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< Page ,Total 23 >
Distributed Algorithm for Voronoi Partition of Wireless Sensor Networks with a Limited Sensing Range EI SCIE PubMed Scopus
期刊论文 | 2018 , 18 (2) | SENSORS
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Abstract :

For Wireless Sensor Networks (WSNs), the Voronoi partition of a region is a challenging problem owing to the limited sensing ability of each sensor and the distributed organization of the network. In this paper, an algorithm is proposed for each sensor having a limited sensing range to compute its limited Voronoi cell autonomously, so that the limited Voronoi partition of the entire WSN is generated in a distributed manner. Inspired by Graham's Scan (GS) algorithm used to compute the convex hull of a point set, the limited Voronoi cell of each sensor is obtained by sequentially scanning two consecutive bisectors between the sensor and its neighbors. The proposed algorithm called the Boundary Scan (BS) algorithm has a lower computational complexity than the existing Range-Constrained Voronoi Cell (RCVC) algorithm and reaches the lower bound of the computational complexity of the algorithms used to solve the problem of this kind. Moreover, it also improves the time efficiency of a key step in the Adjust-Sensing-Radius (ASR) algorithm used to compute the exact Voronoi cell. Extensive numerical simulations are performed to demonstrate the correctness and effectiveness of the BS algorithm. The distributed realization of the BS combined with a localization algorithm in WSNs is used to justify the WSN nature of the proposed algorithm.

Keyword :

distributed algorithm local information sensing capability Wireless Sensor Networks (WSNs) limited Voronoi partition

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GB/T 7714 He, Chenlong , Feng, Zuren , Ren, Zhigang . Distributed Algorithm for Voronoi Partition of Wireless Sensor Networks with a Limited Sensing Range [J]. | SENSORS , 2018 , 18 (2) .
MLA He, Chenlong 等. "Distributed Algorithm for Voronoi Partition of Wireless Sensor Networks with a Limited Sensing Range" . | SENSORS 18 . 2 (2018) .
APA He, Chenlong , Feng, Zuren , Ren, Zhigang . Distributed Algorithm for Voronoi Partition of Wireless Sensor Networks with a Limited Sensing Range . | SENSORS , 2018 , 18 (2) .
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A flocking algorithm for multi-agent systems with connectivity preservation under hybrid metric-topological interactions SCIE PubMed Scopus
期刊论文 | 2018 , 13 (2) | PLOS ONE
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Abstract :

In this paper, we propose a connectivity-preserving flocking algorithm for multi-agent systems in which the neighbor set of each agent is determined by the hybrid metric-topological distance so that the interaction topology can be represented as the range-limited Delaunay graph, which combines the properties of the commonly used disk graph and Delaunay graph. As a result, the proposed flocking algorithm has the following advantages over the existing ones. First, range-limited Delaunay graph is sparser than the disk graph so that the information exchange among agents is reduced significantly. Second, some links irrelevant to the connectivity can be dynamically deleted during the evolution of the system. Thus, the proposed flocking algorithm is more flexible than existing algorithms, where links are not allowed to be disconnected once they are created. Finally, the multi-agent system spontaneously generates a regular quasi-lattice formation without imposing the constraint on the ratio of the sensing range of the agent to the desired distance between two adjacent agents. With the interaction topology induced by the hybrid distance, the proposed flocking algorithm can still be implemented in a distributed manner. We prove that the proposed flocking algorithm can steer the multi-agent system to a stable flocking motion, provided the initial interaction topology of multi-agent systems is connected and the hysteresis in link addition is smaller than a derived upper bound. The correctness and effectiveness of the proposed algorithm are verified by extensive numerical simulations, where the flocking algorithms based on the disk and Delaunay graph are compared.

