Array antenna fault detection method based on DE-GA algorithm
To improve the accuracy of fault detection in array antenna,an enhanced differential evolution-genetic algorithm(DE-GA)is proposed.This algorithm combines the advantages of genetic algorithm(GA)and differential evolution(DE)by employing a dual crossover strategy to help individuals escape local optima.An adaptive weighting mechanism further optimizes offspring selection,enhancing the algorithm's sensitivity and adaptability to fault conditions.Applied to array antenna fault detection,the DE-GA algorithm models the array and optimizes its radiation pattern to match the known faulty pattern,allowing the faulty array's amplitude to be estimated.Experiments show that compared with DE and GA,DE-GA reduces the fitness function value by 11.15%and 12.90%,the mean absolute error by 19.36%and 23.85%,the mean square error by 12.90%and 11.15%,and the maximum error by 12.30%and 13.18%.This demonstrates higher accuracy and improved approximation capabilities.Additionally,the algorithm maintains excellent stability with larger arrays,making it suitable for large-scale fault detection.
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