Browsing by Author "Cheng, Hui"

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    Dynamic genetic algorithms for the dynamic load balanced clustering problem in mobile ad hoc networks
    (Elsevier, 2013) Cheng, Hui; Yang, Shengxiang; Cao, Jiannong
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    Genetic algorithms for dynamic routing problems in mobile ad hoc networks.
    (Springer-Verlag, 2013) Cheng, Hui; Yang, Shengxiang
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    Genetic algorithms with elitism-based immigrants for dynamic load balanced clustering problem in mobile ad hoc networks.
    (IEEE., 2011) Cheng, Hui; Yang, Shengxiang
    Clustering can help aggregate the topology informationand reduce the size of routing tables in a mobile adhoc network (MANET). To achieve fairness and even energy consumption, each clusterhead should ideally support the same number of cluster members. Moreover, one of the most important characteristics in MANETs is the topology dynamics, that is, the network topology changes over time due to energy conservation or node mobility. Therefore, for a dynamic and complex system like MANET, an effective clustering algorithm should efficiently adapt to each topology change and produce the new load balanced solution quickly. The maintenance of the cluster structure should be as stable as possible to reduce overhead. It requires that the new solution should try to keep most of the good parts in the previous solution. In this paper, we propose to use elitism-based immigrants genetic algorithm (EIGA) to solve the dynamic load balanced clustering problem in MANETs. Each individual represents a feasible clustering structure and its fitness is evaluated based on the load balance metric. Immigrants are introduced to help the population to handle the topology dynamics and produce new and closely related solutions. The experimental results show that EIGA can quickly adapt to the environmental changes (i.e., the network topology change) and produce highquality solutions after each change.
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    Genetic algorithms with elitism-based immigrants for dynamic shortest path problem in mobile ad hoc networks.
    (IEEE, 2009) Cheng, Hui; Yang, Shengxiang
    In recent years, the static shortest path (SP) problem has been well addressed using intelligent optimization techniques, e.g., artificial neural networks (ANNs), genetic algorithms (GAs), particle swarm optimization (PSO), etc. However, with the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile ad hoc network (MANET), wireless sensor network (WSN), etc. One of the most important characteristics in mobile wireless networks is the topology dynamics, that is, the network topology changes over time due to energy conservation or node mobility. Therefore, the SP problem turns out to be a dynamic optimization problem (DOP) in MANETs. In this paper, we propose to use elitism-based immigrants GA (EIGA) to solve the dynamic SP problem in MANETs. We consider MANETs as target systems because they represent new generation wireless networks. The experimental results show that the EIGA can quickly adapt to the environmental changes (i.e., the network topology change) and produce good solutions after each change.
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    Genetic algorithms with immigrants and memory schemes for dynamic shortest path routing problems in mobile ad hoc networks.
    (IEEE, 2010) Yang, Shengxiang; Cheng, Hui; Wang, Fang
    In recent years, the static shortest path (SP) problem has been well addressed using intelligent optimization techniques, e.g., artificial neural networks, genetic algorithms (GAs), particle swarm optimization, etc. However, with the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile networks [mobile ad hoc networks (MANETs)], wireless sensor networks, etc. One of the most important characteristics in mobile wireless networks is the topology dynamics, i.e., the network topology changes over time due to energy conservation or node mobility. Therefore, the SP routing problem in MANETs turns out to be a dynamic optimization problem. In this paper, we propose to use GAs with immigrants and memory schemes to solve the dynamic SP routing problem in MANETs. We consider MANETs as target systems because they represent new-generation wireless networks. The experimental results show that these immigrants and memory-based GAs can quickly adapt to environmental changes (i.e., the network topology changes) and produce high-quality solutions after each change.
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    Genetic algorithms with immigrants schemes for dynamic multicast problems in mobile ad hoc networks.