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GB/T 7714 He, Chenlong , Feng, Zuren , Ren, Zhigang . A flocking algorithm for multi-agent systems with connectivity preservation under hybrid metric-topological interactions [J]. | PLOS ONE , 2018 , 13 (2) .
MLA He, Chenlong 等. "A flocking algorithm for multi-agent systems with connectivity preservation under hybrid metric-topological interactions" . | PLOS ONE 13 . 2 (2018) .
APA He, Chenlong , Feng, Zuren , Ren, Zhigang . A flocking algorithm for multi-agent systems with connectivity preservation under hybrid metric-topological interactions . | PLOS ONE , 2018 , 13 (2) .
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A Fuzzy Model Predictive Control Based Upon Adaptive Neural Network Disturbance Observer for a Constrained Hypersonic Vehicle EI SCIE Scopus
期刊论文 | 2018 , 6 , 5927-5938 | IEEE ACCESS
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A fuzzy model predictive control scheme based upon adaptive neural network disturbance observer is proposed for the longitudinal dynamics of a constrained hypersonic vehicle (HV) in the presence of diverse disturbances. First, an equivalent disturbed fuzzy dynamic model with the varying parameters is constructed to approximate the nonlinear dynamics, where the inevitable lumped disturbances, including the fuzzy modeling error, extraneous disturbances, and model uncertainties caused by aerodynamic uncertainties, need to be suppressed. Subsequently, according to the parameter-dependent Lyapunov function, the proposed scheme taking the varying parameters into account is developed to explicitly handle the constraints of fuel equivalence ratio, elevator deflection, and angle of attack. Furthermore, based on the strong nonlinear approximation ability of neural network (NN), an adaptive neural network disturbance observer with the adaptive laws of NN's weight matrixes is established to estimate lumped disturbances, and then an additional compensator formulated by integrating the estimations of lumped disturbances and the corresponding compensation gain matrix is appended to the proposed method for suppressing the lumped disturbances directly. Finally, the comparative simulation results for tracking the reference commands of velocity and altitude demonstrate that the proposed method provides a satisfactory tracking performance even when HV is in the presence of lumped disturbances and constraints.

Keyword :

fuzzy model parameter dependent Lyapunov function Hypersonic vehicle adaptive neural network disturbance observer fuzzy model predictive control

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GB/T 7714 Ma, Yu , Cai, Yuanli . A Fuzzy Model Predictive Control Based Upon Adaptive Neural Network Disturbance Observer for a Constrained Hypersonic Vehicle [J]. | IEEE ACCESS , 2018 , 6 : 5927-5938 .
MLA Ma, Yu 等. "A Fuzzy Model Predictive Control Based Upon Adaptive Neural Network Disturbance Observer for a Constrained Hypersonic Vehicle" . | IEEE ACCESS 6 (2018) : 5927-5938 .
APA Ma, Yu , Cai, Yuanli . A Fuzzy Model Predictive Control Based Upon Adaptive Neural Network Disturbance Observer for a Constrained Hypersonic Vehicle . | IEEE ACCESS , 2018 , 6 , 5927-5938 .
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Scheduled Composite Off-Line Output Feedback Model Predictive Control for a Constrained Hypersonic Vehicle Using Polyhedral Invariant Sets EI SCIE Scopus
期刊论文 | 2018 , 31 (4) | JOURNAL OF AEROSPACE ENGINEERING
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Abstract :

This paper presents a scheduled composite off-line output feedback model predictive control strategy for a constrained hypersonic vehicle (HV) in the presence of external persistent disturbances. First, multiple linear time invariant (LTI) models are constructed to represent the nominal longitudinal dynamics of HV without external persistent disturbances. Then, by combining the construction of asymptotically stable polyhedral invariant sets for explicitly handling asymmetric constraints of control inputs and angle of attack and the state estimator for estimating partial unmeasured states, a set of local off-line output feedback model predictive control schemes are first developed for multiple LTI models. Additionally, based on the strong nonlinear approximation ability of a recurrent cerebellar model articulation controller (RCMAC), a RCMAC disturbance observer (RCMACDO) is presented to estimate the actual disturbances, and then an auxiliary compensation controller is appended to attenuate the influences of external persistent disturbances. Furthermore, by designing a proper scheduling strategy, the proposed control strategy with the overlapped stable regions is proposed for the wide tracking task. Finally, the comparative simulation results for tracking reference commands of velocity and altitude verify the effectiveness of the proposed control strategy.

