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Rajwar K, Deep K, Das S. An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges. Artif Intell Rev 2023; 56:1-71. [PMID: 37362893 PMCID: PMC10103682 DOI: 10.1007/s10462-023-10470-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2023]
Abstract
As the world moves towards industrialization, optimization problems become more challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms (MAs) have been developed to date, with over 350 of them appearing in the last decade. The literature has grown significantly in recent years and should be thoroughly reviewed. In this study, approximately 540 MAs are tracked, and statistical information is also provided. Due to the proliferation of MAs in recent years, the issue of substantial similarities between algorithms with different names has become widespread. This raises an essential question: can an optimization technique be called 'novel' if its search properties are modified or almost equal to existing methods? Many recent MAs are said to be based on 'novel ideas', so they are discussed. Furthermore, this study categorizes MAs based on the number of control parameters, which is a new taxonomy in the field. MAs have been extensively employed in various fields as powerful optimization tools, and some of their real-world applications are demonstrated. A few limitations and open challenges have been identified, which may lead to a new direction for MAs in the future. Although researchers have reported many excellent results in several research papers, review articles, and monographs during the last decade, many unexplored places are still waiting to be discovered. This study will assist newcomers in understanding some of the major domains of metaheuristics and their real-world applications. We anticipate this resource will also be useful to our research community.
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Affiliation(s)
- Kanchan Rajwar
- Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667 India
| | - Kusum Deep
- Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667 India
| | - Swagatam Das
- Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, West Bengal 700108 India
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Pachung P, Bansal JC. An improved Tangent Search Algorithm. MethodsX 2022; 9:101839. [PMID: 36160108 PMCID: PMC9489808 DOI: 10.1016/j.mex.2022.101839] [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: 06/13/2022] [Accepted: 08/26/2022] [Indexed: 11/25/2022] Open
Abstract
The Tangent Search Algorithm (TSA) is a newly developed population-based meta-heuristic algorithm to solve complex optimization problems. It is based on the tangent function, which steers the given solution towards more promising regions of the search space. Though TSA has performed well for many optimization problems, the experimental analyses show that it suffers from the low exploration ability and slow convergence rate. This article proposes an improved TSA algorithm (iTSA). Using two concepts, ‘Fitness Weighted Search Strategy’ (FWSS) and ‘Opposition Based learning’ (OBL), iTSA is better in terms of exploration while maintaining the high convergence rate of TSA.Fitness weighted search strategy (FWSS) is used to increase the exploration ability of TSA. Opposition based learning (OBL) is used to increase the convergence speed of TSA. Together, OBL and FWSS into iTSA outperformed the classical TSA and other considered state-of-the-art algorithms.
The performance of the proposed iTSA is validated on two sets of test functions: CEC14 benchmark functions and a set of 21 well-known classical benchmark functions. The obtained results are compared with those obtained from the basic TSA and other considered state-of-the-art algorithms.
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Alyasseri ZAA, Alomari OA, Al-Betar MA, Makhadmeh SN, Doush IA, Awadallah MA, Abasi AK, Elnagar A. Recent advances of bat-inspired algorithm, its versions and applications. Neural Comput Appl 2022; 34:16387-16422. [PMID: 35971379 PMCID: PMC9366842 DOI: 10.1007/s00521-022-07662-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 07/18/2022] [Indexed: 11/25/2022]
Affiliation(s)
- Zaid Abdi Alkareem Alyasseri
- ECE Department, Faculty of Engineering, University of Kufa, P.O. Box 21, Najaf, Iraq
- College of Engineering, University of Warith Al-Anbiyaa, Karbala, Iraq
- Information Research and Development Center (ITRDC), University of Kufa, Najaf, Iraq
| | | | - Mohammed Azmi Al-Betar
- Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates
- Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, Irbid, Jordan
| | - Sharif Naser Makhadmeh
- Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates
| | - Iyad Abu Doush
- Department of Computing, College of Engineering and Applied Sciences, American University of Kuwait, Salmiya, Kuwait
- Computer Science Department, Yarmouk University, Irbid, Jordan
| | - Mohammed A. Awadallah
- Department of Computer Science, Al-Aqsa University, P.O. Box 4051, Gaza, Palestine
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, United Arab Emirates
| | - Ammar Kamal Abasi
- Machine Learning Department, Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab Emirates
| | - Ashraf Elnagar
- Department of Computer Science, University of Sharjah, Sharjah, United Arab Emirates
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Nama S, Saha AK, Sharma S. Performance up-gradation of Symbiotic Organisms Search by Backtracking Search Algorithm. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022; 13:5505-5546. [PMID: 33868507 PMCID: PMC8036246 DOI: 10.1007/s12652-021-03183-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 03/25/2021] [Indexed: 05/08/2023]
