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 paper, 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 generality of the proposed unified framework compared with single-tasking hyper-heuristics.

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 paper 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 multi-objective 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 evolutionary algorithm (MOEA) can be used to search for improved solutions in the reduced decision space. To verify the performance of CEMO-NR, this paper 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 multi-objective network reconstruction problems.

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.