Keyword :

Polyhedral invariant sets Multiple linear time invariant models Hypersonic vehicle Output feedback model predictive control Recurrent cerebellar model articulation controller (RCMAC) disturbance observer

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GB/T 7714 Ma, Yu , Cai, Yuanli . Scheduled Composite Off-Line Output Feedback Model Predictive Control for a Constrained Hypersonic Vehicle Using Polyhedral Invariant Sets [J]. | JOURNAL OF AEROSPACE ENGINEERING , 2018 , 31 (4) .
MLA Ma, Yu 等. "Scheduled Composite Off-Line Output Feedback Model Predictive Control for a Constrained Hypersonic Vehicle Using Polyhedral Invariant Sets" . | JOURNAL OF AEROSPACE ENGINEERING 31 . 4 (2018) .
APA Ma, Yu , Cai, Yuanli . Scheduled Composite Off-Line Output Feedback Model Predictive Control for a Constrained Hypersonic Vehicle Using Polyhedral Invariant Sets . | JOURNAL OF AEROSPACE ENGINEERING , 2018 , 31 (4) .
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Niching an archive-based Gaussian estimation of distribution algorithm via adaptive clustering EI Scopus
会议论文 | 2018 , 217-218 | 2018 Genetic and Evolutionary Computation Conference, GECCO 2018
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Abstract :

Traditional Gaussian estimation of distribution algorithm (EDA) may suffer from premature convergence and has a high risk of falling into local optimum when dealing with multimodal problem. In this paper, we first attempt to improve the performance of EDA by utilizing historical solutions and develop a novel archive-based EDA variant. The use of historical solutions not only enhances the search efficiency of EDA to a large extent, but also significantly reduces the population size so that a faster convergence could be achieved. Then, the archive-based EDA is further integrated with an novel adaptive clustering strategy for solving multimodal optimization problems. Taking the advantage of the clustering strategy in locating different promising areas and the powerful exploitation ability of the archive-based EDA, the resultant algorithm is endowed with strong capability in finding multiple optima. To verify the efficiency of the proposed algorithm, we tested it on a set of niching benchmark problems, the experimental results indicate that the proposed algorithm is competitive. © 2018 Copyright is held by the owner/author(s).

Keyword :

Archive Clustering Estimation of distribution algorithm (EDA) Estimation of distribution algorithms Historical solutions Multi-modal optimization Multimodal optimization problems Pre-mature convergences

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GB/T 7714 Liang, Yongsheng , Ren, Zhigang , Pang, Bei et al. Niching an archive-based Gaussian estimation of distribution algorithm via adaptive clustering [C] . 2018 : 217-218 .
MLA Liang, Yongsheng et al. "Niching an archive-based Gaussian estimation of distribution algorithm via adaptive clustering" . (2018) : 217-218 .
APA Liang, Yongsheng , Ren, Zhigang , Pang, Bei , Chen, An . Niching an archive-based Gaussian estimation of distribution algorithm via adaptive clustering . (2018) : 217-218 .
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Enhancing Gaussian Estimation of Distribution Algorithm by Exploiting Evolution Direction With Archive EI Scopus
期刊论文 | 2018 | IEEE Transactions on Cybernetics
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As a typical model-based evolutionary algorithm, estimation of distribution algorithm (EDA) possesses unique characteristics and has been widely applied in global optimization. However, the commonly used Gaussian EDA (GEDA) usually suffers from premature convergence, which severely limits its search efficiency. This paper first systematically analyzes the reasons for the deficiency of traditional GEDA, then tries to enhance its performance by exploiting the evolution direction, and finally develops a new GEDA variant named EDA&#x00B2;. Instead of only utilizing some good solutions produced in the current generation to estimate the Gaussian model, EDA&#x00B2; preserves a certain number of high-quality solutions generated in the previous generations into an archive and employs these historical solutions to assist estimating the covariance matrix of Gaussian model. By this means, the evolution direction information hidden in the archive is naturally integrated into the estimated model, which in turn can guide EDA&#x00B2; toward more promising solution regions. Moreover, the new estimation method significantly reduces the population size of EDA&#x00B2; since it needs fewer individuals in the current population for model estimation. As a result, a fast convergence can be achieved. To verify the efficiency of EDA&#x00B2;, we tested it on a variety of benchmark functions and compared it with several state-of-the-art EAs. The experimental results demonstrate that EDA&#x00B2; is efficient and competitive. IEEE

