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Abdel-Salam M, Hu G, Çelik E, Gharehchopogh FS, El-Hasnony IM. Chaotic RIME optimization algorithm with adaptive mutualism for feature selection problems. Comput Biol Med 2024; 179:108803. [PMID: 38955125 DOI: 10.1016/j.compbiomed.2024.108803] [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: 04/15/2024] [Revised: 05/17/2024] [Accepted: 06/24/2024] [Indexed: 07/04/2024]
Abstract
The RIME optimization algorithm is a newly developed physics-based optimization algorithm used for solving optimization problems. The RIME algorithm proved high-performing in various fields and domains, providing a high-performance solution. Nevertheless, like many swarm-based optimization algorithms, RIME suffers from many limitations, including the exploration-exploitation balance not being well balanced. In addition, the likelihood of falling into local optimal solutions is high, and the convergence speed still needs some work. Hence, there is room for enhancement in the search mechanism so that various search agents can discover new solutions. The authors suggest an adaptive chaotic version of the RIME algorithm named ACRIME, which incorporates four main improvements, including an intelligent population initialization using chaotic maps, a novel adaptive modified Symbiotic Organism Search (SOS) mutualism phase, a novel mixed mutation strategy, and the utilization of restart strategy. The main goal of these improvements is to improve the variety of the population, achieve a better balance between exploration and exploitation, and improve RIME's local and global search abilities. The study assesses the effectiveness of ACRIME by using the standard benchmark functions of the CEC2005 and CEC2019 benchmarks. The proposed ACRIME is also applied as a feature selection to fourteen various datasets to test its applicability to real-world problems. Besides, the ACRIME algorithm is applied to the COVID-19 classification real problem to test its applicability and performance further. The suggested algorithm is compared to other sophisticated classical and advanced metaheuristics, and its performance is assessed using statistical tests such as Wilcoxon rank-sum and Friedman rank tests. The study demonstrates that ACRIME exhibits a high level of competitiveness and often outperforms competing algorithms. It discovers the optimal subset of features, enhancing the accuracy of classification and minimizing the number of features employed. This study primarily focuses on enhancing the equilibrium between exploration and exploitation, extending the scope of local search.
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Affiliation(s)
- Mahmoud Abdel-Salam
- Faculty of Computer and Information Science, Mansoura University, Mansoura, 35516, Egypt.
| | - Gang Hu
- Department of Applied Mathematics, Xi'an University of Technology, Xi'an, 710054, PR China
| | - Emre Çelik
- Department of Electrical and Electronics Engineering, Faculty of Engineering, Düzce University, Düzce, Turkey
| | | | - Ibrahim M El-Hasnony
- Faculty of Computer and Information Science, Mansoura University, Mansoura, 35516, Egypt
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2
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PSO-ELPM: PSO with Elite Learning, enhanced Parameter updating, and exponential Mutation operator. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.01.103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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3
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Enhanced chimp optimization algorithm for high level synthesis of digital filters. Sci Rep 2022; 12:21389. [PMID: 36496419 PMCID: PMC9741637 DOI: 10.1038/s41598-022-24343-x] [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: 06/20/2022] [Accepted: 11/14/2022] [Indexed: 12/13/2022] Open
Abstract
The HLS of digital filters is a complex optimization task in electronic design automation that increases the level of abstraction for designing and scheming digital circuits. The complexity of this issue attracting the interest of the researcher and solution of this issue is a big challenge for the researcher. The scientists are trying to present the various most powerful methods for this issue, but keep in mind these methods could be trapped in the complex space of this problem due to own weaknesses. Due to shortcomings of these methods, we are trying to design a new framework with the mixture of the phases of the powerful approaches for high level synthesis of digital filters in this work. This modification has been done by merging the chimp optimizer with sine cosine functions. The sine cosine phases helped in enhancing the exploitation phase of the chimp optimizer and also ignored the local optima in the search area during the searching of new shortest paths. The algorithms have been applied on 23-standard test suites and 14-digital filters for verifying the performance of the algorithms. Experimental results of single and multi-objective functions have been compared in terms of best score, best maxima, average, standard deviation, execution time, occupied area and speed respectively. Furthermore, by analyzing the effectiveness of the proposed algorithm with the recent algorithms for the HLS digital filters design, this can be concluded that the proposed method dominates the other two methods in HLS digital filters design. Another prominent feature of the proposed system in addition to the stated enhancement, is its rapid runtime, lowest delay, occupied area and lowest power in achieving an appropriate response. This could greatly reduce the cost of systems with broad dimensions while increasing the design speed.
