Multiobjective cooperative swarm intelligence algorithm for uplink resource allocation in LTE‐A networks

2019 ◽  
Vol 30 (12) ◽  
Author(s):  
M. Leeban Moses ◽  
B. Kaarthick
Keyword(s):  
Resource Allocation ◽  
Swarm Intelligence ◽  
Intelligence Algorithm ◽  
Swarm Intelligence Algorithm

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 ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Swarm Intelligence ◽  
Clonal Selection ◽  
Rapid Development ◽  
The Elderly ◽  
Pso Algorithm ◽  
Selection Strategy ◽  
Intelligence Algorithm ◽  
On The Road ◽  
Swarm Intelligence Algorithm

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.

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
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