1
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Gopi S, Mohapatra P. Learning cooking algorithm for solving global optimization problems. Sci Rep 2024; 14:13359. [PMID: 38858429 PMCID: PMC11165014 DOI: 10.1038/s41598-024-60821-0] [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: 03/07/2023] [Accepted: 04/27/2024] [Indexed: 06/12/2024] Open
Abstract
In recent years, many researchers have made a continuous effort to develop new and efficient meta-heuristic algorithms to address complex problems. Hence, in this study, a novel human-based meta-heuristic algorithm, namely, the learning cooking algorithm (LCA), is proposed that mimics the cooking learning activity of humans in order to solve challenging problems. The LCA strategy is primarily motivated by observing how mothers and children prepare food. The fundamental idea of the LCA strategy is mathematically designed in two phases: (i) children learn from their mothers and (ii) children and mothers learn from a chef. The performance of the proposed LCA algorithm is evaluated on 51 different benchmark functions (which includes the first 23 functions of the CEC 2005 benchmark functions) and the CEC 2019 benchmark functions compared with state-of-the-art meta-heuristic algorithms. The simulation results and statistical analysis such as the t-test, Wilcoxon rank-sum test, and Friedman test reveal that LCA may effectively address optimization problems by maintaining a proper balance between exploitation and exploration. Furthermore, the LCA algorithm has been employed to solve seven real-world engineering problems, such as the tension/compression spring design, pressure vessel design problem, welded beam design problem, speed reducer design problem, gear train design problem, three-bar truss design, and cantilever beam problem. The results demonstrate the LCA's superiority and capability over other algorithms in solving complex optimization problems.
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Affiliation(s)
- S Gopi
- Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamil Nadu, 632 014, India
| | - Prabhujit Mohapatra
- Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamil Nadu, 632 014, India.
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2
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Chandran V, Mohapatra P. A novel multi-strategy ameliorated quasi-oppositional chaotic tunicate swarm algorithm for global optimization and constrained engineering applications. Heliyon 2024; 10:e30757. [PMID: 38779016 PMCID: PMC11109745 DOI: 10.1016/j.heliyon.2024.e30757] [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: 02/19/2024] [Revised: 04/29/2024] [Accepted: 05/03/2024] [Indexed: 05/25/2024] Open
Abstract
Over the last few decades, a number of prominent meta-heuristic algorithms have been put forth to address complex optimization problems. However, there is a critical need to enhance these existing meta-heuristics by employing a variety of evolutionary techniques to tackle the emerging challenges in engineering applications. As a result, this study attempts to boost the efficiency of the recently introduced bio-inspired algorithm, the Tunicate Swarm Algorithm (TSA), which is motivated by the foraging and swarming behaviour of bioluminescent tunicates residing in the deep sea. Like other algorithms, the TSA has certain limitations, including getting trapped in the local optimal values and a lack of exploration ability, resulting in premature convergence when dealing with highly challenging optimization problems. To overcome these shortcomings, a novel multi-strategy ameliorated TSA, termed the Quasi-Oppositional Chaotic TSA (QOCTSA), has been proposed as an enhanced variant of TSA. This enhanced method contributes the simultaneous incorporation of the Quasi-Oppositional Based Learning (QOBL) and Chaotic Local Search (CLS) mechanisms to effectively balance exploration and exploitation. The implementation of QOBL improves convergence accuracy and exploration rate, while the inclusion of a CLS strategy with ten chaotic maps improves exploitation by enhancing local search ability around the most prospective regions. Thus, the QOCTSA significantly enhances convergence accuracy while maintaining TSA diversification. The experimentations are conducted on a set of thirty-three diverse functions: CEC2005 and CEC2019 test functions, as well as several real-world engineering problems. The statistical and graphical outcomes indicate that QOCTSA is superior to TSA and exhibits a faster rate of convergence. Furthermore, the statistical tests, specifically the Wilcoxon rank-sum test and t-test, reveal that the QOCTSA method outperforms the other competing algorithms in the domain of real-world engineering design problems.
