Accurate traffic sign recognition is critical for safe driving, as over half of traffic accidents stem from drivers’ negligence of traffic signs. Thus, developing robust traffic sign detection methods is essential to improve road safety. While existing object detection methods have achieved remarkable success, their performance in traffic sign detection is often limited by small object sizes and low-resolution appearances. To address these issues, this study proposes a novel traffic sign detection framework with three innovative components for precise localization and classification: 1) a hierarchical feature aggregation module that emphasizes high-level semantic information for traffic sign localization; 2) a cross-layer semantic residual network that enhances recognition of small and blurred signs via hierarchical feature interaction and fusion; 3) a lightweight feature alignment unit that bridges semantic gaps between cross-level representations. These components jointly tackle the challenges of detecting small and blurred traffic signs in real-world driving scenarios. Experiments were conducted on three public datasets (TT100K, CCTSDB2021, GTSDB). Results show the proposed model outperforms other methods with comparable parameters. Additionally, dynamic motion blur augmentation was applied to datasets to simulate real driving scenarios, and experiments confirm the proposed method achieves state-of-the-art performance under such challenging conditions. Code is publicly available athttps://github.com/Mo7nex/SAttFusion-YOLO

Routing is one of the most time-consuming steps in the field-programmable gate array (FPGA) design process. Even if unceasing efforts have been made to accelerate FPGA routing, the existing work seldom pays attention to the underlying FPGA connection router. In this article, we present a fast FPGA connection router called PRoute which implements a novel prerouting-based parallel local routing algorithm. Basically, PRoute precomputes the potential routing solutions for various connection patterns on FPGAs, which can be directly used in the later practical routing. On the whole, PRoute is composed of A-star maze expansion and parallel local search. In the first part, PRoute gradually expands the maze wavefront toward the lowest-cost node to search for the target sink. For a wire-type node, PRoute invokes a fast parallel local search instead taking advantage of the prerouting results, and hence the time-expensive maze expansion can be reduced. Particularly, it allows PRoute to call one another between A-star maze expansion and parallel local search. This enables the runtime efficiency of PRoute while ensuring its global search ability. In addition, we put forward an engineering improvement to further speed up PRoute by avoiding the exploration of block output pins. To our best knowledge, this work is the first to apply the idea of prerouting for FPGAs. Experimental results show that PRoute achieves speedups of$1.8\times $,$2.4\times $,$3.2\times $,$4.1\times $, and$5.1\times $with 1, 4, 8, 16, and 32 threads relative to the baseline versatile place to route’s connection router, respectively, without degrading the quality of results.

Routing is one of the most time-consuming stages in the field-programmable gate array (FPGA) design flow. Even if various attempts have been made to reduce route time, the existing work rarely focuses on improving the underlying A*-based FPGA connection router. In this article, we present a fast FPGA connection router called FCRoute based on a novel soft routing-space pruning algorithm. Within FCRoute, a routing resource priority mechanism is applied to classify the routing resource nodes into high-priority nodes and low-priority ones. On the whole, FCRoute is composed of fast maze search and backtracking process. During the fast maze search, we explore only the high-priority nodes in the routing space. In this way, a great deal of unnecessary work can be avoided. When the fast maze search fails to find the target sink, it allows the backtracking process to explore the low-priority nodes promising to be on the best path, after which a new fast maze search is called. By avoiding the exploration of the majority of low-priority nodes, FCRoute maintains runtime efficiency while ensuring global search ability. In addition, we further accelerate FCRoute with an engineering enhancement which simplifies the cost computations of nodes. Runtime and quality of results are compared with the state-of-the-art connection router in VPR 8. Experimental results show that on average FCRoute explores less than half the number of routing resource nodes, and therefore reduces runtime by 38% while enabling the quality of results. When combined with the enhancement, FCRoute achieves an average 45% reduction on runtime without sacrificing the quality of results.

In large-scale multi-objective problems, the traditional offspring generation operators is directionless and blind, which leads to the low searching capability in a huge search space. For this, we propose a two-stage multi-objective evolutionary algorithm, named MOEA-BTS to solve large-scale multi-objective problems(LSMOPs). In MOEA-BTS, the offspring generation process is divided into two stages. In the early stage, a new hybrid of local and global search direction construction method is proposed, aiming to balance the exploitation and exploration of the search. In the late stage, a series of weight vectors divide the decision space into subspaces, where the competitive swarm optimization algorithm is performed for further precise optimizations. Experiments are conducted on the LSMOPs with 500 and 1000 decision variables and results demonstrate that our proposed algorithm can perform better than several state-of-the-art evolutionary algorithms.

