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Distributed architectures wherein multiple decision-making units are employed to coordinate their decision-making/actions based on real-time communication have become increasingly important for monitoring processes that have large scales and complex structures. Typically, the development of a distributed monitoring scheme involves two key steps, that is, the decomposition of the process into subsystems, and the design of local monitors based on the configured subsystem models. In this article, we propose a distributed process monitoring approach that tackles both steps for large-scale processes. A data-driven process decomposition approach is proposed by leveraging community structure detection to divide variables into subsystems optimally via finding a maximal value of the metric of modularity. A two-layer distributed monitoring scheme is developed where local monitors are designed based on the configured subsystems of variables using canonical correlation analysis. Inner-subsystem interactions and inter-subsystem interactions are tackled by the two layers separately, such that the sensitivity of this monitoring scheme to certain types of faults is improved. We utilize a numerical example to illustrate the effectiveness and superiority of the proposed method. It is then applied to a simulated wastewater treatment process. [ABSTRACT FROM AUTHOR]

In this paper, a new fine-grained evolutionary model, called CCLA-EM, is proposed for solving the optimization problems, which greatly overcomes the premature convergence problem of the existing evolutionary algorithms. In the proposed model, a combination of an evolutionary algorithm with a cellular learning automaton is used. The population individuals are distributed on the cells of a cellular learning automaton. Each individual interacts and cooperates with the individuals of neighboring cells to reach the global optimum. Distributing the population individuals on the cells of a cellular learning automaton allows the parallel implementation of the proposed model. Also, in different stages of the proposed model, numbers generated by a chaotic process are used instead of random ones. The use of numbers generated by a chaotic process leads to a complete search of the search space and hence avoids being trapped in local optima. Experiments on various benchmarks of the community structure detection problem indicate the superiority of the proposed model to the well-known algorithms GA-net and ICLA-net. [ABSTRACT FROM AUTHOR]

Community structure is an important topological feature of complex networks. Detecting community structure is a highly challenging problem in analyzing complex networks and has great importance in understanding the function and organization of networks. Up until now, numerous algorithms have been proposed for detecting community structure in complex networks. A wide range of these algorithms use the maximization of a quality function called modularity. In this article, three different algorithms, namely, MEM‐net, OMA‐net, and GAOMA‐net, have been proposed for detecting community structure in complex networks. In GAOMA‐net algorithm, which is the main proposed algorithm of this article, the combination of genetic algorithm (GA) and object migrating automata (OMA) has been used. In GAOMA‐net algorithm, the MEM‐net algorithm has been used as a heuristic to generate a portion of the initial population. The experiments on both real‐world and synthetic benchmark networks indicate that GAOMA‐net algorithm is efficient for detecting community structure in complex networks. [ABSTRACT FROM AUTHOR]

An appropriate subsystem configuration is a prerequisite for a successful distributed control/state estimation design. Existing subsystem decomposition methods are not designed to handle simultaneous distributed estimation and control. In this article, we address the problem of subsystem decomposition of general nonlinear process networks for simultaneous distributed state estimation and distributed control based on community structure detection. A systematic procedure based on modularity is proposed. A fast folding algorithm that approximately maximizes the modularity is used in the proposed procedure to find candidate subsystem configurations. Two chemical process examples of different complexities are used to illustrate the effectiveness and applicability of the proposed approach. © 2018 American Institute of Chemical Engineers AIChE J, 65: 904–914, 2019 [ABSTRACT FROM AUTHOR]

Complex network theory is introduced to solve the islanding problem in an emergency of distribution networks. In this study, the authors put forward an intentional islanding method based on community detection. In this method, a new index has been defined called electrical edge betweenness, on the strength of edge betweenness in complex networks, which fuses electrical characteristics with topological features of actual power lines. Based on the index, the Girvan–Newman algorithm is employed to detect the community structure of distribution networks. Through referring to the modularity value (function Q) and coherent generator groups, they can get a reasonable amount and regions of communities. Then the whole distribution network can be partitioned into several self‐sustainable islands meeting the stable operation constraints. The effectiveness of the authors' proposed method is tested on a standard IEEE 118‐bus system. [ABSTRACT FROM AUTHOR]

Community structure detection in complex networks contributes greatly to the understanding of complex mechanisms in many fields. In this article, we propose a multiagent evolutionary method for discovering communities in a complex network. The focus of the method lies in the evolutionary process of computational agents in a lattice environment, where each agent corresponds to a candidate solution to the community detection problem. First, the method uses a connection-based encoding scheme to model an agent and a random-walk behavior to construct a solution. Next, it applies three evolutionary operators, i.e., competition, crossover, and mutation, to realize information exchange among agents and solution evolution.We tested the performance of our method using synthetic and real-world networks. The results show its capability in effectively detecting community structures. [ABSTRACT FROM AUTHOR]

The development of efficient algorithms for vehicle routing on time-dependent networks is one of the major challenges in routing under intelligent transportation systems. Existing vehicle routing navigation systems, whether built-in or portable, lack the ability to rely on online servers. Such systems must compute the route in a stand-alone mode with limited hardware processing/memory capacity given an origin/destination pair and departure time. In this article, we propose a computationally efficient, yet effective, hierarchical algorithm to solve the time-dependent shortest path problem. Our proposed algorithm exploits community-based hierarchical representations of road networks, and it recursively reduces the search space in each level of the hierarchy by using our proposed search strategy algorithm. Our proposed algorithm is efficient in terms of finding shortest paths in milliseconds for large-scale road networks while eliminating the need to store preprocessed shortest paths, shortcuts, lower bounds, etc. We demonstrate the performance of the proposed algorithm using data from Detroit, New York, and San Francisco road networks. [ABSTRACT FROM AUTHOR]

Discovering community structures is a fundamental problem concerning how to understand the topology and the functions of complex network. In this paper, we propose how to apply dictionary learning algorithm to community structure detection. We present a new dictionary learning algorithm and systematically compare it with other state-of-the-art models/algorithms. The results show that the proposed algorithm is highly effectively at finding the community structures in both synthetic datasets, including three types of data structures, and real world networks coming from different areas. [ABSTRACT FROM AUTHOR]