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Affiliation(s)
- Vanisree Chandran
- Department of Mathematics, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India
| | - Prabhujit Mohapatra
- Department of Mathematics, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India
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3
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Tang W, Cao L, Chen Y, Chen B, Yue Y. Solving Engineering Optimization Problems Based on Multi-Strategy Particle Swarm Optimization Hybrid Dandelion Optimization Algorithm. Biomimetics (Basel) 2024; 9:298. [PMID: 38786508 PMCID: PMC11118741 DOI: 10.3390/biomimetics9050298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Revised: 05/10/2024] [Accepted: 05/15/2024] [Indexed: 05/25/2024] Open
Abstract
In recent years, swarm intelligence optimization methods have been increasingly applied in many fields such as mechanical design, microgrid scheduling, drone technology, neural network training, and multi-objective optimization. In this paper, a multi-strategy particle swarm optimization hybrid dandelion optimization algorithm (PSODO) is proposed, which is based on the problems of slow optimization speed and being easily susceptible to falling into local extremum in the optimization ability of the dandelion optimization algorithm. This hybrid algorithm makes the whole algorithm more diverse by introducing the strong global search ability of particle swarm optimization and the unique individual update rules of the dandelion algorithm (i.e., rising, falling and landing). The ascending and descending stages of dandelion also help to introduce more changes and explorations into the search space, thus better balancing the global and local search. The experimental results show that compared with other algorithms, the proposed PSODO algorithm greatly improves the global optimal value search ability, convergence speed and optimization speed. The effectiveness and feasibility of the PSODO algorithm are verified by solving 22 benchmark functions and three engineering design problems with different complexities in CEC 2005 and comparing it with other optimization algorithms.
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Affiliation(s)
- Wenjie Tang
- School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, China
| | - Li Cao
- School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, China
| | - Yaodan Chen
- School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, China
| | - Binhe Chen
- School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, China
| | - Yinggao Yue
- School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, China
- Intelligent Information Systems Institute, Wenzhou University, Wenzhou 325035, China
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4
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Beşkirli M, Kiran MS. Optimization of Butterworth and Bessel Filter Parameters with Improved Tree-Seed Algorithm. Biomimetics (Basel) 2023; 8:540. [PMID: 37999181 PMCID: PMC10669204 DOI: 10.3390/biomimetics8070540] [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: 09/29/2023] [Revised: 11/07/2023] [Accepted: 11/07/2023] [Indexed: 11/25/2023] Open
Abstract
Filters are electrical circuits or networks that filter out unwanted signals. In these circuits, signals are permeable in a certain frequency range. Attenuation occurs in signals outside this frequency range. There are two types of filters: passive and active. Active filters consist of passive and active components, including transistors and operational amplifiers, but also require a power supply. In contrast, passive filters only consist of resistors and capacitors. Therefore, active filters are capable of generating signal gain and possess the benefit of high-input and low-output impedance. In order for active filters to be more functional, the parameters of the resistors and capacitors in the circuit must be at optimum values. Therefore, the active filter is discussed in this study. In this study, the tree seed algorithm (TSA), a plant-based optimization algorithm, is used to optimize the parameters of filters with tenth-order Butterworth and Bessel topology. In order to improve the performance of the TSA for filter parameter optimization, opposition-based learning (OBL) is added to TSA to form an improved TSA (I-TSA). The results obtained are compared with both basic TSA and some algorithms. The experimental results show that the I-TSA method is applicable to this problem by performing a successful prediction process.