    (IFAC, the International Federation of Automatic Control, 2010) Yang, Shengxiang; Cheng, Hui
    In this paper, the problem of dynamic quality-of-service (QoS) multicast routing in mobile ad hoc networks is investigated. Lots of interesting works have been done on multicast since it is proved to be a NP-hard problem. However, most of them consider the static network scenarios only and the multicast tree cannot adapt to the topological changes. With the advancement in communication technologies, more and more wireless mobile networks appear, e.g., mobile ad hoc networks (MANETs). In a MANET, the network topology keeps changing due to its inherent characteristics such as the node mobility and energy conservation. Therefore, an effective multicast algorithm should track the topological changes and adapt the best multicast tree to the changes accordingly. In this paper, we propose to use genetic algorithms with immigrants schemes to solve the dynamic QoS multicast problem in MANETs. MANETs are considered as target systems because they represent a new generation of wireless networks. In the construction of the dynamic network environments, two models are proposed and investigated. One is named as the general dynamics model in which the topologies are changed due to that the nodes are scheduled to sleep or wake up. The other is named as the worst dynamics model, in which the topologies are altered because some links on the current best multicast tree are removed. Extensive experiments are conducted based on both of the dynamic network models. The experimental results show that these immigrants based genetic algorithms can quickly adapt to the environmental changes (i.e., the network topology changes) and produce high quality solutions following each change.
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    A genetic-inspired joint multicast routing and channel assignment algorithm in wireless mesh networks.
    (2008) Cheng, Hui; Yang, Shengxiang
    This paper proposes a genetic algorithm (GA) based optimization approach to search a minimum-interference multicast tree which satisfies the end-to-end delay constraint and optimizes the usage of the scarce radio network resource in wireless mesh networks. The path-oriented encoding method is used and each chromosome is represented by a tree data structure (i.e., a set of paths). Since we expect the multicast trees on which the minimum-interference channel assignment can be produced, a fitness function that returns the total channel conflict is devised. Crossover and mutation are well designed to adapt to the tree structure. A simple yet effective channel assignment algorithm is proposed to reduce the channel conflict. Simulation results show that the proposed GA based multicast algorithm achieves better performance in terms of both the total channel con°ict and the tree cost than that of a well known algorithm.
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    Guest editorial: Computational intelligence for cloud computing
    (IEEE Press, 2018-02) Cheng, Hui; Yang, Shengxiang; Yao, Xin; Zhang, Mengjie
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    Hyper-mutation based genetic algorithms for dynamic multicast routing problem in mobile ad hoc networks.
    (IEEE, 2012) Cheng, Hui; Yang, Shengxiang
    In this paper, the problem of dynamic multicast routing in mobile ad hoc networks is investigated. Lots of interesting works have been done on multicast routing since it is proved to be a NP-hard problem. However, most of them consider the static network scenarios only and the multicast tree cannot adapt to the topological changes. In a mobile ad hoc network (MANET), the network topology keeps changing due to its inherent characteristics such as node mobility and energy conservation. Therefore, an effective multicast algorithm should adapt the best multicast tree to the changes accordingly. In this paper, we propose to use two types of hyper-mutation genetic algorithms (GAs) to solve the dynamic multicast routing problem in MANETs. The two GAs are named as high low hyper-mutation GA (hlHMGA) and gradual hyper-mutation GA (grHMGA), respectively. The experimental results show that the first type of hyper-mutation GA (i.e., hlHMGA) can quickly adapt to the environmental changes (i.e., the network topology changes) and produce high quality solutions following each change.
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    Immigrants-enhanced multi-population genetic algorithms for dynamic shortest path routing problems in mobile ad hoc networks
    (Taylor & Francis Group, 2012) Cheng, Hui; Yang, Shengxiang; Wang, Xingwei
    One of the most important characteristics in mobile wireless networks is the topology dynamics, that is, the network topology changes over time as a result of energy conservation or node mobility. Therefore, the shortest path (SP) routing problem turns out to be a dynamic optimization problem in mobile wireless networks. In this article, we propose to use multi-population genetic algorithms (GAs) with an immigrants scheme to solve the dynamic SP routing problem in mobile ad hoc networks, which are the representative of new generation wireless networks. Two types of multi-population GAs are investigated. One is the forking GA in which a parent population continuously searches for a new optimum and a number of child populations try to exploit previously detected promising areas. The other is the shifting-balance GA in which a core population is used to exploit the best solution found and a number of colony populations are responsible for exploring different areas in the solution space. Both multi-population GAs are enhanced by an immigrants scheme to handle the dynamic environments. In the construction of the dynamic network environments, two models are proposed and investigated. One is called the general dynamics model, in which the topologies are changed because the nodes are scheduled to sleep or wake up. The other is called the worst dynamics model, in which the topologies are altered because some links on the current best shortest path are removed. Extensive experiments are conducted based on these two models. The experimental results show that the proposed multi-population GAs with immigrants enhancement can quickly adapt to the environmental changes (i.e., the network topology changes) and produce high-quality solutions after each change.
