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Yao H, Wang L, Zhou X, Jia X, Xiang Q, Zhang W. Predicting the therapeutic efficacy of AIT for asthma using clinical characteristics, serum allergen detection metrics, and machine learning techniques. Comput Biol Med 2023; 166:107544. [PMID: 37866086 DOI: 10.1016/j.compbiomed.2023.107544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 09/07/2023] [Accepted: 09/28/2023] [Indexed: 10/24/2023]
Abstract
Bronchial asthma is a prevalent non-communicable disease among children. The study collected clinical data from 390 children aged 4-17 years with asthma, with or without rhinitis, who received allergen immunotherapy (AIT). Combining these data, this paper proposed a predictive framework for the efficacy of mite subcutaneous immunotherapy in asthma based on machine learning techniques. Introducing the dispersed foraging strategy into the Salp Swarm Algorithm (SSA), a new improved algorithm named DFSSA is proposed. This algorithm effectively alleviates the imbalance between search speed and traversal caused by the fixed partitioning pattern in traditional SSA. Utilizing the fusion of boosting algorithm and kernel extreme learning machine, an AIT performance prediction model was established. To further investigate the effectiveness of the DFSSA-KELM model, this study conducted an auxiliary diagnostic experiment using the immunotherapy predictive medical data collected by the hospital. The findings indicate that selected indicators, such as blood basophil count, sIgE/tIgE (Der p) and sIgE/tIgE (Der f), play a crucial role in predicting treatment outcome. The classification results showed an accuracy of 87.18% and a sensitivity of 93.55%, indicating that the prediction model is an effective and accurate intelligent tool for evaluating the efficacy of AIT.
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Affiliation(s)
- Hao Yao
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Lingya Wang
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Xinyu Zhou
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Xiaoxiao Jia
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Qiangwei Xiang
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China.
| | - Weixi Zhang
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China.
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2
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Gu Y, Chen M, Wang L. A self-learning discrete salp swarm algorithm based on deep reinforcement learning for dynamic job shop scheduling problem. APPL INTELL 2023. [DOI: 10.1007/s10489-023-04479-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2023]
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3
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Wang Z, Ding H, Yang J, Hou P, Dhiman G, Wang J, Yang Z, Li A. Orthogonal pinhole-imaging-based learning salp swarm algorithm with self-adaptive structure for global optimization. Front Bioeng Biotechnol 2022; 10:1018895. [PMID: 36532584 PMCID: PMC9751665 DOI: 10.3389/fbioe.2022.1018895] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 11/17/2022] [Indexed: 09/28/2023] Open
Abstract
Salp swarm algorithm (SSA) is a simple and effective bio-inspired algorithm that is gaining popularity in global optimization problems. In this paper, first, based on the pinhole imaging phenomenon and opposition-based learning mechanism, a new strategy called pinhole-imaging-based learning (PIBL) is proposed. Then, the PIBL strategy is combined with orthogonal experimental design (OED) to propose an OPIBL mechanism that helps the algorithm to jump out of the local optimum. Second, a novel effective adaptive conversion parameter method is designed to enhance the balance between exploration and exploitation ability. To validate the performance of OPLSSA, comparative experiments are conducted based on 23 widely used benchmark functions and 30 IEEE CEC2017 benchmark problems. Compared with some well-established algorithms, OPLSSA performs better in most of the benchmark problems.