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Affiliation(s)
- Mehmet Beşkirli
- Department of Computer Engineering, Karamanoğlu Mehmetbey University, 70200 Karaman, Türkiye
| | - Mustafa Servet Kiran
- Department of Computer Engineering, Konya Technical University, 42250 Konya, Türkiye
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5
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Ferahtia S, Houari A, Rezk H, Djerioui A, Machmoum M, Motahhir S, Ait-Ahmed M. Red-tailed hawk algorithm for numerical optimization and real-world problems. Sci Rep 2023; 13:12950. [PMID: 37558724 PMCID: PMC10412609 DOI: 10.1038/s41598-023-38778-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 07/14/2023] [Indexed: 08/11/2023] Open
Abstract
This study suggests a new nature-inspired metaheuristic optimization algorithm called the red-tailed hawk algorithm (RTH). As a predator, the red-tailed hawk has a hunting strategy from detecting the prey until the swoop stage. There are three stages during the hunting process. In the high soaring stage, the red-tailed hawk explores the search space and determines the area with the prey location. In the low soaring stage, the red-tailed moves inside the selected area around the prey to choose the best position for the hunt. Then, the red-tailed swings and hits its target in the stooping and swooping stages. The proposed algorithm mimics the prey-hunting method of the red-tailed hawk for solving real-world optimization problems. The performance of the proposed RTH algorithm has been evaluated on three classes of problems. The first class includes three specific kinds of optimization problems: 22 standard benchmark functions, including unimodal, multimodal, and fixed-dimensional multimodal functions, IEEE Congress on Evolutionary Computation 2020 (CEC2020), and IEEE CEC2022. The proposed algorithm is compared with eight recent algorithms to confirm its contribution to solving these problems. The considered algorithms are Farmland Fertility Optimizer (FO), African Vultures Optimization Algorithm (AVOA), Mountain Gazelle Optimizer (MGO), Gorilla Troops Optimizer (GTO), COOT algorithm, Hunger Games Search (HGS), Aquila Optimizer (AO), and Harris Hawks optimization (HHO). The results are compared regarding the accuracy, robustness, and convergence speed. The second class includes seven real-world engineering problems that will be considered to investigate the RTH performance compared to other published results profoundly. Finally, the proton exchange membrane fuel cell (PEMFC) extraction parameters will be performed to evaluate the algorithm with a complex problem. The proposed algorithm will be compared with several published papers to approve its performance. The ultimate results for each class confirm the ability of the proposed RTH algorithm to provide higher performance for most cases. For the first class, the RTH mostly got the optimal solutions for most functions with faster convergence speed. The RTH provided better performance for the second and third classes when resolving the real word engineering problems or extracting the PEMFC parameters.
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Affiliation(s)
- Seydali Ferahtia
- Institut de Recherche en Énergie Électrique de Nantes Atlantique, IREENA, Nantes University, Saint-Nazaire, France
- Laboratoire de Génie Electrique, Dept. of Electrical Engineering, University of M'sila, M'sila, Algeria
| | - Azeddine Houari
- Institut de Recherche en Énergie Électrique de Nantes Atlantique, IREENA, Nantes University, Saint-Nazaire, France
| | - Hegazy Rezk
- College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Ali Djerioui
- Laboratoire de Génie Electrique, Dept. of Electrical Engineering, University of M'sila, M'sila, Algeria
| | - Mohamed Machmoum
- Institut de Recherche en Énergie Électrique de Nantes Atlantique, IREENA, Nantes University, Saint-Nazaire, France
| | - Saad Motahhir
- ENSA, University of Sidi Mohamed Ben Abdellah, Fez, Morocco.
| | - Mourad Ait-Ahmed
- Institut de Recherche en Énergie Électrique de Nantes Atlantique, IREENA, Nantes University, Saint-Nazaire, France
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6
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Liu J, Hou Y, Li Y, Zhou H. A multi-strategy improved tree-seed algorithm for numerical optimization and engineering optimization problems. Sci Rep 2023; 13:10768. [PMID: 37402847 DOI: 10.1038/s41598-023-37958-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 06/30/2023] [Indexed: 07/06/2023] Open
Abstract
Tree-seed algorithm is a stochastic search algorithm with superior performance suitable for solving continuous optimization problems. However, it is also prone to fall into local optimum and slow in convergence. Therefore, this paper proposes an improved tree-seed algorithm based on pattern search, dimension permutation, and elimination update mechanism (PDSTSA). Firstly, a global optimization strategy based on pattern search is used to promote detection ability. Secondly, in order to maintain the diversity of the population, a random mutation strategy of individual dimension replacement is introduced. Finally, the elimination and update mechanism based on inferior trees is introduced in the middle and later stages of the iteration. Subsequently, PDSTSA is compared with seven representative algorithms on the IEEE CEC2015 test function for simulation experiments and convergence curve analysis. The experimental results indicate that PDSTSA has better optimization accuracy and convergence speed than other comparison algorithms. Then, the Wilcoxon rank sum test demonstrates that there is a significant difference between the optimization results of PDSTSA and each comparison algorithm. In addition, the results of eight algorithms for solving engineering constrained optimization problems further prove the feasibility, practicability, and superiority of PDSTSA.
