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...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| التنسيق: | Final Year Project |
| اللغة: | English |
| منشور في: |
2015
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/10356/65752 |
| الوسوم: |
إضافة وسم
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| الملخص: | 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. |
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