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Affiliation(s)
- Zongshan Wang
- School of Information Science and Engineering, Yunnan University, Kunming, China
- University Key Laboratory of Internet of Things Technology and Application, Kunming, China
| | - Hongwei Ding
- School of Information Science and Engineering, Yunnan University, Kunming, China
- University Key Laboratory of Internet of Things Technology and Application, Kunming, China
| | - Jingjing Yang
- School of Information Science and Engineering, Yunnan University, Kunming, China
- University Key Laboratory of Internet of Things Technology and Application, Kunming, China
| | - Peng Hou
- School of Computer Science, Fudan University, Shanghai, China
| | - Gaurav Dhiman
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
- Department of Computer Science and Engineering, University Centre for Research and Development, Chandigarh University, Gharuan, India
- Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, India
| | - Jie Wang
- School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou, China
| | - Zhijun Yang
- School of Information Science and Engineering, Yunnan University, Kunming, China
- University Key Laboratory of Internet of Things Technology and Application, Kunming, China
| | - Aishan Li
- Rackham Graduate School, University of Michigan, Ann Arbor, MI, United States
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4
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Xing J, Zhao H, Chen H, Deng R, Xiao L. Boosting Whale Optimizer with Quasi-Oppositional Learning and Gaussian Barebone for Feature Selection and COVID-19 Image Segmentation. JOURNAL OF BIONIC ENGINEERING 2022; 20:797-818. [PMID: 36466725 PMCID: PMC9707266 DOI: 10.1007/s42235-022-00297-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 10/09/2022] [Accepted: 10/19/2022] [Indexed: 06/17/2023]
Abstract
UNLABELLED Whale optimization algorithm (WOA) tends to fall into the local optimum and fails to converge quickly in solving complex problems. To address the shortcomings, an improved WOA (QGBWOA) is proposed in this work. First, quasi-opposition-based learning is introduced to enhance the ability of WOA to search for optimal solutions. Second, a Gaussian barebone mechanism is embedded to promote diversity and expand the scope of the solution space in WOA. To verify the advantages of QGBWOA, comparison experiments between QGBWOA and its comparison peers were carried out on CEC 2014 with dimensions 10, 30, 50, and 100 and on CEC 2020 test with dimension 30. Furthermore, the performance results were tested using Wilcoxon signed-rank (WS), Friedman test, and post hoc statistical tests for statistical analysis. Convergence accuracy and speed are remarkably improved, as shown by experimental results. Finally, feature selection and multi-threshold image segmentation applications are demonstrated to validate the ability of QGBWOA to solve complex real-world problems. QGBWOA proves its superiority over compared algorithms in feature selection and multi-threshold image segmentation by performing several evaluation metrics. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s42235-022-00297-8.
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Affiliation(s)
- Jie Xing
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou, 325035 China
| | - Hanli Zhao
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou, 325035 China
| | - Huiling Chen
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou, 325035 China
| | - Ruoxi Deng
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou, 325035 China
| | - Lei Xiao
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou, 325035 China
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5
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Zhou B, Bian J. A bi-objective salp swarm algorithm with sine cosine operator for resource constrained multi-manned disassembly line balancing problem. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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6
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A novel multilevel color image segmentation technique based on an improved firefly algorithm and energy curve. EVOLVING SYSTEMS 2022. [DOI: 10.1007/s12530-022-09460-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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7
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Ɖurasević M, Jakobović D. Heuristic and metaheuristic methods for the parallel unrelated machines scheduling problem: a survey. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10247-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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8
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Application of a Non-Dominated Sorting Genetic Algorithm to Solve a Bi-Objective Scheduling Problem Regarding Printed Circuit Boards. MATHEMATICS 2022. [DOI: 10.3390/math10132305] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
An unrelated parallel machine scheduling problem motivated by the scheduling of a printed circuit board assembly (PCBA) under surface mount technology (SMT) is discussed in this paper. This problem involved machine eligibility restrictions, sequence-dependent setup times, precedence constraints, unequal job release times, and constraints of shared resources with the objectives of minimizing the makespan and the total job tardiness. Since this scheduling problem is NP-hard, a mathematical model was first built to describe the problem, and a heuristic approach using a non-dominated sorting genetic algorithm (NSGA-II) was then designed to solve this bi-objective problem. Multiple near-optimal solutions were provided using the Pareto front solution and crowding distance concepts. To demonstrate the efficiency and effectiveness of the proposed approach, this study first tested the proposed approach by solving test problems on a smaller scale. It was found that the proposed approach could obtain optimal solutions for small test problems. A real set of work orders and production data was provided by a famous hardware manufacturer in Taiwan. The solutions suggested by the proposed approach were provided using Gantt charts to visually assist production planners to make decisions. It was found that the proposed approach could not only successfully improve the planning time but also provide several feasible schedules with equivalent performance for production planners to choose from.
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An innovative quadratic interpolation salp swarm-based local escape operator for large-scale global optimization problems and feature selection. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07391-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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10
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Ding H, Cao X, Wang Z, Dhiman G, Hou P, Wang J, Li A, Hu X. Velocity clamping-assisted adaptive salp swarm algorithm: balance analysis and case studies. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:7756-7804. [PMID: 35801444 DOI: 10.3934/mbe.2022364] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Salp swarm algorithm (SSA) is a recently proposed, powerful swarm-intelligence based optimizer, which is inspired by the unique foraging style of salps in oceans. However, the original SSA suffers from some limitations including immature balance between exploitation and exploration operators, slow convergence and local optimal stagnation. To alleviate these deficiencies, a modified SSA (called VC-SSA) with velocity clamping strategy, reduction factor tactic, and adaptive weight mechanism is developed. Firstly, a novel velocity clamping mechanism is designed to boost the exploitation ability and the solution accuracy. Next, a reduction factor is arranged to bolster the exploration capability and accelerate the convergence speed. Finally, a novel position update equation is designed by injecting an inertia weight to catch a better balance between local and global search. 23 classical benchmark test problems, 30 complex optimization tasks from CEC 2017, and five engineering design problems are employed to authenticate the effectiveness of the developed VC-SSA. The experimental results of VC-SSA are compared with a series of cutting-edge metaheuristics. The comparisons reveal that VC-SSA provides better performance against the canonical SSA, SSA variants, and other well-established metaheuristic paradigms. In addition, VC-SSA is utilized to handle a mobile robot path planning task. The results show that VC-SSA can provide the best results compared to the competitors and it can serve as an auxiliary tool for mobile robot path planning.