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4
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Shaban WM. Insight into breast cancer detection: new hybrid feature selection method. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-08062-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
AbstractBreast cancer, which is also the leading cause of death among women, is one of the most common forms of the disease that affects females all over the world. The discovery of breast cancer at an early stage is extremely important because it allows selecting appropriate treatment protocol and thus, stops the development of cancer cells. In this paper, a new patients detection strategy has been presented to identify patients with the disease earlier. The proposed strategy composes of two parts which are data preprocessing phase and patient detection phase (PDP). The purpose of this study is to introduce a feature selection methodology for determining the most efficient and significant features for identifying breast cancer patients. This method is known as new hybrid feature selection method (NHFSM). NHFSM is made up of two modules which are quick selection module that uses information gain, and feature selection module that uses hybrid bat algorithm and particle swarm optimization. Consequently, NHFSM is a hybrid method that combines the advantages of bat algorithm and particle swarm optimization based on filter method to eliminate many drawbacks such as being stuck in a local optimal solution and having unbalanced exploitation. The preprocessed data are then used during PDP in order to enable a quick and accurate detection of patients. Based on experimental results, the proposed NHFSM improves the efficiency of patients’ classification in comparison with state-of-the-art feature selection approaches by roughly 0.97, 0.76, 0.75, and 0.716 in terms of accuracy, precision, sensitivity/recall, and F-measure. In contrast, it has the lowest error rate value of 0.03.
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Singh N, Houssein EH, Mirjalili S, Cao Y, Selvachandran G. An efficient improved African vultures optimization algorithm with dimension learning hunting for traveling salesman and large‐scale optimization applications. INT J INTELL SYST 2022. [DOI: 10.1002/int.23091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Affiliation(s)
- Narinder Singh
- Department of Mathematics Punjabi University Patiala Punjab India
| | | | - Seyedali Mirjalili
- Institute for Integrated and Intelligent Systems Griffith University Nathan Queensland Australia
| | - Yankai Cao
- Faculty of Applied Science, UBC Chemical and Biological Engineering The University of British Columbia Vancouver British Columbia Canada
| | - Ganeshsree Selvachandran
- Department of Actuarial Science and Applied Statistics, Faculty of Business and Management UCSI University, Jalan Menara Gading Cheras Kuala Lumpur Malaysia
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6
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Yu F, Tong L, Xia X. Adjustable driving force based particle swarm optimization algorithm. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.07.067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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7
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A novel hybrid of chimp with cuckoo search algorithm for the optimal designing of digital infinite impulse response filter using high-level synthesis. Soft comput 2022. [DOI: 10.1007/s00500-022-07410-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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8
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Machine Leaning-Based Optimization Algorithm for Myocardial Injury under High-Intensity Exercise in Track and Field Athletes. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7792958. [PMID: 35586102 PMCID: PMC9110131 DOI: 10.1155/2022/7792958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 04/08/2022] [Accepted: 04/19/2022] [Indexed: 11/29/2022]
Abstract
In order to train at high-intensity, athletics can again cause varying degrees of myocardial damage. Evaluating the balance between exercise myocardial injury and exercise intensity should actively prevent myocardial injury caused by high-intensity athletic training. In this paper, an intelligent optimization algorithm is used to investigate the degree of myocardial injury. The basic idea is to define the measured data and the output of the numerical model as an objective function of the structural parameters, to obtain the structural parameters by finding ways to continuously optimize the objective function to be close to the observed values, and to identify the injury based on the changes in these parameters before and after myocardial injury. The objective function can be defined in various ways, and the myocardial injury optimization algorithm can be chosen. In order to obtain the best computational results, numerical simulations of damage identification are performed using the objective function and three machine learning-based optimization algorithms. The computational results show that the combination of the objective function and the machine learning algorithms provides good accuracy and computational speed in identifying myocardial injury.
