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In the context of rapid urbanization and the substantial increase in logistics demand, the deployment of Unmanned Aerial Vehicle (UAV) delivery systems has emerged as a pivotal technology for augmenting delivery efficiency and enhancing customer satisfaction. This study tackles the intricate challenge of task allocation among multiple logistics UAVs within urban delivery scenarios, striving to simultaneously minimize transportation costs and maximize customer satisfaction by optimizing the task allocation mechanism. To-wards this objective, we have conceptualized a multi-logistics UAV task allocation model and engineered an avant-garde Improved Genetic Algorithm (IGA) to address this model effectively. The algorithm amalgamates a multi-round roulette selection strategy with a tournament selection mechanism, markedly augmenting the likelihood of selecting superior individuals. Through the amalgamation of sequential and two-point crossover techniques, alongside refined mutation probabilities adjustments, it considerably bolsters search accuracy and expedites the algorithm's convergence rate. Furthermore, the algorithm employs a variegated suite of crossover and mutation strategies to foster population diversity and ensure the preservation of elite individuals, concurrently facilitating the advancement of less fit individuals. Empirical simulation outcomes attest that the enhanced genetic algorithm secures approximately a 50% enhancement in the average fitness function value, adeptly engineering task allocation solutions that are cost-effective and yield higher customer satisfaction, thereby manifesting a pronounced competitive edge over conventional genetic algorithms. This inquiry not only unveils novel avenues for the refinement of UAV logistics delivery systems but also furnishes substantial theoretical underpinnings and pragmatic insights for the intellectualization of future urban community logistics distribution networks.
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