scholarly journals Scientific Impact of Sports on Human Health and Physique Based on Optimization Big Data Ant Colony Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Lin Wu
Keyword(s):  
Big Data ◽  
Physical Fitness ◽  
Human Health ◽  
Ant Colony Algorithm ◽  
Impact Analysis ◽  
Ant Colony ◽  
Classification Rule ◽  
Scientific Impact ◽  
Fitness Level ◽  
The Impact

With the continuous improvement of living standards, people began to pay more and more attention to sports, and the impact of sports on human health and physique has been paid more and more attention. This study mainly analyzes the scientific impact of sports on human health and physique under the background of big data. Firstly, the big data analytic hierarchy process is used to construct the comprehensive evaluation structure system of sports on human health and physique. Then, an improved big data adaptive ant colony classification rule algorithm is proposed. Finally, the performance evaluation and physical impact analysis of the improved big data algorithm are carried out. The results show that compared with other algorithms, ACA ∗ (ant colony algorithm) based on big data has more obvious advantages in stability, optimization ability, running time, and convergence speed and is more suitable for practical application. In general, the improvement of the physical fitness level of the association members in 2019 mainly depends on the results of the improvement of the physical fitness level. In the future, we need to strengthen physical exercise, change living habits and traffic habits, and other methods to optimize the overall physical fitness.

Cloud computing demand elasticity algorithm based on ant colony algorithm

2020 ◽  
Vol 13 ◽  
Author(s):  
Chunyu Liu ◽  
Fengrui Mu ◽  
Weilong Zhang
Keyword(s):  
Ant Colony Algorithm ◽  
Theory Of Elasticity ◽  
Ant Colony ◽  
Demand Elasticity ◽  
Network Efficiency ◽  
Elasticity Of Demand ◽  
Operating Profit ◽  
Network Load ◽  
Network Utilization ◽  
The Impact

Background: In recent era of technology, the traditional Ant Colony Algorithm (ACO) is insufficient in solving the problem of network congestion and load balance, and network utilization. Methods: This paper proposes an improved ant colony algorithm, which considers the price factor based on the theory of elasticity of demand. The price factor is denominated in the impact on the network load which means indirect control of network load, congestion or auxiliary solution to calculate the idle resources caused by the low network utilization and reduced profits. Results: Experimental results show that the improved algorithm can balance the overall network load, extend the life of path by nearly 3 hours, greatly reduce the risk of network paralysis, and increase the profit of the manufacturer by 300 million Yuan. Conclusion: Furthermore, results shows that the improved method has a great application value in improving the network efficiency, balancing network load, prolonging network life and increasing network operating profit.

Classification-Rule Discovery with an Ant Colony Algorithm

2005 ◽  
pp. 420-424 ◽  
Author(s):  
Rafael S. Parpinelli ◽  
Heitor S. Lopes ◽  
Alex A. Freitas
Keyword(s):  
Computational Intelligence ◽  
Visual Information ◽  
Food Source ◽  
Ant Colony Algorithm ◽  
Chemical Substance ◽  
Ant Colony ◽  
Classification Rule ◽  
Natural Behaviour ◽  
Exchange Information ◽  
Pheromone Trail

Ant colony optimization (ACO) is a relatively new computational intelligence paradigm inspired by the behaviour of natural ants (Bonabeau, Dorigo & Theraulaz, 1999). The natural behaviour of ants that we are interested in is the following. Ants often find the shortest path between a food source and the nest of the colony without using visual information. In order to exchange information about which path should be followed, ants communicate with each other by means of a chemical substance called pheromone. As ants move, a certain amount of pheromone is dropped on the ground, creating a pheromone trail. The more ants follow a given trail, the more attractive that trail becomes to be followed by other ants. This process involves a loop of positive feedback, in which the probability that an ant chooses a path is proportional to the number of ants that have already passed by that path.