Keyword :

Archive Benchmark functions Estimation of distribution algorithm (EDA) Estimation of distribution algorithms Evolution direction High-quality solutions Historical solutions Pre-mature convergences

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GB/T 7714 Liang, Yongsheng , Ren, Zhigang , Yao, Xianghua et al. Enhancing Gaussian Estimation of Distribution Algorithm by Exploiting Evolution Direction With Archive [J]. | IEEE Transactions on Cybernetics , 2018 .
MLA Liang, Yongsheng et al. "Enhancing Gaussian Estimation of Distribution Algorithm by Exploiting Evolution Direction With Archive" . | IEEE Transactions on Cybernetics (2018) .
APA Liang, Yongsheng , Ren, Zhigang , Yao, Xianghua , Feng, Zuren , Chen, An , Guo, Wenhua . Enhancing Gaussian Estimation of Distribution Algorithm by Exploiting Evolution Direction With Archive . | IEEE Transactions on Cybernetics , 2018 .
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Boosting Cooperative Coevolution for Large Scale Optimization With a Fine-Grained Computation Resource Allocation Strategy EI Scopus
期刊论文 | 2018 | IEEE Transactions on Cybernetics
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Cooperative coevolution (CC) has shown great potential for solving large-scale optimization problems (LSOPs). However, traditional CC algorithms often waste part of the computation resource (CR) as they equally allocate CR among all subproblems. The recently developed contribution-based CC algorithms improve the traditional ones to a certain extent by adaptively allocating CR according to some heuristic rules. Different from existing works, this paper explicitly constructs a mathematical model for the CR allocation (CRA) problem in CC and proposes a novel fine-grained CRA (FCRA) strategy by fully considering both the theoretically optimal solution of the CRA model and the evolution characteristics of CC. FCRA takes a single iteration as a basic CRA unit and always selects the subproblem which is most likely to make the largest contribution to the total fitness improvement to undergo a new iteration, where the contribution of a subproblem at a new iteration is estimated according to its current contribution, current evolution status, as well as the estimation for its current contribution. We verified the efficiency of FCRA by combining it with the success-history-based adaptive differential evolution which is an excellent DE variant but has never been employed in the CC framework. Experimental results on two benchmark suites for LSOPs demonstrate that FCRA significantly outperforms existing CRA strategies and the resulting CC algorithm is highly competitive in solving LSOPs. IEEE

Keyword :

Boosting Computation resource allocations Cooperative co-evolution Differential Evolution Large-scale optimization Partitioning algorithms Resource management

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GB/T 7714 Ren, Zhigang , Liang, Yongsheng , Zhang, Aimin et al. Boosting Cooperative Coevolution for Large Scale Optimization With a Fine-Grained Computation Resource Allocation Strategy [J]. | IEEE Transactions on Cybernetics , 2018 .
MLA Ren, Zhigang et al. "Boosting Cooperative Coevolution for Large Scale Optimization With a Fine-Grained Computation Resource Allocation Strategy" . | IEEE Transactions on Cybernetics (2018) .
APA Ren, Zhigang , Liang, Yongsheng , Zhang, Aimin , Yang, Yang , Feng, Zuren , Wang, Lin . Boosting Cooperative Coevolution for Large Scale Optimization With a Fine-Grained Computation Resource Allocation Strategy . | IEEE Transactions on Cybernetics , 2018 .
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Enhancing cooperative coevolution for large scale optimization by adaptively constructing surrogate models EI Scopus
会议论文 | 2018 , 221-222 | 2018 Genetic and Evolutionary Computation Conference, GECCO 2018
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It has been shown that cooperative coevolution (CC) can effectively deal with large scale optimization problems (LSOPs) through a divide-and-conquer strategy. However, its performance is severely restricted by the current context-vector-based sub-solution evaluation method because this method requires too many computation resources. To alleviate this issue, this study proposes an adaptive surrogate model assisted CC framework which adaptively constructs surrogate models for different sub-problems by fully considering their characteristics. By this means, the computation cost could be greatly reduced without significantly sacrificing evaluation quality. Empirical studies on IEEE CEC 2010 large scale benchmark suit show that the concrete algorithm based on this framework performs well. © 2018 Copyright is held by the owner/author(s).

