the main experiments with different dimensions, 30, 50, and 100. The experimental evaluations proved that the M-MFO provides sufficient exploration ability and population diversity maintenance by employing migration strategy and guiding archive. In addition, the statistical results analyzed by the Friedman test proved that the M-MFO demonstrates competitive performance compared to the contender algorithms used in the experiments.
Collapse
|
13
|
Abstract
This research proposes a new multi-membrane search algorithm (MSA) based on cell biological behavior. Cell secretion protein behavior and cell division and fusion strategy are the main inspirations for the algorithm. In order to verify the performance of the algorithm, we used 19 benchmark functions to compare the MSA test results with MVO, GWO, MFO and ALO. The number of iterations of each algorithm on each benchmark function is 100, the population number is 10, and the running is repeated 50 times, and the average and standard deviation of the results are recorded. Tests show that the MSA is competitive in unimodal benchmark functions and multi-modal benchmark functions, and the results in composite benchmark functions are all superior to MVO, MFO, ALO, and GWO algorithms. This paper also uses MSA to solve two classic engineering problems: welded beam design and pressure vessel design. The result of welded beam design is 1.7252, and the result of pressure vessel design is 5887.7052, which is better than other comparison algorithms. Statistical experiments show that MSA is a high-performance algorithm that is competitive in unimodal and multimodal functions, and its performance in compound functions is significantly better than MVO, MFO, ALO, and GWO algorithms.
Collapse
|
14
|
Sikandar S, Baloch NK, Hussain F, Amin W, Zikria YB, Yu H. An Optimized Nature-Inspired Metaheuristic Algorithm for Application Mapping in 2D-NoC. SENSORS (BASEL, SWITZERLAND) 2021; 21:5102. [PMID: 34372337 PMCID: PMC8347139 DOI: 10.3390/s21155102] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 07/26/2021] [Accepted: 07/26/2021] [Indexed: 11/16/2022]
Abstract
Mapping application task graphs on intellectual property (IP) cores into network-on-chip (NoC) is a non-deterministic polynomial-time hard problem. The evolution of network performance mainly depends on an effective and efficient mapping technique and the optimization of performance and cost metrics. These metrics mainly include power, reliability, area, thermal distribution and delay. A state-of-the-art mapping technique for NoC is introduced with the name of sailfish optimization algorithm (SFOA). The proposed algorithm minimizes the power dissipation of NoC via an empirical base applying a shared k-nearest neighbor clustering approach, and it gives quicker mapping over six considered standard benchmarks. The experimental results indicate that the proposed techniques outperform other existing nature-inspired metaheuristic approaches, especially in large application task graphs.
Collapse
Affiliation(s)
- Saleha Sikandar
- Computer Engineering Department, University of Engineering and Technology, Taxila 47050, Pakistan; (S.S.); (N.K.B.); (F.H.); (W.A.)
| | - Naveed Khan Baloch
- Computer Engineering Department, University of Engineering and Technology, Taxila 47050, Pakistan; (S.S.); (N.K.B.); (F.H.); (W.A.)
| | - Fawad Hussain
- Computer Engineering Department, University of Engineering and Technology, Taxila 47050, Pakistan; (S.S.); (N.K.B.); (F.H.); (W.A.)
| | - Waqar Amin
- Computer Engineering Department, University of Engineering and Technology, Taxila 47050, Pakistan; (S.S.); (N.K.B.); (F.H.); (W.A.)
| | - Yousaf Bin Zikria
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea
| | - Heejung Yu
- Department of Electronics and Information Engineering, Korea University, Sejong 30019, Korea
| |
Collapse
|