Opposition-based learning (OBL) has been widely used to improve many swarm intelligent optimization (SI) algorithms for continuous problems during the past few decades. When the SI optimization algorithms apply OBL to solve discrete problems, the construction and utilization of the opposite solution is the key issue. Ant colony optimization (ACO) generally used to solve combinatorial optimization problems is a kind of classical SI optimization algorithm. Opposition-based ACO which is combined in OBL is proposed to solve the symmetric traveling salesman problem (TSP) in this paper. Two strategies for constructing opposite path by OBL based on solution characteristics of TSP are also proposed. Then, in order to use information of opposite path to improve the performance of ACO, three different strategies, direction, indirection, and random methods, mentioned for pheromone update rules are discussed individually. According to the construction of the inverse solution and the way of using it in pheromone updating, three kinds of improved ant colony algorithms are proposed. To verify the feasibility and effectiveness of strategies, two kinds of ACO algorithms are employed to solve TSP instances. The results demonstrate that the performance of opposition-based ACO is better than that of ACO without OBL.

Automatic modulation classification in the manner of pattern recognition has caught massive attention due to its simplicity in workflow design. Recently, enormous interest has been aroused because of the boosting development of deep learning and numerous network structures, leading to various substantial contributions. However, to the best of our knowledge, current approaches fail to handle two essential concerns simultaneously: the statistical distribution of the noise and the computational complexity due to the network structure. Since these factors play critical roles when facing practical applications, a modified constellation concept based on the Score function of Cauchy distribution is proposed as a robust feature to impulsive noise. Besides, a lightweight structure based on Shuffle Unit and Gated Recurrent Unit is proposed as the recognizer to lower the potential risk of overfitting and cope with the time-consuming problem. Monte-Carlo experiments involving multiple comparison algorithms are executed under different conditions, and results verify the superior performance of the proposed AMC scheme.

A decision tree structure is proposed to handle automatic modulation classification under mixed noise and fading channels. In the first layer of the tree, IQ signals are categorized into three groups according to the error entropy. In the second layer, IQ signals preprocessed by Cauchy-Score functions are fed as patterns into the neural network where global and local features are abstracted by Transformer and CNN parallelly and concatenated to make a final decision. Monte-Carlo experiments demonstrate the superiority of the proposed method, strategy and network under mixed noise and fading channels.