scholarly journals Research on Resource Allocation and Optimization of Community Intelligent Sports Service for the Elderly Based on Group Intelligence

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Qiaofeng Liu ◽  
Jinglun Huang ◽  
Bin Zhang ◽  
Jihong Zhao ◽  
Chengyun Zhang ◽  
...  

Objective. The mainstream development trend in the era of intelligent sports. At present, with the rapid development of science and technology, it is absolutely wise to combine group intelligence with community intelligent sports services for the elderly. Group intelligence has opened a new era of intelligent sports service. Group intelligence has become an important factor in the development and growth of community intelligent sports service for the elderly and has become a hot topic at present. However, intelligence has encountered difficulties on the road of development. At present, the aging of the population is getting worse and worse, and the elderly have higher and higher requirements for fitness and leisure services, which leads to the need for sports services to be continuously strengthened. The distribution of resources is uneven, the data is not clear enough, and the swarm intelligence algorithm is not perfect. With the adaptation of the elderly to intelligence, more intelligent, concise, and personalized services need to be developed. The most important method is to optimize the swarm intelligence algorithm continuously. In this paper, PSO algorithm is optimized and HCSSPSO algorithm is proposed. HCSSPSO algorithm is a combination of PSO algorithm and clonal selection strategy, and test simulation experiments, PSO algorithm, CLPSO algorithm, and HCSSPSO algorithm for comparison. From the experimental results, HCSSPSO algorithm has better convergence speed and stability, whether it is data or comparison graph. The data optimized by HCSSPSO algorithm is higher than the original data and the other two algorithms in terms of satisfaction and resource allocation.

Author(s):  
I. I. Aina ◽  
C. N. Ejieji

In this paper, a new metaheuristic algorithm named refined heuristic intelligence swarm (RHIS) algorithm is developed from an existing particle swarm optimization (PSO) algorithm by introducing a disturbing term to the velocity of PSO and modifying the inertia weight, in which the comparison between the two algorithms is also addressed.


2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Dawei Zhang ◽  
Daling Wang

With the passage of time and recent advances in science and information technology, the development in the area of course selection based on some defined criteria has made the choice of the mechanism easy and effective. In this paper, the approach is based on an innovative perspective, and swarm intelligence was introduced for the intentional choice mechanism of course selection. The study has considered the course selection in English as an example. Swarm intelligence algorithm and integrative course selection were combined with the recommendation algorithm and the intent of course selection in English course to discuss the relevant decision mechanism. Firstly, the comprehensive selection intentional recommendation algorithm and PSO algorithm were introduced, and the algorithm was initialized. Secondly, the operation process was described in detail, and the application process was analyzed. Then, it was introduced into the English course elective process. Finally, the experimental results of the study came with the conclusion through the test that the PSO algorithm has a higher degree of accuracy and can better judge individual behaviors, which contributes to the establishment of choice-choice mechanism. The effectiveness of the study was demonstrated through experiments.


2014 ◽  
Vol 951 ◽  
pp. 239-244 ◽  
Author(s):  
Xiao Qiang Xu ◽  
De Ming Lei

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.


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