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Affiliation(s)
- Jingsen Liu
- International Joint Laboratory of Intelligent Network Theory and Key Technology, Henan University, Kaifeng, China
- College of Software, Henan University, Kaifeng, China
| | - Yanlin Hou
- International Joint Laboratory of Intelligent Network Theory and Key Technology, Henan University, Kaifeng, China
- College of Software, Henan University, Kaifeng, China
| | - Yu Li
- Institute of Management Science and Engineering, Henan University, Kaifeng, China.
- Business School, Henan University, Kaifeng, China.
| | - Huan Zhou
- Business School, Henan University, Kaifeng, China
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7
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Chen J, Cai Z, Chen H, Chen X, Escorcia-Gutierrez J, Mansour RF, Ragab M. Renal Pathology Images Segmentation Based on Improved Cuckoo Search with Diffusion Mechanism and Adaptive Beta-Hill Climbing. JOURNAL OF BIONIC ENGINEERING 2023; 20:1-36. [PMID: 37361683 PMCID: PMC10154766 DOI: 10.1007/s42235-023-00365-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 03/03/2023] [Accepted: 03/07/2023] [Indexed: 06/28/2023]
Abstract
Lupus Nephritis (LN) is a significant risk factor for morbidity and mortality in systemic lupus erythematosus, and nephropathology is still the gold standard for diagnosing LN. To assist pathologists in evaluating histopathological images of LN, a 2D Rényi entropy multi-threshold image segmentation method is proposed in this research to apply to LN images. This method is based on an improved Cuckoo Search (CS) algorithm that introduces a Diffusion Mechanism (DM) and an Adaptive β-Hill Climbing (AβHC) strategy called the DMCS algorithm. The DMCS algorithm is tested on 30 benchmark functions of the IEEE CEC2017 dataset. In addition, the DMCS-based multi-threshold image segmentation method is also used to segment renal pathological images. Experimental results show that adding these two strategies improves the DMCS algorithm's ability to find the optimal solution. According to the three image quality evaluation metrics: PSNR, FSIM, and SSIM, the proposed image segmentation method performs well in image segmentation experiments. Our research shows that the DMCS algorithm is a helpful image segmentation method for renal pathological images.
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Affiliation(s)
- Jiaochen Chen
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035 China
| | - Zhennao Cai
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035 China
| | - Huiling Chen
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035 China
| | - Xiaowei Chen
- Department of Rheumatology and Immunology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000 China
| | - José Escorcia-Gutierrez
- Department of Computational Science and Electronics, Universidad de la Costa, CUC, 080002 Barranquilla, Colombia
| | - Romany F. Mansour
- Department of Mathematics, Faculty of Science, New Valley University, 72511, El-Kharga, Egypt
| | - Mahmoud Ragab
- Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589 Saudi Arabia
- Department of Mathematics, Faculty of Science, Al-Azhar University, Naser City, Cairo, 11884 Egypt
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8
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Wang J, Bei J, Song H, Zhang H, Zhang P. A whale optimization algorithm with combined mutation and removing similarity for global optimization and multilevel thresholding image segmentation. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
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9
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Mohapatra S, Mohapatra P. American zebra optimization algorithm for global optimization problems. Sci Rep 2023; 13:5211. [PMID: 36997597 PMCID: PMC10063666 DOI: 10.1038/s41598-023-31876-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 03/20/2023] [Indexed: 04/01/2023] Open
Abstract
A novel bio-inspired meta-heuristic algorithm, namely the American zebra optimization algorithm (AZOA), which mimics the social behaviour of American zebras in the wild, is proposed in this study. American zebras are distinguished from other mammals by their distinct and fascinating social character and leadership exercise, which navies the baby zebras to leave the herd before maturity and join a separate herd with no family ties. This departure of the baby zebra encourages diversification by preventing intra-family mating. Moreover, the convergence is assured by the leadership exercise in American zebras, which directs the speed and direction of the group. This social lifestyle behaviour of American zebras is indigenous in nature and is the main inspiration for proposing the AZOA meta-heuristic algorithm. To examine the efficiency of the AZOA algorithm, the CEC-2005, CEC-2017, and CEC-2019 benchmark functions are considered, and compared with the several state-of-the-art meta-heuristic algorithms. The experimental outcomes and statistical analysis reveal that AZOA is capable of attaining the optimal solutions for maximum benchmark functions while maintaining a good balance between exploration and exploitation. Furthermore, numerous real-world engineering problems have been employed to demonstrate the robustness of AZOA. Finally, it is anticipated that the AZOA will accomplish domineeringly for forthcoming advanced CEC benchmark functions and other complex engineering problems.
