We propose a new many-objective evolutionary algorithm with Pareto-adaptive reference points. In this algorithm, the shape of the Pareto-optimal front is estimated based on a ratio of Euclidean distances. If the estimated shape is likely to be convex, the nadir point is used as the reference point to calculate convergence and diversity indicators for individuals. Otherwise, the reference point is set to the ideal point. In addition, the estimation of the nadir point is different from what was widely used in the literature. The nadir point, together with the ideal point, provides a feasible way to deal with dominance resistant solutions, which are difficult to be detected and eliminated in Pareto-based algorithms. The proposed algorithm is compared with state-of-the-art many-objective optimization algorithms on a number of unconstrained and constrained test problems with up to 15 objectives. The experimental results show that it performs better than other algorithms in most of the test instances. Moreover, the new algorithm shows good performance on problems whose Pareto-optimal fronts are irregular (being discontinuous, degenerated, bent or mixed). The observed high performance and inherent good properties (such as being free of weight vectors and control parameters) make the new proposal a promising tool for other similar problems.

Consensus representation learning is one of the most popular approaches in the field of multi-view clustering. However, most of the existing methods cannot learn discriminative representations with a clustering-friendly structure since these methods ignore the separation among clusters and the compactness within each cluster. To tackle this issue, we propose a new deep multi-view clustering network with a dual contrastive mechanism to learn clustering-friendly representations. Specifically, our method employs dual contrasting losses: a dynamic cluster diffusion loss to maximize the distance between different clusters and a reliable neighbor-guided positive alignment loss to enhance compactness within each cluster. Our approach includes several key components: view-specific encoders to extract high-level features from each view, and an adaptive feature fusion strategy to obtain consensus representations across multiple views. The dynamic cluster diffusion module ensures inter-cluster separation by maximizing distances between different clusters in the consensus feature space. Simultaneously, the reliable neighbor-guided positive alignment module improves within-cluster compactness through a pseudo-label and nearest neighbor structure-driven contrastive loss. Experimental results on several datasets show that our method can acquire clustering-friendly representations with both good properties of inter-cluster separation and within-cluster compactness, and outperforms the existing state-of-the-art approaches in clustering performance.

Two-echelon vehicle routing problem is a challenging problem that involves both strategic and tactical planning decisions on both echelons. The satellite locations and the customer distribution affect the cost of different components on the second echelon, thus the possibilities of satellite-to-customer assignment complicates the problem. In this study, we propose a graph-based fuzzy evolutionary algorithm for solving 2E-VRP. The proposed method integrates a graph-based fuzzy assignment scheme into an iteratively evolutionary learning process to minimize the total cost. To resolve the possibilities of the satellite-to-customer assignment, graph-based fuzzy operator is used to take advantage of population evolution and avoid excessive fitness evaluations of unpromising moves in different satellites. Each offspring is produced via graph-based fuzzy assignment procedure out of an assignment graph from parent individuals, and fuzzy local search procedure is used to further improve the offspring. Experimental results on the public test sets demonstrate the competitiveness of the proposed method.

Constrained multiobjective optimization problems widely exist in real-world applications. To handle them, the balance between constraints and objectives is crucial, but remains challenging due to non-negligible impacts of problem types. In our context, the problem types refer particularly to those determined by the relationship between the constrained Pareto-optimal front (PF) and the unconstrained PF. Unfortunately, there has been little awareness on how to achieve this balance when faced with different types of problems. In this article, we propose a new constraint handling technique (CHT) by taking into account potential problem types. Specifically, inspired by the prior work, problems are classified into three primary types: 1) I; 2) II; and 3) III, with the constrained PF being made up of the entire, part and none of the unconstrained counterpart, respectively. Clearly, any problem must be one of the three types. For each possible type, there exists a tailored mechanism being used to handle the relationships between constraints and objectives (i.e., constraint priority, objective priority, or the switch between them). It is worth mentioning that exact problem types are not required because we just consider their possibilities in the new CHT. Conceptually, we show that the new CHT can make a tradeoff among different types of problems. This argument is confirmed by experimental studies performed on 38 benchmark problems, whose types are known, and a real-world problem (with unknown types) in search-based software engineering. Results demonstrate that within both decomposition-based and nondecomposition-based frameworks, the new CHT can indeed achieve a good tradeoff among different problem types, being better than several state-of-the-art CHTs.

Cracks are among the most common, most likely, and earliest of all pavement distresses. Detecting and repairing cracks as early as possible can help extend the service life of pavements. However, Detecting cracks with precision can be challenging due to their varied structural characteristics and complex background interference. In this paper, a new convolutional neural network architecture, OUR-Net, is designed to more efficiently treat both high-and low-frequency visual image features. An Ocatve Convolution is incorporated into the proposed network as an enhancement to conventional convolution. In particular, an Octave Convolution Residual Block (OCRB) is embedded in the encoder to replace the convolutional layer of the classical encoder. Moerover, we propose Octave Max Unpooling (OMU) as the upsampling operation of the decoder, enabling the neural network to learn how to decode multi-spatial frequency features. Compared with models using traditional convolution, OUR-Net has better capability of processing multi-scale information, thus simultaneously improving model performance while saving computational costs by reducing spatial redundancy. We evaluate the superiority of the proposed method by comparing it to state-of-the-art crack segmentation methods on four public datasets (CrackLS315, CFD, Crack200, DeepCrack), which encompass cracks of various widths. Comprehensive experimental results reveal that the proposed method performs excellently, which achieves F1-score and mIoU of 0.9112, 0.9271, 0.8106, 0.9318, and 0.8369, 0.8644, 0.6815, 0.8723, respectively, on the four datasets. A lightweight version of the proposed network is constructed using depthwise separable convolution that achieves excellent performance with only 0.88M parameters.