scholarly journals Exercise Behavior Prediction and Injury Assessment Based on Swarm Intelligence Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Ding Ding ◽  
Jianqiong Jiang ◽  
Changya Liu
Keyword(s):  
Swarm Intelligence ◽  
High Stability ◽  
Simulation Experiment ◽  
Science And Technology ◽  
Exercise Behavior ◽  
Behavior Prediction ◽  
The Past ◽  
Intelligence Algorithm ◽  
Injury Assessment ◽  
Swarm Intelligence Algorithm

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.

Research on Swarm Intelligence Algorithm with an Artificial Bee Colony Algorithm for Lot Streaming Problem in Job Shop

2014 ◽  
Vol 951 ◽  
pp. 239-244 ◽  
Author(s):  
Xiao Qiang Xu ◽  
De Ming Lei
Keyword(s):  
Swarm Intelligence ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Job Shop ◽  
Lot Streaming ◽  
Lot Scheduling ◽  
Bee Colony ◽  
Intelligence Algorithm ◽  
Second Period ◽  
Swarm Intelligence Algorithm

The lot streaming (LS) problem in job shop with equal-size sub-lots and intermittent idling is considered. An effective swarm intelligence algorithm with an artificial bee colony (ABC) algorithm is proposed for the minimization of total penalties of tardiness and earliness. In the first period of ABC, the employed bee phase and the onlooker bee phase are both for lot/sub-lot scheduling. In the second period, the LS conditions are determined in the employed bee phase and the lot/sub-lot is scheduled in the onlooker phase. The worst solution of the swarm is replaced with the elite one every few cycles. Computational results show the promising advantage of ABC.

scholarly journals Meerkat Clan Algorithm: A New Swarm Intelligence Algorithm

2018 ◽  
Vol 10 (1) ◽  
pp. 354
Author(s):  
Ahmed T. Sadiq Al-Obaidi ◽  
Hasanen S. Abdullah ◽  
Zied O. Ahmed
Keyword(s):  
Swarm Intelligence ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Traveling Salesman ◽  
Organizational Skills ◽  
Suricata Suricatta ◽  
Kalahari Desert ◽  
Intelligence Algorithm ◽  
Swarm Intelligence Algorithm

<p>Evolutionary computation and swarm intelligence meta-heuristics are exceptional instances that environment has been a never-ending source of creativeness. The behavior of bees, bacteria, glow-worms, fireflies and other beings have stirred swarm intelligence scholars to create innovative optimization algorithms. This paper proposes the Meerkat Clan Algorithm (MCA) that is a novel swarm intelligence algorithm resulting from watchful observation of the Meerkat (Suricata suricatta) in the Kalahari Desert in southern Africa. This animal shows an exceptional intelligence, tactical organizational skills, and remarkable directional cleverness in its traversal of the desert when searching for food. A Meerkat Clan Algorithm (MCA) proposed to solve the optimization problems through reach the optimal solution by efficient way comparing with another swarm intelligence. Traveling Salesman Problem uses as a case study to measure the capacity of the proposed algorithm through comparing its results with another swarm intelligence. MCA shows its capacity to solve the Traveling Salesman’s Problem. Its dived the solutions group to sub-group depend of meerkat behavior that gives a good diversity to reach an optimal solution. Paralleled with the current algorithms for resolving TSP by swarm intelligence, it has been displayed that the size of the resolved problems could be enlarged by adopting the algorithm proposed here.</p>

Protecting ECG Signals with Hybrid Swarm Intelligence Algorithm

2021 ◽  
pp. 47-60
Author(s):  
Ayushi Kirar ◽  
Siddharth Bhalerao ◽  
Om Prakash Verma ◽  
Irshad Ahmad Ansari
Keyword(s):  
Swarm Intelligence ◽  
Hybrid Swarm ◽  
Ecg Signals ◽  
Intelligence Algorithm ◽  
Swarm Intelligence Algorithm

Soccer Game Optimization

2015 ◽  
pp. 1434-1469 ◽  
Author(s):  
Hindriyanto Dwi Purnomo ◽  
Hui-Ming Wee
Keyword(s):  
Evolutionary Algorithm ◽  
Information Sharing ◽  
Swarm Intelligence ◽  
Soccer Player ◽  
Optimization Problems ◽  
Optimization Method ◽  
New Method ◽  
Intelligence Algorithm ◽  
Reproduction Process ◽  
Swarm Intelligence Algorithm

A new metaheuristic algorithm is proposed. The algorithm integrates the information sharing as well as the evolution operators in the swarm intelligence algorithm and evolutionary algorithm respectively. The basic soccer player movement is used as the analogy to describe the algorithm. The new method has two basic operators; the move off and the move forward. The proposed method elaborates the reproduction process in evolutionary algorithm with the powerful information sharing in the swarm intelligence algorithm. Examples of implementations are provided for continuous and discrete problems. The experiment results reveal that the proposed method has the potential to become a powerful optimization method. As a new method, the proposed algorithm can be enhanced in many different ways such as investigating the parameter setting, elaborating more aspects of the soccer player movement as well as implementing the proposed method to solve various optimization problems.

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