Browsing by Author "Wang, Xingwei"
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Item Embargo A constrained multimodal multi-objective evolutionary algorithm based on adaptive epsilon method and two-level selection(Elsevier, 2025-01-15) Wang, Fengxia; Huang, Min; Yang, Shengxiang; Wang, XingweiConstrained multimodal multi-objective optimization problems (CMMOPs) commonly arise in practical problems in which multiple Pareto optimal sets (POSs) correspond to one Pareto optimal front (POF). The existence of constraints and multimodal characteristics makes it challenging to design effective algorithms that promote diversity in the decision space and convergence in the objective space. Therefore, this paper proposes a novel constrained multimodal multi-objective evolutionary algorithm, namely CM-MOEA, to address CMMOPs. In CM-MOEA, an adaptive epsilon-constrained method is designed to utilize promising infeasible solutions, promoting exploration in the search space. Then, a diversity-based offspring generation method is performed to select diverse solutions for mutation, searching for more equivalent POSs. Furthermore, the two-level environmental selection strategy that combines local and global environmental selection is developed to guarantee diversity and convergence of solutions. Finally, we design an archive update strategy that stores well-distributed excellent solutions, which more effectively approach the true POF. The proposed CM-MOEA is compared with several state-of-the-art algorithms on 17 test problems. The experimental results demonstrate that the proposed CM-MOEA has significant advantages in solving CMMOPs.Item Open Access An adaptive localized decision variable analysis approach to large scale multi-objective and many-objective optimization(IEEE Press, 2021-01) Ma, Lianbo; Huang, Min; Yang, Shengxiang; Wang, Rui; Wang, XingweiThis paper proposes an adaptive localized decision variable analysis approach under the decomposition-based framework to solve the large scale multi-objective and many objective optimization problems. Its main idea is to incorporate the guidance of reference vectors into the control variable analysis and optimize the decision variables using an adaptive strategy. Especially, in the control variable analysis, for each search direction, the convergence relevance degree of each decision variable is measured by a projection-based detection method. In the decision variable optimization, the grouped decision variables are optimized with an adaptive scalarization strategy, which is able to adaptively balance the convergence and diversity of the solutions in the objective space. The proposed algorithm is evaluated with a suite of test problems with 2-10 objectives and 200-1000 variables. Experimental results validate the effectiveness and efficiency of the proposed algorithm on the large scale multiobjective and many-objective optimization problems.Item Metadata only Fourth party logistics routing problem model with fuzzy duration time and cost discount(Elsevier, 2013) Cui, Y.; Huang, Min; Yang, Shengxiang; Lee, L. H.; Wang, XingweiItem Metadata only Fourth party logistics routing problem with fuzzy duration time(Elsevier, 2013) Huang, Min; Cui, Y.; Yang, Shengxiang; Wang, XingweiItem Open Access A green intelligent routing algorithm supporting flexible QoS for many-to-many multicast(Elsevier, 2017-07-19) Wang, Xingwei; Zhang, Jinhong; Huang, Min; Yang, ShengxiangThe tremendous energy consumption attributed to the Information and Communication Technology (ICT) field has become a persistent concern during the last few years, attracting significant academic and industrial efforts. Networks have begun to be improved towards being “green”. Considering Quality of Service (QoS) and power consumption for green Internet, a Green Intelligent flexible QoS many-to-many Multicast routing algorithm (GIQM) is presented in this paper. In the proposed algorithm, a Rendezvous Point Confirming Stage (RPCS) is first carried out to obtain a rendezvous point and the candidate Many-to-many Multicast Sharing Tree (M2ST); then an Optimal Solution Identifying Stage (OSIS) is performed to generate a modified M2ST rooted at the rendezvous point, and an optimal M2ST is obtained by comparing the original M2ST and the modified M2ST. The network topology of Cernet2, GéANT and Internet2 were considered for the simulation of GIQM. The results from a series of experiments demonstrate the good performance and outstanding power-saving potential of the proposed GIQM with QoS satisfied.Item Metadata only 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, XingweiOne 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.Item Open Access Learning to optimize: Reference vector reinforcement learning adaption to constrained many-objective optimization of industrial copper burdening system(IEEE Press, 2021-05) Ma, Lianbo; Li, Nan; Guo, Yinan; Wang, Xingwei; Yang, Shengxiang; Huang, Min; Zhang, HaoThe performance of decomposition-based algorithms is sensitive to the Pareto front shapes since their reference vectors preset in advance are not always adaptable to various problem characteristics with no a priori knowledge. For this issue, this paper proposes an adaptive reference vector reinforcement learning approach to decomposition-based algorithms for the industrial copper burdening optimization. The proposed approach involves two main operations, i.e., a reinforcement learning operation and a reference point sampling operation. Given the fact that the states of reference vectors interact with the landscape environment (quite often), the reinforcement learning operation treats the reference vector adaption process as a reinforcement learning task, where each reference vector learns from the environmental feedback and selects optimal actions for gradually fitting the problem characteristics. Accordingly, the reference point sampling operation uses estimation-of-distribution learning models to sample new reference points. Finally, the resultant algorithm is applied to handle the proposed industrial copper burdening problem. For this problem, an adaptive penalty function and a soft constraint-based relaxing approach are used to handle complex constraints. Experimental results on both benchmark problems and real-world instances verify the competitiveness and effectiveness of the proposed algorithm.Item Metadata only 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, MinIn 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.Item Open Access Penalty and prediction methods for dynamic constrained multi-objective optimization(Elsevier, 2023-04-25) Wang, Fengxia; Huang, Min; Yang, Shengxiang; Wang, XingweiDynamic constrained multi-objective optimization problems (DCMOPs) involve objective functions and constraints that vary over time, requiring optimization algorithms to track the changing Pareto optimal set (POS) quickly. This paper proposes a new dynamic constrained multi-objective evolutionary algorithm (NDCMOEA) to address this issue. Specifically, the constraint handling strategy based on a novel penalty function integrates the constraint deviation values in the objective space and the similarity deviation values in the decision space. This method promotes selecting promising infeasible solutions closer to the POS and drives the population towards the Pareto optimal front (POF). When environmental changes occur, we employ a dynamic response strategy based on random initialization and an inverse Gaussian process model (IGPM) predictor considering the information of feasible region changes. Then, the IGPM predictor uses the sampled points generated by the Latin hypercube sampling (LHS) mechanism in the preferred regions of the objective space to obtain the initial population with better convergence and diversity in the new environment. The proposed algorithm is validated on a set of test instances and a real-world fluid catalytic cracking-distillation process optimization problem. The experimental results indicate that NDCMOEA is very competitive in dealing with DCMOPs compared with several state-of-the-art algorithms.Item Metadata only QoS multicast tree construction in IP/DWDM optical internet by bio-inspired algorithms.(Elsevier, 2010) Yang, Shengxiang; Cheng, Hui; Wang, Xingwei; Huang, Min; Cao, JiannongIn 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.Item Metadata only A review of personal communications services.(Nova Science Publishers., 2009) Cheng, Hui; Wang, Xingwei; Huang, Min; Yang, ShengxiangItem Metadata only A review of personal communications services.(IEEE, 2008) Cheng, Hui; Wang, Xingwei; Huang, Min; Yang, ShengxiangPCS 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.Item Metadata only 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, ShengxiangClustering 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.