Effective ACPS-Based Rescheduling of Parallel Batch Processing Machines with MapReduce

2014 ◽  
Vol 575 ◽  
pp. 820-824
Author(s):  
Bin Zhang ◽  
Jia Jin Le ◽  
Mei Wang
Keyword(s):  
Big Data ◽  
Parallel Computing ◽  
Ant Colony Algorithm ◽  
Search Algorithm ◽  
Ant Colony ◽  
Search Problem ◽  
Parallel Search ◽  
Good Efficiency ◽  
Heuristic Scheduling ◽  
Low Efficiency

MapReduce is a highly efficient distributed and parallel computing framework, allowing users to readily manage large clusters in parallel computing. For Big data search problem in the distributed computing environment based on MapReduce architecture, in this paper we propose an Ant colony parallel search algorithm (ACPSMR) for Big data. It take advantage of the group intelligence of ant colony algorithm for global parallel search heuristic scheduling capabilities to solve problem of multi-task parallel batch scheduling with low efficiency in the MapReduce. And we extended HDFS design in MapReduce architecture, which make it to achieve effective integration with MapReduce. Then the algorithm can make the best of the scalability, high parallelism of MapReduce. The simulation experiment result shows that, the new algorithm can take advantages of cloud computing to get good efficiency when mining Big data.

Parallel Ant Colony Optimization Algorithm Based on Multiplicate Pheromon Declining

2012 ◽  
Vol 433-440 ◽  
pp. 3577-3583
Author(s):  
Yan Zhang ◽  
Hao Wang ◽  
Yong Hua Zhang ◽  
Yun Chen ◽  
Xu Li
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Computing Power ◽  
Slow Convergence ◽  
The Impact

To overcome the defect of the classical ant colony algorithm’s slow convergence speed, and its vulnerability to local optimization, the authors propose Parallel Ant Colony Optimization Algorithm Based on Multiplicate Pheromon Declining to solve Traveling Salesman Problem according to the characteristics of natural ant colony multi-group and pheromone updating features of ant colony algorithm, combined with OpenMP parallel programming idea. The new algorithm combines three different pheromone updating methods to make a new declining pheromone updating method. It effectively reduces the impact of pheromone on the non-optimal path in the ants parade loop to subsequent ants and improves the parade quality of subsequent ants. It makes full use of multi-core CPU's computing power and improves the efficiency significantly. The new algorithm is compared with ACO through experiments. The results show that the new algorithm has faster convergence rate and better ability of global optimization than ACO.

scholarly journals Improving the Interpretability of Classification Rules Discovered by an Ant Colony Algorithm: Extended Results

2016 ◽  
Vol 24 (3) ◽  
pp. 385-409 ◽  
Author(s):  
Fernando E. B. Otero ◽  
Alex A. Freitas
Keyword(s):  
Ant Colony Algorithm ◽  
Predictive Accuracy ◽  
Ant Colony ◽  
Support Vector ◽  
Classification Rules ◽  
Class Prediction ◽  
Vector Machines ◽  
Size Measure ◽  
The Impact

Most ant colony optimization (ACO) algorithms for inducing classification rules use a ACO-based procedure to create a rule in a one-at-a-time fashion. An improved search strategy has been proposed in the cAnt-Miner[Formula: see text] algorithm, where an ACO-based procedure is used to create a complete list of rules (ordered rules), i.e., the ACO search is guided by the quality of a list of rules instead of an individual rule. In this paper we propose an extension of the cAnt-Miner[Formula: see text] algorithm to discover a set of rules (unordered rules). The main motivations for this work are to improve the interpretation of individual rules by discovering a set of rules and to evaluate the impact on the predictive accuracy of the algorithm. We also propose a new measure to evaluate the interpretability of the discovered rules to mitigate the fact that the commonly used model size measure ignores how the rules are used to make a class prediction. Comparisons with state-of-the-art rule induction algorithms, support vector machines, and the cAnt-Miner[Formula: see text] producing ordered rules are also presented.