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Abed-alguni BH, Paul D, Hammad R. Improved Salp swarm algorithm for solving single-objective continuous optimization problems. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03269-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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10
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Chen D, Liu J, Yao C, Zhang Z, Du X. Multi-strategy improved salp swarm algorithm and its application in reliability optimization. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:5269-5292. [PMID: 35430864 DOI: 10.3934/mbe.2022247] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
To improve the convergence speed and solution precision of the standard Salp Swarm Algorithm (SSA), a hybrid Salp Swarm Algorithm based on Dimension-by-dimension Centroid Opposition-based learning strategy, Random factor and Particle Swarm Optimization's social learning strategy (DCORSSA-PSO) is proposed. Firstly, a dimension-by-dimension centroid opposition-based learning strategy is added in the food source update stage of SSA to increase the population diversity and reduce the inter-dimensional interference. Secondly, in the followers' position update equation of SSA, constant 1 is replaced by a random number between 0 and 1 to increase the randomness of the search and the ability to jump out of local optima. Finally, the social learning strategy of PSO is also added to the followers' position update equation to accelerate the population convergence. The statistical results on ten classical benchmark functions by the Wilcoxon test and Friedman test show that compared with SSA and other well-known optimization algorithms, the proposed DCORSSA-PSO has significantly improved the precision of the solution and the convergence speed, as well as its robustness. The DCORSSA-PSO is applied to system reliability optimization design based on the T-S fault tree. The simulation results show that the failure probability of the designed system under the cost constraint is less than other algorithms, which illustrates that the application of DCORSSA-PSO can effectively improve the design level of reliability optimization.
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Affiliation(s)
- Dongning Chen
- Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao 066004, China
- Key Laboratory of Advanced Forging & Stamping Technology and Science (Yanshan University), Ministry of Education of China, Qinhuangdao 066004, China
| | - Jianchang Liu
- Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao 066004, China
- Key Laboratory of Advanced Forging & Stamping Technology and Science (Yanshan University), Ministry of Education of China, Qinhuangdao 066004, China
| | - Chengyu Yao
- Key Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004, China
| | - Ziwei Zhang
- Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao 066004, China
- Key Laboratory of Advanced Forging & Stamping Technology and Science (Yanshan University), Ministry of Education of China, Qinhuangdao 066004, China
| | - Xinwei Du
- Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao 066004, China
- Key Laboratory of Advanced Forging & Stamping Technology and Science (Yanshan University), Ministry of Education of China, Qinhuangdao 066004, China
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11
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Singh R, Kaur R. A Novel Archimedes Optimization Algorithm with Levy Flight for Designing Microstrip Patch Antenna. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022. [DOI: 10.1007/s13369-021-06307-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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12
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Kaur N, Kaur L, Cheema SS. An enhanced version of Harris Hawks Optimization by dimension learning-based hunting for Breast Cancer Detection. Sci Rep 2021; 11:21933. [PMID: 34753979 PMCID: PMC8578615 DOI: 10.1038/s41598-021-01018-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 10/18/2021] [Indexed: 11/08/2022] Open
Abstract
Swarm intelligence techniques have a vast range of real world applications.Some applications are in the domain of medical data mining where, main attention is on structure models for the classification and expectation of numerous diseases. These biomedical applications have grabbed the interest of numerous researchers because these are most serious and prevalent causes of death among the human whole world out of which breast cancer is the most serious issue. Mammography is the initial screening assessment of breast cancer. In this study, an enhanced version of Harris Hawks Optimization (HHO) approach has been developed for biomedical databases, known as DLHO. This approach has been introduced by integrating the merits of dimension learning-based hunting (DLH) search strategy with HHO. The main objective of this study is to alleviate the lack of crowd diversity, premature convergence of the HHO and the imbalance amid the exploration and exploitation. DLH search strategy utilizes a dissimilar method to paradigm a neighborhood for each search member in which the neighboring information can be shared amid search agents. This strategy helps in maintaining the diversity and the balance amid global and local search. To evaluate the DLHO lot of experiments have been taken such as (i) the performance of optimizers have analysed by using 29-CEC -2017 test suites, (ii) to demonstrate the effectiveness of the DLHO it has been tested on different biomedical databases out of which we have used two different databases for Breast i.e. MIAS and second database has been taken from the University of California at Irvine (UCI) Machine Learning Repository.Also to test the robustness of the proposed method its been tested on two other databases of such as Balloon and Heart taken from the UCI Machine Learning Repository. All the results are in the favour of the proposed technique.
