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Qiao J, Wang G, Yang Z, Luo X, Chen J, Li K, Liu P. A hybrid particle swarm optimization algorithm for solving engineering problem. Sci Rep 2024; 14:8357. [PMID: 38594511 DOI: 10.1038/s41598-024-59034-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 04/05/2024] [Indexed: 04/11/2024] Open
Abstract
To overcome the disadvantages of premature convergence and easy trapping into local optimum solutions, this paper proposes an improved particle swarm optimization algorithm (named NDWPSO algorithm) based on multiple hybrid strategies. Firstly, the elite opposition-based learning method is utilized to initialize the particle position matrix. Secondly, the dynamic inertial weight parameters are given to improve the global search speed in the early iterative phase. Thirdly, a new local optimal jump-out strategy is proposed to overcome the "premature" problem. Finally, the algorithm applies the spiral shrinkage search strategy from the whale optimization algorithm (WOA) and the Differential Evolution (DE) mutation strategy in the later iteration to accelerate the convergence speed. The NDWPSO is further compared with other 8 well-known nature-inspired algorithms (3 PSO variants and 5 other intelligent algorithms) on 23 benchmark test functions and three practical engineering problems. Simulation results prove that the NDWPSO algorithm obtains better results for all 49 sets of data than the other 3 PSO variants. Compared with 5 other intelligent algorithms, the NDWPSO obtains 69.2%, 84.6%, and 84.6% of the best results for the benchmark function (f 1 - f 13 ) with 3 kinds of dimensional spaces (Dim = 30,50,100) and 80% of the best optimal solutions for 10 fixed-multimodal benchmark functions. Also, the best design solutions are obtained by NDWPSO for all 3 classical practical engineering problems.
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Affiliation(s)
- Jinwei Qiao
- School of Mechanical and Automotive Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, China
- Shandong Institute of Mechanical Design and Research, Jinan, 250353, China
| | - Guangyuan Wang
- School of Mechanical and Automotive Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, China
- Shandong Institute of Mechanical Design and Research, Jinan, 250353, China
| | - Zhi Yang
- School of Mechanical and Automotive Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, China.
- Shandong Institute of Mechanical Design and Research, Jinan, 250353, China.
| | - Xiaochuan Luo
- School of Information Science and Engineering, Northeastern University, Shenyang, 110819, China
| | - Jun Chen
- School of Mechanical and Automotive Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, China
- Shandong Institute of Mechanical Design and Research, Jinan, 250353, China
| | - Kan Li
- Fushun Supervision Inspection Institute for Special Equipment, Fushun, 113000, China
| | - Pengbo Liu
- School of Mechanical and Automotive Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, China
- Shandong Institute of Mechanical Design and Research, Jinan, 250353, China
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Liu D, Wang C, Ji Y, Fu Q, Li M, Ali S, Li T, Cui S. Measurement and analysis of regional flood disaster resilience based on a support vector regression model refined by the selfish herd optimizer with elite opposition-based learning. J Environ Manage 2021; 300:113764. [PMID: 34547576 DOI: 10.1016/j.jenvman.2021.113764] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Revised: 08/09/2021] [Accepted: 09/14/2021] [Indexed: 06/13/2023]
Abstract
Flood disasters are sudden, frequent, uncertain and highly hazardous natural disasters. The precise identification of the spatiotemporal evolution characteristics, key driving factors and influencing mechanisms of resilience has become a hot spot in disaster risk reduction research. Therefore, the cumulative information contribution rate-Pearson correlation coefficient (CICR- PCC) model is used in this paper to construct a flood disaster resilience index system by quantitative methods, and a support vector regression model refined by the selfish herd optimizer with elite opposition-based learning (EO-SHO-SVR) is built to improve the accuracy of flood disaster resilience evaluation. On this basis, the EO-SHO-SVR model is used to analyze the spatiotemporal evolution of flood disaster resilience in the Jiansanjiang branch of China Beidahuang Agricultural Reclamation Group Co., Ltd. over the past 22 years. In addition, to verify the comprehensive performance of the EO-SHO-SVR model, support vector regression (SVR), imperial competition algorithm-improved support vector regression (ICA-SVR), and unimproved selfish herd optimizer support vector regression (SHO-SVR) models were selected for comparative analysis. The results show that during the study period, the resilience levels reached a plateau of high levels from 1997 to 2018 after experiencing a state of steady low levels followed by increased volatility. Among the investigated factors, land-average flood prevention investment, GDP per capita, agricultural machinery power per unit of arable land, water conservancy project investment as a percentage of GDP, and rainfall are the main driving factors that cause spatiotemporal differences in flood disaster resilience in the study area. Spatially, the resilience levels in the Jiansanjiang branch are ordered as northern farms > southern farms > central farms, and the comprehensive index of resilience shows an increasing trend from west to east. In the model comparison, the EO-SHO-SVR model has outstanding advantages in fitting performance, reliability, rationality and stability, which fully demonstrates that the EO-SHO-SVR model is highly advanced and practical in the measurement of flood disaster resilience. These research results can provide a more accurate evaluation model of regional flood disaster resilience. In addition, they can also provide valuable information for regional flood resilience improvement and flood risk avoidance.
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Affiliation(s)
- Dong Liu
- School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin, Heilongjiang, 150030, China; Key Laboratory of Effective Utilization of Agricultural Water Resources of Ministry of Agriculture, Northeast Agricultural University, Harbin, Heilongjiang, 150030, China; Heilongjiang Provincial Key Laboratory of Water Resources and Water Conservancy Engineering in Cold Region, Northeast Agricultural University, Harbin, Heilongjiang, 150030, China; Key Laboratory of Water-Saving Agriculture of Ordinary University in Heilongjiang Province, Northeast Agricultural University, Harbin, Heilongjiang, 150030, China.
| | - Chunqing Wang
- School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin, Heilongjiang, 150030, China.
| | - Yi Ji
- School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin, Heilongjiang, 150030, China.
| | - Qiang Fu
- School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin, Heilongjiang, 150030, China.
| | - Mo Li
- School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin, Heilongjiang, 150030, China.
| | - Shoaib Ali
- School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin, Heilongjiang, 150030, China.
| | - Tianxiao Li
- School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin, Heilongjiang, 150030, China.
| | - Song Cui
- School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin, Heilongjiang, 150030, China.
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