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Affiliation(s)
- Sarada Mohapatra
- Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India
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10
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Wang D, Liu H, Tu L, Ding G. An orthogonal electric fish optimization algorithm with quantization for global numerical optimization. Soft comput 2023. [DOI: 10.1007/s00500-023-07930-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
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11
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Fahmy H, El-Gendy EM, Mohamed M, Saafan MM. ECH 3OA: An Enhanced Chimp-Harris Hawks Optimization Algorithm for copyright protection in Color Images using watermarking techniques. Knowl Based Syst 2023. [DOI: 10.1016/j.knosys.2023.110494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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12
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Gharehchopogh FS, Ucan A, Ibrikci T, Arasteh B, Isik G. Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:2683-2723. [PMID: 36685136 PMCID: PMC9838547 DOI: 10.1007/s11831-023-09883-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 01/05/2023] [Indexed: 06/17/2023]
Abstract
Meta-heuristic algorithms have a high position among academic researchers in various fields, such as science and engineering, in solving optimization problems. These algorithms can provide the most optimal solutions for optimization problems. This paper investigates a new meta-heuristic algorithm called Slime Mould algorithm (SMA) from different optimization aspects. The SMA algorithm was invented due to the fluctuating behavior of slime mold in nature. It has several new features with a unique mathematical model that uses adaptive weights to simulate the biological wave. It provides an optimal pathway for connecting food with high exploration and exploitation ability. As of 2020, many types of research based on SMA have been published in various scientific databases, including IEEE, Elsevier, Springer, Wiley, Tandfonline, MDPI, etc. In this paper, based on SMA, four areas of hybridization, progress, changes, and optimization are covered. The rate of using SMA in the mentioned areas is 15, 36, 7, and 42%, respectively. According to the findings, it can be claimed that SMA has been repeatedly used in solving optimization problems. As a result, it is anticipated that this paper will be beneficial for engineers, professionals, and academic scientists.
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Affiliation(s)
| | - Alaettin Ucan
- Department of Computer Engineering, Osmaniye Korkut Ata University, Osmaniye, Turkey
| | - Turgay Ibrikci
- Department of Software Engineering, Adana Alparslan Turkes Science and Technology University, Adana, Turkey
| | - Bahman Arasteh
- Department of Software Engineering, Faculty of Engineering and Natural Science, Istinye University, Istanbul, Turkey
| | - Gultekin Isik
- Department of Computer Engineering, Igdir University, Igdir, Turkey
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13
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Gharehchopogh FS, Namazi M, Ebrahimi L, Abdollahzadeh B. Advances in Sparrow Search Algorithm: A Comprehensive Survey. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023. [PMID: 36034191 DOI: 10.1007/s11831-021-09698-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Mathematical programming and meta-heuristics are two types of optimization methods. Meta-heuristic algorithms can identify optimal/near-optimal solutions by mimicking natural behaviours or occurrences and provide benefits such as simplicity of execution, a few parameters, avoidance of local optimization, and flexibility. Many meta-heuristic algorithms have been introduced to solve optimization issues, each of which has advantages and disadvantages. Studies and research on presented meta-heuristic algorithms in prestigious journals showed they had good performance in solving hybrid, improved and mutated problems. This paper reviews the sparrow search algorithm (SSA), one of the new and robust algorithms for solving optimization problems. This paper covers all the SSA literature on variants, improvement, hybridization, and optimization. According to studies, the use of SSA in the mentioned areas has been equal to 32%, 36%, 4%, and 28%, respectively. The highest percentage belongs to Improved, which has been analyzed by three subsections: Meat-Heuristics, artificial neural networks, and Deep Learning.