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    Joint multicast routing and channel assignment in multiradio multichannel wireless mesh networks using simulated annealing.
    (Springer-Verlag., 2008) Cheng, Hui; Yang, Shengxiang
    This paper proposes a simulated annealing (SA) algorithm based optimization approach to search a minimum-interference multicast tree which satisfies the end-to-end delay constraint and optimizes the usage of the scarce radio network resource in wireless mesh networks. In the proposed SA multicast algorithm, the path-oriented encoding method is adopted and each candidate solution is represented by a tree data structure (i.e., a set of paths). Since we anticipate the multicast trees on which the minimum-interference channel assignment can be produced, a fitness function that returns the total channel conflict is devised. The techniques for controlling the annealing process are well developed. A simple yet effective channel assignment algorithm is proposed to reduce the channel conflict. Simulation results show that the proposed SA based multicast algorithm can produce the multicast trees which have better performance in terms of both the total channel conflict and the tree cost than that of a well known multicast algorithm in wireless mesh networks.
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    Joint multicast routing and channel assignment in multiradio multichannel wireless mesh networks using tabu search.
    (IEEE, 2009) Cheng, Hui; Yang, Shengxiang
    This paper proposes a tabu search (TS) based optimization approach to search a minimum-interference multicast tree which satisfies the end-to-end delay constraint and optimizes the usage of the scarce radio network resource in wireless mesh networks. The path-oriented encoding method is adopted and each candidate solution is represented by a tree data structure (i.e., a set of paths). Since we expect the multicast trees on which the minimum-interference channel assignment can be produced, a fitness function that returns the total channel conflict is devised. The techniques for controlling the tabu search procedure are well developed. A simple yet effective channel assignment algorithm is proposed to reduce the channel conflict. Simulation results show that the proposed TS multicast algorithm can produce the multicast trees which have better performance in terms of both the total channel conflict and the tree cost than that of a well known multicast algorithm in wireless mesh networks.
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    Joint QoS multicast routing and channel assignment in multiradio multichannel wireless mesh networks using intelligent computational methods.
    (World Federation on Soft Computing (WFSC), 2011) Cheng, Hui; Yang, Shengxiang
    In this paper, the quality of service multicast routing and channel assignment (QoS-MRCA) problem is investigated. It is proved to be a NP-hard problem. Previous work separates the multicast tree construction from the channel assignment. Therefore they bear severe drawback, that is, channel assignment cannot work well with the determined multicast tree. In this paper, we integrate them together and solve it by intelligent computational methods. First, we develop a unified framework which consists of the problem formulation, the solution representation, the fitness function, and the channel assignment algorithm. Then, we propose three separate algorithms based on three representative intelligent computational methods (i.e., genetic algorithm, simulated annealing, and tabu search). These three algorithms aim to search minimum-interference multicast trees which also satisfy the end-to-end delay constraint and optimize the usage of the scarce radio network resource in wireless mesh networks. To achieve this goal, the optimization techniques based on state of the art genetic algorithm and the techniques to control the annealing process and the tabu search procedure are well developed separately. Simulation results show that the proposed three intelligent computational methods based multicast algorithms all achieve better performance in terms of both the total channel conflict and the tree cost than those comparative references.
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    Multi-population genetic algorithms with immigrants scheme for dynamic shortest path routing problems in mobile ad hoc networks.
    (Springer-Verlag., 2010) Cheng, Hui; Yang, Shengxiang
    The static shortest path (SP) problem has been well addressed using intelligent optimization techniques, e.g., artificial neural networks, genetic algorithms (GAs), particle swarm optimization, etc. However, with the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile ad hoc network (MANET), wireless mesh network, etc. One of the most important characteristics in mobile wireless networks is the topology dynamics, that is, the network topology changes over time due to energy conservation or node mobility. Therefore, the SP problem turns out to be a dynamic optimization problem in mobile wireless networks. In this paper, we propose to use multi-population GAs with immigrants scheme to solve the dynamic SP problem in MANETs which is the representative of new generation wireless networks. The experimental results show that the proposed GAs can quickly adapt to the environmental changes (i.e., the network topology change) and produce good solutions after each change.