Abstract
Symbiotic Organisms Search (SOS) algorithm is characterized based on the framework of relationships among the ecosystem species. Nevertheless, it is suffering from wasteful discovery, little productivity, and slack convergence rate. These deficiencies cause stagnation at the local optimum, which is hazardous in deciding the genuine optima of the optimization problem. Backtracking Search Algorithm (BSA) is likewise another streamlining method for comprehending the non-direct complex optimization problem. Consequently, in the current paper, an endeavor has been made toward the expulsion of the downsides from the traditional SOS by proposing a novel ensemble technique called e-SOSBSA to overhaul the degree of intensification and diversification. In e-SOSBSA, firstly, the mutation operator of BSA with the self-adaptive mutation rate is incorporated to produce a mutant of population and leap out from the local optima. Secondly, the crossover operator of BSA with the adaptive component of mixrate is incorporated to leverage the entire active search regions visited previously. The suggested e-SOSBSA has been tested with 20 classical benchmark functions, IEEE CEC2014, CEC2015, CEC2017, and the latest CEC 2020 test functions. Statistical analyses, convergence analysis, and diversity analysis are performed to show the stronger search capabilities of the proposed e-SOSBSA in contrast with the component algorithms and several state-of-the-art algorithms. Moreover, the proposed e-SOSBSA is applied to find the optimum value of the seven problems of engineering optimization. The numerical investigations and examinations show that the proposed e-SOSBSA can be profoundly viable in tackling real-world engineering optimization problems.
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Affiliation(s)
- Sukanta Nama
- Department of Applied Mathematics, Maharaja Bir Bikram University, Agartala, Tripura India
- 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
| | - Sushmita Sharma
- Department of Mathematics, National Institute of Technology Agartala, Agartala, Tripura 799046 India
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Piotrowski AP, Piotrowska AE. Differential evolution and particle swarm optimization against COVID-19. Artif Intell Rev 2021; 55:2149-2219. [PMID: 34426713 PMCID: PMC8374127 DOI: 10.1007/s10462-021-10052-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/17/2021] [Indexed: 11/29/2022]
Abstract
COVID-19 disease, which highly affected global life in 2020, led to a rapid scientific response. Versatile optimization methods found their application in scientific studies related to COVID-19 pandemic. Differential Evolution (DE) and Particle Swarm Optimization (PSO) are two metaheuristics that for over two decades have been widely researched and used in various fields of science. In this paper a survey of DE and PSO applications for problems related with COVID-19 pandemic that were rapidly published in 2020 is presented from two different points of view: 1. practitioners seeking the appropriate method to solve particular problem, 2. experts in metaheuristics that are interested in methodological details, inter comparisons between different methods, and the ways for improvement. The effectiveness and popularity of DE and PSO is analyzed in the context of other metaheuristics used against COVID-19. It is found that in COVID-19 related studies: 1. DE and PSO are most frequently used for calibration of epidemiological models and image-based classification of patients or symptoms, but applications are versatile, even interconnecting the pandemic and humanities; 2. reporting on DE or PSO methodological details is often scarce, and the choices made are not necessarily appropriate for the particular algorithm or problem; 3. mainly the basic variants of DE and PSO that were proposed in the late XX century are applied, and research performed in recent two decades is rather ignored; 4. the number of citations and the availability of codes in various programming languages seems to be the main factors for choosing metaheuristics that are finally used.
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Affiliation(s)
- Adam P. Piotrowski
- Institute of Geophysics, Polish Academy of Sciences, Ks. Janusza 64, 01-452 Warsaw, Poland
| | - Agnieszka E. Piotrowska
- Faculty of Polish Studies, University of Warsaw, Krakowskie Przedmiescie 26/28, 00-927 Warsaw, Poland
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Ezugwu AE, Shukla AK, Nath R, Akinyelu AA, Agushaka JO, Chiroma H, Muhuri PK. Metaheuristics: a comprehensive overview and classification along with bibliometric analysis. Artif Intell Rev 2021. [DOI: 10.1007/s10462-020-09952-0] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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A Comprehensive Survey of the Harmony Search Algorithm in Clustering Applications. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10113827] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The Harmony Search Algorithm (HSA) is a swarm intelligence optimization algorithm which has been successfully applied to a broad range of clustering applications, including data clustering, text clustering, fuzzy clustering, image processing, and wireless sensor networks. We provide a comprehensive survey of the literature on HSA and its variants, analyze its strengths and weaknesses, and suggest future research directions.