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Affiliation(s)
- Hongwei Ding
- School of Information Science and Engineering, Yunnan University, Kunming 650500, China
- University Key Laboratory of Internet of Things Technology and Application, Yunnan Province, Kunming 650500, China
| | - Xingguo Cao
- School of Information Science and Engineering, Yunnan University, Kunming 650500, China
- University Key Laboratory of Internet of Things Technology and Application, Yunnan Province, Kunming 650500, China
| | - Zongshan Wang
- School of Information Science and Engineering, Yunnan University, Kunming 650500, China
- University Key Laboratory of Internet of Things Technology and Application, Yunnan Province, Kunming 650500, China
| | - Gaurav Dhiman
- Department of Computer Science, Government Bikram College of Commerce, Patiala, India
- University Centre for Research and Development, Department of Computer Science and Engineering, Chandigarh University, Gharuan, Mohali, India
- Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, India
| | - Peng Hou
- School of Computer Science, Fudan University, Shanghai 200433, China
| | - Jie Wang
- School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450000, China
| | - Aishan Li
- Rackham Graduate School, University of Michigan, Ann Arbor, USA
| | - Xiang Hu
- College of Information, Shanghai Ocean University, Shanghai, China
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Huang X, Chen L, Zhang Y, Su S, Lin Y, Cao X. Improved firefly algorithm with courtship learning for unrelated parallel machine scheduling problem with sequence-dependent setup times. JOURNAL OF CLOUD COMPUTING: ADVANCES, SYSTEMS AND APPLICATIONS 2022. [DOI: 10.1186/s13677-022-00282-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
AbstractThe Unrelated Parallel Machines Scheduling Problem (UPMSP) with sequence-dependent setup times has been widely applied to cloud computing, edge computing and so on. When the setup times are ignored, UPMSP will be a NP problem. Moreover, when considering the sequence related setup times, UPMSP is difficult to solve, and this situation will be more serious in the case of high-dimensional. This work firstly select the maximum completion time as the optimization objective, which establishes a mathematical model of UPMSP with sequence-dependent setup times. In addition, an improved firefly algorithm with courtship learning is proposed. Finally, in order to provide an approximate solution in an acceptable time, the proposed algorithm is applied to solve the UPMSP with sequence-dependent setup times. The experimental results show that the proposed algorithm has competitive performance when dealing with UPMSP with sequence-dependent setup times.
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Zheng R, Jia H, Abualigah L, Wang S, Wu D. An improved remora optimization algorithm with autonomous foraging mechanism for global optimization problems. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:3994-4037. [PMID: 35341284 DOI: 10.3934/mbe.2022184] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The remora optimization algorithm (ROA) is a newly proposed metaheuristic algorithm for solving global optimization problems. In ROA, each search agent searches new space according to the position of host, which makes the algorithm suffer from the drawbacks of slow convergence rate, poor solution accuracy, and local optima for some optimization problems. To tackle these problems, this study proposes an improved ROA (IROA) by introducing a new mechanism named autonomous foraging mechanism (AFM), which is inspired from the fact that remora can also find food on its own. In AFM, each remora has a small chance to search food randomly or according to the current food position. Thus the AFM can effectively expand the search space and improve the accuracy of the solution. To substantiate the efficacy of the proposed IROA, twenty-three classical benchmark functions and ten latest CEC 2021 test functions with various types and dimensions were employed to test the performance of IROA. Compared with seven metaheuristic and six modified algorithms, the results of test functions show that the IROA has superior performance in solving these optimization problems. Moreover, the results of five representative engineering design optimization problems also reveal that the IROA has the capability to obtain the optimal results for real-world optimization problems. To sum up, these test results confirm the effectiveness of the proposed mechanism.