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Affiliation(s)
| | - Mohammad Namazi
- Department of Computer Engineering, Maybod Branch. Islamic Azad University, Maybod, Iran
| | - Laya Ebrahimi
- Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran
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14
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Daoud MS, Shehab M, Al-Mimi HM, Abualigah L, Zitar RA, Shambour MKY. Gradient-Based Optimizer (GBO): A Review, Theory, Variants, and Applications. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2022; 30:2431-2449. [PMID: 36597494 PMCID: PMC9801167 DOI: 10.1007/s11831-022-09872-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 12/14/2022] [Indexed: 06/17/2023]
Abstract
This paper introduces a comprehensive survey of a new population-based algorithm so-called gradient-based optimizer (GBO) and analyzes its major features. GBO considers as one of the most effective optimization algorithm where it was utilized in different problems and domains, successfully. This review introduces set of related works of GBO where distributed into; GBO variants, GBO applications, and evaluate the efficiency of GBO compared with other metaheuristic algorithms. Finally, the conclusions concentrate on the existing work on GBO, showing its disadvantages, and propose future works. The review paper will be helpful for the researchers and practitioners of GBO belonging to a wide range of audiences from the domains of optimization, engineering, medical, data mining and clustering. As well, it is wealthy in research on health, environment and public safety. Also, it will aid those who are interested by providing them with potential future research.
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Affiliation(s)
| | - Mohammad Shehab
- Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, 11953 Jordan
| | - Hani M. Al-Mimi
- Department of Cybersecurity, Al-Zaytoonah University, Amman, Jordan
| | - Laith Abualigah
- Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al Al-Bayt University, Mafraq, 25113 Jordan
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328 Jordan
- Faculty of Information Technology, Middle East University, Amman, 11831 Jordan
- Applied Science Research Center, Applied Science Private University, Amman, 11931 Jordan
- School of Computer Sciences, Universiti Sains Malaysia, 11800 George Town, Pulau Pinang Malaysia
- Center for Engineering Application &
Technology Solutions, Ho Chi Minh City Open University, Ho Chi Minh, Viet Nam
| | - Raed Abu Zitar
- Sorbonne Center of Artificial Intelligence, Sorbonne University-Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Mohd Khaled Yousef Shambour
- The Custodian of the Two Holy Mosques Institute for Hajj and Umrah Research, Umm Al-Qura University, Mecca, Saudi Arabia
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15
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Agushaka JO, Ezugwu AE, Olaide ON, Akinola O, Zitar RA, Abualigah L. Improved Dwarf Mongoose Optimization for Constrained Engineering Design Problems. JOURNAL OF BIONIC ENGINEERING 2022; 20:1263-1295. [PMID: 36530517 PMCID: PMC9745293 DOI: 10.1007/s42235-022-00316-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 11/26/2022] [Accepted: 11/29/2022] [Indexed: 06/17/2023]
Abstract
This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha selection in IDMO differs from the DMO, where evaluating the probability value of each fitness is just a computational overhead and contributes nothing to the quality of the alpha or other group members. The fittest dwarf mongoose is selected as the alpha, and a new operator ω is introduced, which controls the alpha movement, thereby enhancing the exploration ability and exploitability of the IDMO. Second, the scout group movements are modified by randomization to introduce diversity in the search process and explore unvisited areas. Finally, the babysitter's exchange criterium is modified such that once the criterium is met, the babysitters that are exchanged interact with the dwarf mongoose exchanging them to gain information about food sources and sleeping mounds, which could result in better-fitted mongooses instead of initializing them afresh as done in DMO, then the counter is reset to zero. The proposed IDMO was used to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The performance of the IDMO, using different performance metrics and statistical analysis, is compared with the DMO and eight other existing algorithms. In most cases, the results show that solutions achieved by the IDMO are better than those obtained by the existing algorithms.
