7 results
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2. Exercise Behavior Prediction and Injury Assessment Based on Swarm Intelligence Algorithm.
- Author
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Ding, Ding, Jiang, Jianqiong, and Liu, Changya
- Subjects
- *
SWARM intelligence , *ALGORITHMS , *PROBLEM solving , *SPORTS forecasting , *SPORTS injuries , *BEHAVIORAL assessment - Abstract
The biggest view of the whole world on science and technology and sports is that science and technology and sports both represent national strength. At present, the integration of sports and science and technology has not reached a certain height, especially in the prediction of sports behavior and injury assessment, and the investment in science and technology is still lacking. This leads to a high number of injuries caused by sports every year. However, swarm intelligence algorithm has made few breakthrough achievements in the past few years, and the combination of sports behavior and swarm intelligence algorithm can just solve this problem. It is very important to choose the algorithm for predicting and assessing sports behavior. We should choose an efficient algorithm with high stability, high convergence speed, and optimization ability. In this paper, the IPSGWO algorithm is proposed to realize this application. IPSGWO algorithm is based on the GWO algorithm, with appropriate strategies and ideas, to maximize the improvement. In this paper, the convergence curve of PSO, GWO, and IPSGWO is tested to determine whether the IPSGWO algorithm has more stable and higher performance, and the simulation experiment is used to determine whether the IPSGWO algorithm is suitable for prediction and injury assessment compared with the other two. From the experimental results, the IPSGWO algorithm does have higher performance; because of this, it is more accurate for prediction and injury assessment. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
3. An Enhanced Slime Mould Algorithm and Its Application for Digital IIR Filter Design.
- Author
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Liang, Xiaodan, Wu, Dong, Liu, Yang, He, Maowei, and Sun, Liling
- Subjects
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MYXOMYCETES , *SWARM intelligence , *METAHEURISTIC algorithms , *PROBLEM solving , *FORAGING behavior , *ALGORITHMS - Abstract
In the past few decades, metaheuristic algorithms (MA) have been developed tremendously and have been successfully applied in many fields. In recent years, a large number of new MA have been proposed. Slime mould algorithm (SMA) is a novel swarm-based intelligence optimization algorithm. SMA solves the optimization problem by imitating the foraging and movement behavior of slime mould. It can effectively obtain a promising global optimal solution. However, it still suffers some shortcomings such as the unstable convergence speed, the imprecise search accuracy, and incapability of identifying a local optimal solution when faced with complicated optimization problems. With the purpose of overcoming the shortcomings of SMA, this paper proposed a multistrategy enhanced version of SMA called ESMA. The three enhanced strategies are chaotic initialization strategy (CIS), orthogonal learning strategy (OLS), and boundary reset strategy (BRS). The CIS is used to generate an initial population with diversity in the early stage of ESMA, which can increase the convergence speed of the algorithm and the quality of the final solution. Then, the OLS is used to discover the useful information of the best solutions and offer a potential search direction, which enhances the local search ability and raises the convergence rate. Finally, the BRS is used to correct individual positions, which ensures the population diversity and enhances the overall search capabilities of ESMA. The performance of ESMA was validated on the 30 IEEE CEC2014 functions and three IIR model identification problems, compared with other nine well-regarded and state-of-the-art algorithms. Simulation results and analysis prove that the ESMA has a superior performance. The three strategies involved in ESMA have significantly improved the performance of the basic SMA. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
4. A New Discrete Grid-Based Bacterial Foraging Optimizer to Solve Complex Influence Maximization of Social Networks.
- Author
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Zhang, Yichuan, Yong, Yibo, Yang, Shujun, and Zhang, Tian
- Subjects
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SOCIAL influence , *SOCIAL networks , *PROBLEM solving , *ALGORITHMS , *MATHEMATICAL optimization - Abstract
Influence maximization (IM) is fundamental to social network applications. It aims to find multiple seed nodes with an enormous impact cascade to maximize these nodes' spread of influence in social networks. Traditional methods for solving influence maximization of the social network, such as the distance method, greedy method, and PageRank method, may suffer from issues of low calculation accuracy and high computational cost. In this paper, we propose a new bacterial foraging optimization algorithm to solve the IM problem based on the complete-three-layer-influence (CTLI) evaluation model. In this algorithm, a novel grid-based reproduction strategy and a direction-adjustment-based chemotaxis strategy are devised to enhance the algorithm's searchability. Finally, we conduct comprehensive experiments on four social network cases to verify the effectiveness of the proposed algorithm. The experimental results show that our proposed algorithm effectively solves the social network's influence maximization. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
5. Cooperative Coevolution with Two-Stage Decomposition for Large-Scale Global Optimization Problems.
- Author
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Yue, H. D. and Sun, Y.