scholarly journals Influence of Healthy Physical Fitness on the Teaching of Aerobics Course Based on Big Data Mining Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Laipeng Xiao
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Physical Fitness ◽  
Physical Training ◽  
Body Shape ◽  
Teaching Experiment ◽  
The Body ◽  
Mining Technology ◽  
Big Data Mining ◽  
The Impact

Healthy physical fitness is one of the hot topics discussed by scholars at home and abroad in recent years, and it is a key indicator for evaluating students’ physical function and body shape. Aerobics, also known as bodybuilding, means that the body and health of students should have a better promotion effect, but in reality, many students found that after elective aerobics, body shape and health level basically did not improve, which is related to the setting of aerobics courses, especially the lack of physical training. Aerobics and other sports have common requirements in physical training, such as strength quality, speed quality, endurance quality, agility quality, and flexibility quality. This article is aimed at studying the impact of healthy physical fitness based on big data mining technology on the teaching of aerobics. On the basis of analyzing the process of data mining, the composition of healthy physical fitness, and the role of aerobics, it is used to test students in a certain university through experimental methods and statistical methods. Carry out aerobics teaching experiment, and compare and analyze the data measured by the experimental samples. The experimental results show that the use of healthy physical fitness in aerobics teaching can effectively promote the learning and improvement of aerobics skills.

scholarly journals Application of Rough Ant Colony Algorithm in Adolescent Psychology

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Tao Cong ◽  
Lin Jiang ◽  
Qihang Sun ◽  
Yang Li
Keyword(s):  
Big Data ◽  
Juvenile Delinquency ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Hot Spot ◽  
Rapid Development ◽  
Ant Colony ◽  
Good Effect ◽  
Adolescent Psychology ◽  
Combinatorial Optimization Problems

With the rapid development of big data, big data research in the security protection industry has been increasingly regarded as a hot spot. This article mainly aims at solving the problem of predicting the tendency of juvenile delinquency based on the experimental data of juvenile blindly following psychological crime. To solve this problem, this paper proposes a rough ant colony classification algorithm, referred to as RoughAC, which first uses the concept of upper and lower approximate sets in rough sets to determine the degree of membership. In addition, in the ant colony algorithm, we use the membership value to update the pheromone. Experiments show that the algorithm can not only solve the premature convergence problem caused by stagnation near the local optimal solution but also solve the continuous domain and combinatorial optimization problems and achieve better classification results. Moreover, the algorithm has a good effect on predicting classification and can provide guidance for predicting the tendency of juvenile delinquency.

scholarly journals Parallel Classification Algorithm Design of Human Resource Big Data Based on Spark Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wang Zhouhuo
Keyword(s):  
Big Data ◽  
Human Resources ◽  
Human Resource ◽  
Data Clustering ◽  
Ant Colony Algorithm ◽  
Algorithm Design ◽  
Large Data ◽  
Classification Algorithm ◽  
Classification Rule ◽  
Fuzzy Genetic

In order to solve the problem of large data classification of human resources, a new parallel classification algorithm of large data of human resources based on the Spark platform is proposed in this study. According to the spark platform, it can complete the update and distance calculation of the human resource big data clustering center and design the big data clustering process. Based on this, the K-means clustering method is introduced to mine frequent itemsets of large data and optimize the aggregation degree of similar large data. A fuzzy genetic algorithm is used to identify the balance of big data. This study adopts the selective integration method to study the unbalanced human resource database classifier in the process of transmission, introduces the decision contour matrix to construct the anomaly support model of the set of unbalanced human resource data classifier, identifies the features of the big data of human resource in parallel, repairs the relevance of the big data of human resource, introduces the improved ant colony algorithm, and finally realizes the design of the parallel classification algorithm of the big data of human resource. The experimental results show that the proposed algorithm has a low time cost, good classification effect, and ideal parallel classification rule complexity.

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