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Affiliation(s)
- Jeffrey O. Agushaka
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201 KwaZulu-Natal South Africa
- Department of Computer Science, Federal University of Lafia, Lafia, 950101 Nigeria
| | - Absalom E. Ezugwu
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201 KwaZulu-Natal South Africa
- Unit for Data Science and Computing, North-West University, 11 Hoffman Street, Potchefstroom, 2520 South Africa
| | - Oyelade N. Olaide
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201 KwaZulu-Natal South Africa
| | - Olatunji Akinola
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201 KwaZulu-Natal South Africa
| | - Raed Abu Zitar
- Sorbonne Center of Artificial Intelligence, Sorbonne University-Abu Dhabi, 38044 Abu Dhabi, United Arab Emirates
| | - 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
- Faculty of Information Technology, Applied Science Private University, Amman, 11931 Jordan
- School of Computer Sciences, Universiti Sains Malaysia, 11800 Pulau Pinang, Malaysia
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16
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Dong H, Xu Y, Cao D, Zhang W, Yang Z, Li X. An improved teaching–learning-based optimization algorithm with a modified learner phase and a new mutation-restarting phase. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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17
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Binary African vultures optimization algorithm for various optimization problems. INT J MACH LEARN CYB 2022. [DOI: 10.1007/s13042-022-01703-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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18
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Yuan K, Yu D, Feng J, Yang L, Jia C, Huang Y. A block cipher algorithm identification scheme based on hybrid k-nearest neighbor and random forest algorithm. PeerJ Comput Sci 2022; 8:e1110. [PMID: 36262148 PMCID: PMC9575859 DOI: 10.7717/peerj-cs.1110] [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: 05/11/2022] [Accepted: 08/29/2022] [Indexed: 06/16/2023]
Abstract
Cryptographic algorithm identification, which refers to analyzing and identifying the encryption algorithm used in cryptographic system, is of great significance to cryptanalysis. In order to improve the accuracy of identification work, this article proposes a new ensemble learning-based model named hybrid k-nearest neighbor and random forest (HKNNRF), and constructs a block cipher algorithm identification scheme. In the ciphertext-only scenario, we use NIST randomness test methods to extract ciphertext features, and carry out binary-classification and five-classification experiments on the block cipher algorithms using proposed scheme. Experiments show that when the ciphertext size and other experimental conditions are the same, compared with the baselines, the HKNNRF model has higher classification accuracy. Specifically, the average binary-classification identification accuracy of HKNNRF is 69.5%, which is 13%, 12.5%, and 10% higher than the single-layer support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF) respectively. The five-classification identification accuracy can reach 34%, which is higher than the 21% accuracy of KNN, the 22% accuracy of RF and the 23% accuracy of SVM respectively under the same experimental conditions.
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Affiliation(s)
- Ke Yuan
- School of Computer and Information Engineering, Henan University, Kaifeng, Henan, China
- Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, Henan, China
| | - Daoming Yu
- School of Computer and Information Engineering, Henan University, Kaifeng, Henan, China
| | - Jingkai Feng
- International Education College, Henan University, Zhengzhou, Henan, China
| | - Longwei Yang
- School of Computer and Information Engineering, Henan University, Kaifeng, Henan, China
| | - Chunfu Jia
- College of Cybersecurity, Nankai University, Tianjin, Tianjin, China
| | - Yiwang Huang
- School of Data Science, Tongren University, Tongren, Guizhou, China
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19
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Nand R, Sharma B, Chaudhary K. An introduction of preference based stepping ahead firefly algorithm for the uncapacitated examination timetabling. PeerJ Comput Sci 2022; 8:e1068. [PMID: 36091985 PMCID: PMC9455270 DOI: 10.7717/peerj-cs.1068] [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: 04/01/2022] [Accepted: 07/21/2022] [Indexed: 06/15/2023]
Abstract
In recent times, there has been a growing attention to intelligent optimization algorithms centred on swarm principles such as the firefly algorithm (FA). It was proposed for the continuous domain that mimics the attraction of fireflies to flashing light and has been used in discrete domains via modification. A discrete domain that is a major challenge in most higher education institutes (HEI) is examination timetabling. This article presents a new methodology based on FA for uncapacitated examination timetabling problems (UETP) where the proposed method is an extension of earlier work by the authors on the continuous domain. UETP is considered in this article as it is a university examination timetabling problem, which is still an active research area and has not been solved by FA algorithm as per authors knowledge. The proposed method concentrates on solving the initial solution using discrete FA where it consolidates the reordering of examinations and slots through a heuristic ordering known as neighborhood search. Three neighborhoods are employed in this research, where one is used during the initialization phase while two are utilized during solution improvement phase. Later, through preference parameters, a novel stepping ahead mechanism is used, which employs neighborhood searches built on previous searches. The proposed method is tested with 12 UETP problems where the preference based stepping ahead FA creates comparative results to the best ones available in the literature for the Toronto exam timetabling dataset. The results obtained are proof of concept at the preliminary stage and require further experiments on other educational datasets such as the second international timetable competition benchmark sets. The newly introduced preference based stepping ahead mechanism takes advantage of the current best solution space where it exploits the solution space for better solutions. This paves the way for researchers to utilize the mechanism in other domains such as robotics, etc.
