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Genetic algorithm with maximum-minimum crossover (GA-MMC) applied in optimization of radiation pattern control of
Leonardo W T Silva1, Vitor F Barros2, Sandro G Silva3
1Launching Center of Barreira do Inferno, Brazilian Air Force, RN-063 59140-970, Parnamirim RN, Brazil. lwts@ig.com.br.
A new Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) optimizes phased array radars (PARs) for improved trajectory tracking. This method enhances genetic diversity, achieving over 90% success and boosting population fitness by 20%.
Area of Science:
- Aerospace Engineering
- Electrical Engineering
- Computer Science
Background:
- Rocket Tracking Systems (RTS) utilize radar sensors for trajectory data processing.
- Upgrading radar antennas from parabolic reflectors (PRs) to phased arrays (PAs) enhances functionality and maintenance.
- Phased arrays allow electronic control of radiation patterns by adjusting element signal excitation.
Purpose of the Study:
- To develop an optimization method for modeling the complex excitation signal combinations in phased array radars (PARs).
- To introduce a novel Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) for controlling PA radiation patterns.
- To enhance the efficiency and effectiveness of PAR operation through advanced optimization techniques.
Main Methods:
- Development of the Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC).
- Implementation of a reconfigurable algorithm with multiple objectives and differentiated coding.
- Introduction of a novel crossover genetic operator pairing fittest with least fit individuals to increase genetic diversity.
Main Results:
- The GA-MMC method demonstrated success in over 90% of test applications.
- Achieved a more than 20% increase in the fitness of the final population.
- Successfully reduced the issue of premature convergence in the optimization process.
Conclusions:
- The GA-MMC is an effective method for optimizing phased array radar radiation patterns.
- This approach significantly improves optimization outcomes compared to conventional methods.
- The developed algorithm enhances RTS functionality for improved trajectory tracking.
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