- Subjects
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GLOBAL optimization , *DECOMPOSITION method , *COEVOLUTION , *PROBLEM solving , *ALGORITHMS - Abstract
Cooperative coevolution (CC) is an effective framework for solving large-scale global optimization (LSGO) problems. However, CC with static decomposition method is ineffective for fully nonseparable problems, and CC with dynamic decomposition method to decompose problems is computationally costly. Therefore, a two-stage decomposition (TSD) method is proposed in this paper to decompose LSGO problems using as few computational resources as possible. In the first stage, to decompose problems using low computational resources, a hybrid-pool differential grouping (HPDG) method is proposed, which contains a hybrid-pool-based detection structure (HPDS) and a unit vector-based perturbation (UVP) strategy. In the second stage, to decompose the fully nonseparable problems, a known information-based dynamic decomposition (KIDD) method is proposed. Analytical methods are used to demonstrate that HPDG has lower decomposition complexity compared to state-of-the-art static decomposition methods. Experiments show that CC with TSD is a competitive algorithm for solving LSGO problems. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
6. A New Discrete Grid-Based Bacterial Foraging Optimizer to Solve Complex Influence Maximization of Social Networks.
- Author
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Zhang, Yichuan, Yong, Yibo, Yang, Shujun, and Zhang, Tian
- Subjects
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SOCIAL influence , *SOCIAL networks , *PROBLEM solving , *ALGORITHMS , *MATHEMATICAL optimization - Abstract
Influence maximization (IM) is fundamental to social network applications. It aims to find multiple seed nodes with an enormous impact cascade to maximize these nodes' spread of influence in social networks. Traditional methods for solving influence maximization of the social network, such as the distance method, greedy method, and PageRank method, may suffer from issues of low calculation accuracy and high computational cost. In this paper, we propose a new bacterial foraging optimization algorithm to solve the IM problem based on the complete-three-layer-influence (CTLI) evaluation model. In this algorithm, a novel grid-based reproduction strategy and a direction-adjustment-based chemotaxis strategy are devised to enhance the algorithm's searchability. Finally, we conduct comprehensive experiments on four social network cases to verify the effectiveness of the proposed algorithm. The experimental results show that our proposed algorithm effectively solves the social network's influence maximization. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
7. A Novel Dynamic Algorithm for IT Outsourcing Risk Assessment Based on Transaction Cost Theory.
- Author
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Cong, Guodong and Chen, Tinggui
- Subjects
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INFORMATION technology outsourcing , *TRANSACTION costs , *RISK assessment , *ALGORITHMS , *PROBABILITY theory , *PROBLEM solving - Abstract
With the great risk exposed in IT outsourcing, how to assess IT outsourcing risk becomes a critical issue. However, most of approaches to date need to further adapt to the particular complexity of IT outsourcing risk for either falling short in subjective bias, inaccuracy, or efficiency. This paper proposes a dynamic algorithm of risk assessment. It initially forwards extended three layers (risk factors, risks, and risk consequences) of transferring mechanism based on transaction cost theory (TCT) as the framework of risk analysis, which bridges the interconnection of components in three layers with preset transferring probability and impact. Then, it establishes an equation group between risk factors and risk consequences, which assures the “attribution” more precisely to track the specific sources that lead to certain loss. Namely, in each phase of the outsourcing lifecycle, both the likelihood and the loss of each risk factor and those of each risk are acquired through solving equation group with real data of risk consequences collected. In this “reverse” way, risk assessment becomes a responsive and interactive process with real data instead of subjective estimation, which improves the accuracy and alleviates bias in risk assessment. The numerical case proves the effectiveness of the algorithm compared with the approach forwarded by other references. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
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