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20
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Gharehchopogh FS, Namazi M, Ebrahimi L, Abdollahzadeh B. Advances in Sparrow Search Algorithm: A Comprehensive Survey. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2022; 30:427-455. [PMID: 36034191 PMCID: PMC9395821 DOI: 10.1007/s11831-022-09804-w] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Accepted: 08/02/2022] [Indexed: 05/29/2023]
Abstract
Mathematical programming and meta-heuristics are two types of optimization methods. Meta-heuristic algorithms can identify optimal/near-optimal solutions by mimicking natural behaviours or occurrences and provide benefits such as simplicity of execution, a few parameters, avoidance of local optimization, and flexibility. Many meta-heuristic algorithms have been introduced to solve optimization issues, each of which has advantages and disadvantages. Studies and research on presented meta-heuristic algorithms in prestigious journals showed they had good performance in solving hybrid, improved and mutated problems. This paper reviews the sparrow search algorithm (SSA), one of the new and robust algorithms for solving optimization problems. This paper covers all the SSA literature on variants, improvement, hybridization, and optimization. According to studies, the use of SSA in the mentioned areas has been equal to 32%, 36%, 4%, and 28%, respectively. The highest percentage belongs to Improved, which has been analyzed by three subsections: Meat-Heuristics, artificial neural networks, and Deep Learning.
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Affiliation(s)
| | - Mohammad Namazi
- Department of Computer Engineering, Maybod Branch. Islamic Azad University, Maybod, Iran
| | - Laya Ebrahimi
- Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran
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21
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Zhang X, Xiao F, Tong X, Yun J, Liu Y, Sun Y, Tao B, Kong J, Xu M, Chen B. Time Optimal Trajectory Planing Based on Improved Sparrow Search Algorithm. Front Bioeng Biotechnol 2022; 10:852408. [PMID: 35392405 PMCID: PMC8981035 DOI: 10.3389/fbioe.2022.852408] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 02/14/2022] [Indexed: 11/13/2022] Open
Abstract
Complete trajectory planning includes path planning, inverse solution solving and trajectory optimization. In this paper, a highly smooth and time-saving approach to trajectory planning is obtained by improving the kinematic and optimization algorithms for the time-optimal trajectory planning problem. By partitioning the joint space, the paper obtains an inverse solution calculation based on the partitioning of the joint space, saving 40% of the inverse kinematics solution time. This means that a large number of computational resources can be saved in trajectory planning. In addition, an improved sparrow search algorithm (SSA) is proposed to complete the solution of the time-optimal trajectory. A Tent chaotic mapping was used to optimize the way of generating initial populations. The algorithm was further improved by combining it with an adaptive step factor. The experiments demonstrated the performance of the improved SSA. The robot's trajectory is further optimized in time by an improved sparrow search algorithm. Experimental results show that the method can improve convergence speed and global search capability and ensure smooth trajectories.
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Affiliation(s)
- Xiaofeng Zhang
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
| | - Fan Xiao
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
| | - XiLiang Tong
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
| | - Juntong Yun
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
| | - Ying Liu
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
| | - Ying Sun
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
| | - Bo Tao
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
| | - Jianyi Kong
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
- Precision Manufacturing Research Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Manman Xu
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
- Precision Manufacturing Research Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Baojia Chen
- Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance, China Three Gorges University, Yichang, China
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22
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A Quantum Language-Inspired Tree Structural Text Representation for Semantic Analysis. MATHEMATICS 2022. [DOI: 10.3390/math10060914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Text representation is an important topic in the field of natural language processing, which can effectively transfer knowledge to downstream tasks. To extract effective semantic information from text with unsupervised methods, this paper proposes a quantum language-inspired tree structural text representation model to study the correlations between words with variable distance for semantic analysis. Combining the different semantic contributions of associated words in different syntax trees, a syntax tree-based attention mechanism is established to highlight the semantic contributions of non-adjacent associated words and weaken the semantic weight of adjacent non-associated words. Moreover, the tree-based attention mechanism includes not only the overall information of entangled words in the dictionary but also the local grammatical structure of word combinations in different sentences. Experimental results on semantic textual similarity tasks show that the proposed method obtains significant performances over the state-of-the-art sentence embeddings.
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