Artificial Immune Algorithm-based Airplane Path Planning under Complicated Environment

This paper mainly studies the flight route planning in complex and uncertain environments. The improved artificial immune algorithm is used to complete the flight route planning in different complex environments. The experimental results show that the improved artificial immune algorithm has a higher success rate of route planning than the common genetic algorithm in complex environments; therefore, it also shows that this method has better adaptability in complex environment

A new approach experimental to diagnosis of the failures in mechanical structures using the artificial immune algorithm with negative selection / Uma nova abordagem experimental para o diagnóstico das falhas nas estruturas mecânicas utilizando o algoritmo de imunidade artificial com seleção negativa

In this paper we present a new experimental approach to diagnose failures in mechanical structures using as decision tool an artificial immune algorithm with negative selection. This method is divided into two modules, and the acquisition and data processing module and analysis, detecting and characterizing flaws module. The module for data acquisition and processing of the experimental apparatus is constituted as sensors and actuators, so as to capture the signals in the structure and store it in the computer. From the signal acquisition executed if the negative selection algorithm to identify and characterize flaws in the structure. The main application of this methodology is to assist in the inspection process of mechanical structures in order to identify and characterize the flaws, as well as perform the decisions in order to avoid accidents. To evaluate the proposed methodology, experiments were performed in the laboratory where a real signs database was captured in a structure of the beam type, made of aluminum. The results obtained in the tests show robustness and efficiency when compared to literature.