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Affiliation(s)
- Rong Zheng
- School of Information Engineering, Sanming University, Sanming 365004, China
| | - Heming Jia
- School of Information Engineering, Sanming University, Sanming 365004, China
| | - Laith Abualigah
- Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan
- School of Computer Science, Universiti Sains Malaysia, Penang 11800, Malaysia
| | - Shuang Wang
- School of Information Engineering, Sanming University, Sanming 365004, China
| | - Di Wu
- School of Education and Music, Sanming University, Sanming 365004, China
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13
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Zhang H, Liu T, Ye X, Heidari AA, Liang G, Chen H, Pan Z. Differential evolution-assisted salp swarm algorithm with chaotic structure for real-world problems. ENGINEERING WITH COMPUTERS 2022; 39:1735-1769. [PMID: 35035007 PMCID: PMC8743356 DOI: 10.1007/s00366-021-01545-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 11/02/2021] [Indexed: 06/02/2023]
Abstract
There is a new nature-inspired algorithm called salp swarm algorithm (SSA), due to its simple framework, it has been widely used in many fields. But when handling some complicated optimization problems, especially the multimodal and high-dimensional optimization problems, SSA will probably have difficulties in convergence performance or dropping into the local optimum. To mitigate these problems, this paper presents a chaotic SSA with differential evolution (CDESSA). In the proposed framework, chaotic initialization and differential evolution are introduced to enrich the convergence speed and accuracy of SSA. Chaotic initialization is utilized to produce a better initial population aim at locating a better global optimal. At the same time, differential evolution is used to build up the search capability of each agent and improve the sense of balance of global search and intensification of SSA. These mechanisms collaborate to boost SSA in accelerating convergence activity. Finally, a series of experiments are carried out to test the performance of CDESSA. Firstly, IEEE CEC2014 competition fuctions are adopted to evaluate the ability of CDESSA in working out the real-parameter optimization problems. The proposed CDESSA is adopted to deal with feature selection (FS) problems, then five constrained engineering optimization problems are also adopted to evaluate the property of CDESSA in dealing with real engineering scenarios. Experimental results reveal that the proposed CDESSA method performs significantly better than the original SSA and other compared methods.
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Affiliation(s)
- Hongliang Zhang
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035 China
| | - Tong Liu
- College of Computer Science and Technology, Jilin University, Changchun, 130012 China
| | - Xiaojia Ye
- Shanghai Lixin University of Accounting and Finance, Shanghai, 201209 China
| | - Ali Asghar Heidari
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035 China
| | - Guoxi Liang
- Department of Information Technology, Wenzhou Polytechnic, Wenzhou, 325035 China
| | - Huiling Chen
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035 China
| | - Zhifang Pan
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000 People’s Republic of China
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Qawqzeh Y, Alharbi MT, Jaradat A, Abdul Sattar KN. A review of swarm intelligence algorithms deployment for scheduling and optimization in cloud computing environments. PeerJ Comput Sci 2021; 7:e696. [PMID: 34541313 PMCID: PMC8409329 DOI: 10.7717/peerj-cs.696] [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: 05/20/2021] [Accepted: 08/05/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND This review focuses on reviewing the recent publications of swarm intelligence algorithms (particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), and the firefly algorithm (FA)) in scheduling and optimization problems. Swarm intelligence (SI) can be described as the intelligent behavior of natural living animals, fishes, and insects. In fact, it is based on agent groups or populations in which they have a reliable connection among them and with their environment. Inside such a group or population, each agent (member) performs according to certain rules that make it capable of maximizing the overall utility of that certain group or population. It can be described as a collective intelligence among self-organized members in certain group or population. In fact, biology inspired many researchers to mimic the behavior of certain natural swarms (birds, animals, or insects) to solve some computational problems effectively. METHODOLOGY SI techniques were utilized in cloud computing environment seeking optimum scheduling strategies. Hence, the most recent publications (2015-2021) that belongs to SI algorithms are reviewed and summarized. RESULTS It is clear that the number of algorithms for cloud computing optimization is increasing rapidly. The number of PSO, ACO, ABC, and FA related journal papers has been visibility increased. However, it is noticeably that many recently emerging algorithms were emerged based on the amendment on the original SI algorithms especially the PSO algorithm. CONCLUSIONS The major intention of this work is to motivate interested researchers to develop and innovate new SI-based solutions that can handle complex and multi-objective computational problems.
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Affiliation(s)
- Yousef Qawqzeh
- Department of Computer Science and Engineering, Hafr Al Batin University, Hafr AL Batin, Saudi Arabia
| | - Mafawez T. Alharbi
- Department of Natural and Applied Sciences, Buraydah Community College, Qassim University, Buraydeh, Qassim, Saudi Arabia
| | - Ayman Jaradat
- Computer Science and Information Department, Majmaah University, AlZulfi, Riyadh, Saudi Arabia
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