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Naji Alwerfali HS, A A Al-Qaness M, Abd Elaziz M, Ewees AA, Oliva D, Lu S. Multi-Level Image Thresholding Based on Modified Spherical Search Optimizer and Fuzzy Entropy. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E328. [PMID: 33286101 PMCID: PMC7516786 DOI: 10.3390/e22030328] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2020] [Revised: 03/05/2020] [Accepted: 03/10/2020] [Indexed: 11/17/2022]
Abstract
Multi-level thresholding is one of the effective segmentation methods that have been applied in many applications. Traditional methods face challenges in determining the suitable threshold values; therefore, metaheuristic (MH) methods have been adopted to solve these challenges. In general, MH methods had been proposed by simulating natural behaviors of swarm ecosystems, such as birds, animals, and others. The current study proposes an alternative multi-level thresholding method based on a new MH method, a modified spherical search optimizer (SSO). This was performed by using the operators of the sine cosine algorithm (SCA) to enhance the exploitation ability of the SSO. Moreover, Fuzzy entropy is applied as the main fitness function to evaluate the quality of each solution inside the population of the proposed SSOSCA since Fuzzy entropy has established its performance in literature. Several images from the well-known Berkeley dataset were used to test and evaluate the proposed method. The evaluation outcomes approved that SSOSCA showed better performance than several existing methods according to different image segmentation measures.
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Affiliation(s)
- Husein S Naji Alwerfali
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Mohammed A A Al-Qaness
- State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
| | - Mohamed Abd Elaziz
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
| | - Ahmed A Ewees
- Department of Computer, Damietta University, Damietta 34517, Egypt
| | - Diego Oliva
- Depto. de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara C.P. 44100, Jalisco, Mexico
| | - Songfeng Lu
- Hubei Engineering Research Center on Big Data Security, School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
- Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen 518063, China
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Elaziz MA, Ewees AA, Yousri D, Alwerfali HSN, Awad QA, Lu S, Al-Qaness MAA. An Improved Marine Predators Algorithm With Fuzzy Entropy for Multi-Level Thresholding: Real World Example of COVID-19 CT Image Segmentation. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:125306-125330. [PMID: 34192114 PMCID: PMC8043509 DOI: 10.1109/access.2020.3007928] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 07/04/2020] [Indexed: 05/04/2023]
Abstract
Medical imaging techniques play a critical role in diagnosing diseases and patient healthcare. They help in treatment, diagnosis, and early detection. Image segmentation is one of the most important steps in processing medical images, and it has been widely used in many applications. Multi-level thresholding (MLT) is considered as one of the simplest and most effective image segmentation techniques. Traditional approaches apply histogram methods; however, these methods face some challenges. In recent years, swarm intelligence methods have been leveraged in MLT, which is considered an NP-hard problem. One of the main drawbacks of the SI methods is when searching for optimum solutions, and some may get stuck in local optima. This because during the run of SI methods, they create random sequences among different operators. In this study, we propose a hybrid SI based approach that combines the features of two SI methods, marine predators algorithm (MPA) and moth-?ame optimization (MFO). The proposed approach is called MPAMFO, in which, the MFO is utilized as a local search method for MPA to avoid trapping at local optima. The MPAMFO is proposed as an MLT approach for image segmentation, which showed excellent performance in all experiments. To test the performance of MPAMFO, two experiments were carried out. The first one is to segment ten natural gray-scale images. The second experiment tested the MPAMFO for a real-world application, such as CT images of COVID-19. Therefore, thirteen CT images were used to test the performance of MPAMFO. Furthermore, extensive comparisons with several SI methods have been implemented to examine the quality and the performance of the MPAMFO. Overall experimental results confirm that the MPAMFO is an efficient MLT approach that approved its superiority over other existing methods.
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Affiliation(s)
- Mohamed Abd Elaziz
- Department of MathematicsFaculty of ScienceZagazig UniversityZagazig44519Egypt
| | - Ahmed A Ewees
- Department of ComputerDamietta UniversityDamietta34511Egypt
| | - Dalia Yousri
- Electrical Engineering DepartmentFaculty of EngineeringFayoum UniversityFaiyum63514Egypt
| | - Husein S Naji Alwerfali
- School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan430074China
| | - Qamar A Awad
- Department of MathematicsFaculty of ScienceZagazig UniversityZagazig44519Egypt
| | - Songfeng Lu
- School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan430074China
- Hubei Engineering Research Center on Big Data Security, School of Cyber Science and EngineeringHuazhong university of Science and TechnologyWuhan430074China
| | - Mohammed A A Al-Qaness
- State Key Laboratory for Information Engineering in Surveying, Mapping, and Remote SensingWuhan UniversityWuhan430079China
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