In recent years, intelligent design technology that is based on interactive evolutionary algorithms, namely interactive evolutionary design (IED) systems, has received extensive attention in the computer science, design, and other related literature. However, due to the complexity of design problems and the limitation of human cognitive ability, IED faces several challenges in actual design applications. With the aim to address these problems in the IED, this paper deconstructs the IED of the product styling from the perspective of the cognitive association of the users, and proposes a corresponding cognitive intervention method that is based on the association of information. We built databases of the perceptual evaluation results of typical cases and coded profiles of the typical cases, combined with the corresponding interaction process, to improve the efficiency of creating associations between dissimilar information in the early stages of evolution. Besides, in order to simplify the process of creating associations between similar information, this paper proposes a clustering model of similar information based on explicit and implicit distances. The proposed method is then applied to the evolutionary design of an SUV. The experimental results show that the proposed method reduces the initial and total evaluation time. Therefore, the proposed method improves users’ ability to understand the complex design tasks of IED for product styling, optimizing the interactive evaluation process by guiding designers to efficiently create the cognitive association of information, and increases the effectiveness of adopting IED to solve actual design problems about product styling.

Orthogonal frequency division multiplexing (OFDM) in 5G has many advantages; however, one of the disadvantages is that the superposition of a large number of subcarriers leads to a high peak-to-average power ratio (PAPR) of the transmit signal. A high PAPR results in high-power amplifier distortion and performance degradation. The partial transmit sequence (PTS) algorithm is commonly used for PAPR reduction. It enumerates all combinations of phase factors, weighs the signal using each phase factor combination, and finds the set of phase factors that minimizes the PAPR value of the OFDM signal. The advantage of the PTS is that it determines the optimal solution through enumeration; however, its major drawback is the higher complexity caused by the use of enumeration. Some studies have introduced the discrete particle swarm optimization (DPSO) algorithm instead of enumeration to determine the optimal solution of the PTS algorithm. As an excellent optimization method, the DPSO algorithm represents each individual as a solution during the optimization. Through iterative updates of the initial population, individuals in the population continuously move closer to the optimal solution. This approach significantly reduces complexity compared with the exhaustive enumeration used in the traditional PTS algorithm. However, the disadvantage of the general DPSO algorithm is that it can result in premature and early convergence, which leads to degradation of the PAPR reduction performance. In this study, we propose an improved method based on the general DPSO-based PTS algorithm, and the improved algorithm MDPSO-PTS adopts dynamic time-varying learning factors, which can find the optimal combination of phase factors more efficiently. The MDPSO-PTS algorithm expands the search space when seeking the optimal combination of phase factors. This avoids the drawback of premature convergence commonly observed in general DPSO-PTS algorithms, preventing early consideration of local optima as global optima. A comparative simulation of the improved MDPSO-PTS algorithm with the general DPSO-PTS algorithm shows that the improved algorithm has stronger PAPR reduction, whereas the complexity remains basically unchanged. A comparative simulation with the traditional PTS algorithm shows a significant reduction in complexity, with only a slight, acceptable loss of reduction performance.

In the traditional CNN design, the hyperparameters, such as the size of the convolutional kernel and stride, are difficult to determine. In this paper, a new convolutional network architecture, named multi-branch fuzzy architecture network (MBFAN), was proposed for this problem. In MBFAN, some branches with a certain convolutional neural network architecture are connected in parallel. In each branch, a different-sized convolutional kernel is applied. By data training and normalization, a weight is given to each branch. By these weights, the important features in the final output are strengthened. By normalization, the branches were interconnected together, making the training process more efficient. Due to overfitting, with the increase of branches, the MBFAN accuracy increases, and then decreases. The number of branches is optimized when the MBFAN accuracy is highest. On the other hand, the location of the convolutional kernel center in an image has a great influence on the convolutional results. This is also discussed in MBFAN. For the experiments, the proposed MBFAN was adopted and tested in a simple convolutional network and a VGG16 network.