Evolutionary optimization algorithms and their applications in wireless systems
Nowdays Evolutionary Optimization has recently experienced a remarkable growth. This report convers PSO algorithms and BAT algorithms. Both of them are started with a population which generated randomly and evaluate the population by using the fitness values. However PSO simulates the behaviors of b...
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sg-ntu-dr.10356-657522023-07-07T17:23:00Z Evolutionary optimization algorithms and their applications in wireless systems Lu, Shijie Lu Yilong School of Electrical and Electronic Engineering Centre for Advanced Information Systems DRNTU::Engineering::Electrical and electronic engineering::Wireless communication systems Nowdays Evolutionary Optimization has recently experienced a remarkable growth. This report convers PSO algorithms and BAT algorithms. Both of them are started with a population which generated randomly and evaluate the population by using the fitness values. However PSO simulates the behaviors of bird flocking, BAT simulates the echolocation of microbats. And using MATLAB software implements the program to compare the two algorithms in Beamforming application. From the result, we can conclude that BAT algorithm is better than PSO algorithm, as BAT algorithm is more efficient and fast. Furthermore, some further improvements, suggestions and recommendation for the similar projects carried on in the future. Bachelor of Engineering 2015-12-11T06:02:57Z 2015-12-11T06:02:57Z 2015 2015 Final Year Project (FYP) http://hdl.handle.net/10356/65752 en Nanyang Technological University 45 p. application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering::Wireless communication systems |
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DRNTU::Engineering::Electrical and electronic engineering::Wireless communication systems Lu, Shijie Evolutionary optimization algorithms and their applications in wireless systems |
| description |
Nowdays Evolutionary Optimization has recently experienced a remarkable growth. This report convers PSO algorithms and BAT algorithms. Both of them are started with a population which generated randomly and evaluate the population by using the fitness values. However PSO simulates the behaviors of bird flocking, BAT simulates the echolocation of microbats. And using MATLAB software implements the program to compare the two algorithms in Beamforming application. From the result, we can conclude that BAT algorithm is better than PSO algorithm, as BAT algorithm is more efficient and fast. Furthermore, some further improvements, suggestions and recommendation for the similar projects carried on in the future. |
| author2 |
Lu Yilong |
| author_facet |
Lu Yilong Lu, Shijie |
| format |
Final Year Project |
| author |
Lu, Shijie |
| author_sort |
Lu, Shijie |
| title |
Evolutionary optimization algorithms and their applications in wireless systems |
| title_short |
Evolutionary optimization algorithms and their applications in wireless systems |
| title_full |
Evolutionary optimization algorithms and their applications in wireless systems |
| title_fullStr |
Evolutionary optimization algorithms and their applications in wireless systems |
| title_full_unstemmed |
Evolutionary optimization algorithms and their applications in wireless systems |
| title_sort |
evolutionary optimization algorithms and their applications in wireless systems |
| publishDate |
2015 |
| url |
http://hdl.handle.net/10356/65752 |
| _version_ |
1772827120933797888 |