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    A multipopulation parallel genetic simulated annealing-based QoS routing and wavelength assignment integration algorithm for multicast in optical networks.
    (Elsevier., 2009) Cheng, Hui; Wang, Xingwei; Yang, Shengxiang; Huang, Min
    In this paper, we propose an integrated Quality of Service (QoS) routing algorithm for optical networks. Given a QoS multicast request and the delay interval specified by users, the proposed algorithm can find a flexible-QoS-based cost suboptimal routing tree. The algorithm first constructs the multicast tree based on the multipopulation parallel genetic simulated annealing algorithm, and then assigns wavelengths to the tree based on the wavelength graph. In the algorithm, routing and wavelength assignment are integrated into a single process. For routing, the objective is to find a cost suboptimal multicast tree. For wavelength assignment, the objective is to minimize the delay of the multicast tree, which is achieved by minimizing the number of wavelength conversion. Thus both the cost of multicast tree and the user QoS satisfaction degree can approach the optimal. Our algorithm also considers load balance. Simulation results show that the proposed algorithm is feasible and effective. We also discuss the practical realization mechanisms of the algorithm.
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    QoS multicast tree construction in IP/DWDM optical internet by bio-inspired algorithms.
    (Elsevier, 2010) Yang, Shengxiang; Cheng, Hui; Wang, Xingwei; Huang, Min; Cao, Jiannong
    In this paper, two bio-inspired Quality of Service (QoS) multicast algorithms are proposed in IP over dense wavelength division multiplexing (DWDM) optical Internet. Given a QoS multicast request and the delay interval required by the application, both algorithms are able to find a flexible QoS-based cost suboptimal routing tree. They first construct the multicast trees based on ant colony optimization and artificial immune algorithm, respectively. Then a dedicated wavelength assignment algorithm is proposed to assign wavelengths to the trees aiming to minimize the delay of the wavelength conversion. In both algorithms, multicast routing and wavelength assignment are integrated into a single process. Therefore, they can find the multicast trees on which the least wavelength conversion delay is achieved. Load balance is also considered in both algorithms. Simulation results show that these two bio-inspired algorithms can construct high performance QoS routing trees for multicast applications in IP/DWDM optical Internet.
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    A review of personal communications services.
    (Nova Science Publishers., 2009) Cheng, Hui; Wang, Xingwei; Huang, Min; Yang, Shengxiang
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    A review of personal communications services.
    (IEEE, 2008) Cheng, Hui; Wang, Xingwei; Huang, Min; Yang, Shengxiang
    PCS is an acronym for personal communications service. Ubiquitous PCS can be implemented by integrating the wireless and wireline systems on the basis of intelligent network (IN), which provides network functions of terminal and personal mobility. In this chapter, we focus on various aspects of PCS. First we describe the motivation and technological evolution for personal communications. Then we introduce three key issues related to PCS: spectrum allocation, mobility, and standardization efforts. Since PCS involves several different communication technologies, we introduce its heterogeneous and distributed system architecture. Finally, IN is described in detail because it plays a critical role in the development of PCS.
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    Stability-aware multi-metric clustering in mobile ad hoc networks with group mobility.
    (Wiley, 2009) Cheng, Hui; Cao, Jiannong; Wang, Xingwei; Das, Sajal K.; Yang, Shengxiang
    Clustering can help aggregate the topology information and reduce the size of routing tables in a mobile ad hoc network (MANET). The maintenance of the cluster structure should be as stable as possible to reduce overhead and make the network topology less dynamic. Hence, stability measures the goodness of clustering. However, for a complex system like MANET, one clustering metric is far from reflecting the network dynamics. Some prior works have considered multiple metrics by combining them into one weighted sum, which suffers from intrinsic drawbacks as a scalar objective function to provide solution for multi-objective optimization. In this paper, we propose a stability-aware multi-metric clustering algorithm, which can (1) achieve stable cluster structure by exploiting group mobility and (2) optimize multiple metrics with the help of a multi-objective evolutionary algorithm (MOEA). Performance evaluation shows that our algorithm can generate a stable clustered topology and also achieve optimal solutions in small-scale networks. For large-scale networks, it outperforms the well-known weighted clustering algorithm (WCA) that uses a weighted sum of multiple metrics.