The problem of reconstructing nonlinear and complex dynamical systems from available data or time series is prominent in many fields, including engineering, physical, computer, biological, and social sciences. Many methods have been proposed to address this problem and their performance is satisfactory. However, none of them can reconstruct network structure from large-scale real-time streaming data, which leads to the failure of real-time and online analysis or control of complex systems. In this article, to overcome the limitations of current methods, we first extend the network reconstruction problem (NRP) to online settings, and then develop a follow-the-regularized-leader (FTRL)-Proximal style method to address the online complex NRP; we refer to it as Online-NR. The performance of Online-NR is validated on synthetic evolutionary game network reconstruction datasets and eight real-world networks. The experimental results demonstrate that Online-NR can effectively solve the problem of online network reconstruction with large-scale real-time streaming data. Moreover, Online-NR outperforms or matches nine state-of-the-art network reconstruction methods.

The problem of inferring nonlinear and complex dynamical systems from available data is prominent in many fields, including engineering, biological, social, physical, and computer sciences. Many evolutionary algorithm (EA)-based network reconstruction methods have been proposed to address this problem, but they ignore several useful information of network structure, such as community structure, which widely exists in various complex networks. Inspired by the community structure, this article develops a community-based evolutionary multiobjective network reconstruction framework to promote the reconstruction performance of EA-based network reconstruction methods due to their good performance; we refer this framework as CEMO-NR. CEMO-NR is a generic framework and any population-based multiobjective metaheuristic algorithm can be employed as the base optimizer. CEMO-NR employs the community structure of networks to divide the original decision space into multiple small decision spaces, and then any multiobjective EA (MOEA) can be used to search for improved solutions in the reduced decision space. To verify the performance of CEMO-NR, this article also designs a test suite for complex network reconstruction problems. Three representative MOEAs are embedded into CEMO-NR and compared with their original versions, respectively. The experimental results have demonstrated the significant improvement benefiting from the proposed CEMO-NR in 30 multiobjective network reconstruction problems (MONRPs).

In recent research, hyper-heuristics have attracted increasing attention in various fields. The most appealing feature of hyper-heuristics is that they aim to provide more generalized solutions to optimization problems by searching in a high-level space of heuristics instead of direct problem domains. Despite the promising findings in hyper-heuristics, the design of more general search methodologies still presents a key research. Evolutionary multitasking is a relatively new evolutionary paradigm which attempts to solve multiple optimization problems simultaneously. It exploits the underlying similarities among different optimization tasks by transferring information among them, thus accelerating the optimization of all tasks. Inherently, hyper-heuristics and evolutionary multitasking are similar in the following three ways: 1) they both operate on third-party search spaces; 2) high-level search methodologies are universal; and 3) they both conduct cross-domain optimization. To integrate their advantages effectively, i.e., the knowledge-transfer and cross-domain optimization of evolutionary multitasking and the search in the heuristic spaces of hyper-heuristics, in this article, a unified framework of evolutionary multitasking graph-based hyper-heuristic (EMHH) is proposed. To assess the generality and effectiveness of the EMHH, population-based graph-based hyper-heuristics integrated with evolutionary multitasking to solve exam timetabling and graph-coloring problems, separately and simultaneously, are studied. The experimental results demonstrate the effectiveness, efficiency, and increased the generality of the proposed unified framework compared with single-tasking hyper-heuristics.

The disaster emergency relief plays a vital role in reducing casualties and economic losses. Emergency logistics scheduling (ELS) aims at dispatching emergency resources to the victims of disasters, which is an important event of disaster relief. In this paper, a model for ELS in disaster relief is built that includes several suppliers with a variety of resources, several kinds of vehicles, and multiple disasters. To ensure timely and effective dispatching, an objective is first designed to minimize the completion time of scheduling and the total unsatisfied time of the whole relief. Then, a multi-agent genetic algorithm (MAGA) is proposed to solve this problem. Four evolutionary operators, namely, the crossover, mutation, self-learning and the effective neighborhood competition operators are designed for agents. The proposed algorithm is applied to the case of Chi-Chi earthquake in Taiwan, and the experimental results show that the performance of this algorithm is better than that of genetic algorithm and memetic algorithm.

In this paper, a new moving block sequence (MBS) representation for resource-constrained project scheduling problems (RCPSPs) is proposed, which is different from the classical activity list that has been widely used for RCPSPs. An activity in a project of RCPSPs has fixed duration and resource demands, thus, it can be modeled as a rectangle block whose height represents the resource demands and width the duration. Therefore, by modeling every activity in a project as a block, a project that consists of N activities can be represented as a permutation of N blocks that satisfy the precedence constraints. Then, for every activity in the project, four initial positions and corresponding move modes are designed, by using which activities can be moved from their initial positions to the appropriate locations that minimize the makespan of the project without violating the precedence and resource constraints. Based on this newly designed MBS representation, the multi-agent evolutionary algorithm (MAEA) is used for solving RCPSPs. The comparison with 16 existing state-of-the-art algorithms on benchmark J30 test set, which contains 480 instances in total, shows that the proposed algorithm is competitive in solving RCPSPs.