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Affiliation(s)
- Navneet Kaur
- Department of Computer Science and Engineering, Punjabi University, Patiala, Patiala, 147002, India.
| | - Lakhwinder Kaur
- Department of Computer Science and Engineering, Punjabi University, Patiala, Patiala, 147002, India
| | - Sikander Singh Cheema
- Department of Computer Science and Engineering, Punjabi University, Patiala, Patiala, 147002, India
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13
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Wang S, Liu Q, Liu Y, Jia H, Abualigah L, Zheng R, Wu D. A Hybrid SSA and SMA with Mutation Opposition-Based Learning for Constrained Engineering Problems. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:6379469. [PMID: 34531910 PMCID: PMC8440113 DOI: 10.1155/2021/6379469] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Accepted: 08/19/2021] [Indexed: 11/18/2022]
Abstract
Based on Salp Swarm Algorithm (SSA) and Slime Mould Algorithm (SMA), a novel hybrid optimization algorithm, named Hybrid Slime Mould Salp Swarm Algorithm (HSMSSA), is proposed to solve constrained engineering problems. SSA can obtain good results in solving some optimization problems. However, it is easy to suffer from local minima and lower density of population. SMA specializes in global exploration and good robustness, but its convergence rate is too slow to find satisfactory solutions efficiently. Thus, in this paper, considering the characteristics and advantages of both the above optimization algorithms, SMA is integrated into the leader position updating equations of SSA, which can share helpful information so that the proposed algorithm can utilize these two algorithms' advantages to enhance global optimization performance. Furthermore, Levy flight is utilized to enhance the exploration ability. It is worth noting that a novel strategy called mutation opposition-based learning is proposed to enhance the performance of the hybrid optimization algorithm on premature convergence avoidance, balance between exploration and exploitation phases, and finding satisfactory global optimum. To evaluate the efficiency of the proposed algorithm, HSMSSA is applied to 23 different benchmark functions of the unimodal and multimodal types. Additionally, five classical constrained engineering problems are utilized to evaluate the proposed technique's practicable abilities. The simulation results show that the HSMSSA method is more competitive and presents more engineering effectiveness for real-world constrained problems than SMA, SSA, and other comparative algorithms. In the end, we also provide some potential areas for future studies such as feature selection and multilevel threshold image segmentation.
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Affiliation(s)
- Shuang Wang
- School of Information Engineering, Sanming University, Sanming 365004, China
| | - Qingxin Liu
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Yuxiang Liu
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, 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, Gelugor, Penang, Malaysia
| | - Rong Zheng
- 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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14
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Zhou S, Han Y, Sha L, Zhu S. A multi-sample particle swarm optimization algorithm based on electric field force. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:7464-7489. [PMID: 34814258 DOI: 10.3934/mbe.2021369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Aiming at the premature convergence problem of particle swarm optimization algorithm, a multi-sample particle swarm optimization (MSPSO) algorithm based on electric field force is proposed. Firstly, we introduce the concept of the electric field into the particle swarm optimization algorithm. The particles are affected by the electric field force, which makes the particles exhibit diverse behaviors. Secondly, MSPSO constructs multiple samples through two new strategies to guide particle learning. An electric field force-based comprehensive learning strategy (EFCLS) is proposed to build attractive samples and repulsive samples, thus improving search efficiency. To further enhance the convergence accuracy of the algorithm, a segment-based weighted learning strategy (SWLS) is employed to construct a global learning sample so that the particles learn more comprehensive information. In addition, the parameters of the model are adjusted adaptively to adapt to the population status in different periods. We have verified the effectiveness of these newly proposed strategies through experiments. Sixteen benchmark functions and eight well-known particle swarm optimization algorithm variants are employed to prove the superiority of MSPSO. The comparison results show that MSPSO has better performance in terms of accuracy, especially for high-dimensional spaces, while maintaining a faster convergence rate. Besides, a real-world problem also verified that MSPSO has practical application value.
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Affiliation(s)
- Shangbo Zhou
- College of Computer Science, Chongqing University, Chongqing 400044, China
- Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing 400030, China
| | - Yuxiao Han
- College of Computer Science, Chongqing University, Chongqing 400044, China
- Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing 400030, China
| | - Long Sha
- College of Computer Science, Chongqing University, Chongqing 400044, China
- Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing 400030, China
| | - Shufang Zhu
- College of Computer Science, Chongqing University, Chongqing 400044, China
- Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing 400030, China
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15
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Singh N, Kaur J. Hybridizing sine–cosine algorithm with harmony search strategy for optimization design problems. Soft comput 2021. [DOI: 10.1007/s00500-021-05841-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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