Keyword :

Computation costs Computation resources Context vector Cooperative co-evolution Divide and conquer Empirical studies Large-scale optimization Surrogate model

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GB/T 7714 Pang, Bei , Ren, Zhigang , Liang, Yongsheng et al. Enhancing cooperative coevolution for large scale optimization by adaptively constructing surrogate models [C] . 2018 : 221-222 .
MLA Pang, Bei et al. "Enhancing cooperative coevolution for large scale optimization by adaptively constructing surrogate models" . (2018) : 221-222 .
APA Pang, Bei , Ren, Zhigang , Liang, Yongsheng , Chen, An . Enhancing cooperative coevolution for large scale optimization by adaptively constructing surrogate models . (2018) : 221-222 .
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A global information based adaptive threshold for grouping large scale optimization problems EI Scopus
会议论文 | 2018 , 833-840 | 2018 Genetic and Evolutionary Computation Conference, GECCO 2018
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By taking the idea of divide-and-conquer, cooperative coevolution (CC) provides a powerful architecture for large scale global optimization (LSGO) problems, but its efficiency highly relies on the decomposition strategy. It has been shown that differential grouping (DG) performs well on decomposing LSGO problems by effectively detecting the interaction among decision variables. However, its decomposition accuracy highly depends on the threshold. To improve the decomposition accuracy of DG, a global information based adaptive threshold setting algorithm (GIAT) is proposed in this paper. On the one hand, by reducing the sensitivities of the indicator in DG to the roundoff error and the magnitude of contribution weight of subcomponent, we proposed a new indicator for two variables which is much more sensitive to their interaction. On the other hand, instead of setting the threshold only based on one pair of variables, the threshold is generated from the interaction information for all pair of variables. By conducting the experiments on two sets of LSGO benchmark functions, the correctness and robustness of this new indicator and GIAT were verified. © 2018 Association for Computing Machinery.

Keyword :

Cooperative co-evolution Decomposition strategy Global informations Interaction information Large scale global optimizations Large-scale optimization Problem decomposition Threshold setting

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GB/T 7714 Chen, An , Zhang, Yipeng , Yang, Yang et al. A global information based adaptive threshold for grouping large scale optimization problems [C] . 2018 : 833-840 .
MLA Chen, An et al. "A global information based adaptive threshold for grouping large scale optimization problems" . (2018) : 833-840 .
APA Chen, An , Zhang, Yipeng , Yang, Yang , Ren, Zhigang , Liang, Yongsheng , Pang, Bei . A global information based adaptive threshold for grouping large scale optimization problems . (2018) : 833-840 .
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A historical interdependency based differential grouping algorithm for large scale global optimization EI Scopus
会议论文 | 2018 , 1711-1715 | 2018 Genetic and Evolutionary Computation Conference, GECCO 2018
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Cooperative co-evolution (CC) is a powerful evolutionary computation framework for solving large scale global optimization (LSGO) problems via the strategy of “divide-and-conquer”, but its efficiency highly relies on the decomposition result. Existing decomposition algorithms either cannot obtain correct decomposition results or require a large number of fitness evaluations (FEs). To alleviate these limitations, this paper proposes a new decomposition algorithm named historical interdependency based differential grouping (HIDG). HIDG detects interdependency from the perspective of vectors. By utilizing historical interdependency information, it develops a novel criterion which can directly deduce the interdependencies among some vectors without consuming extra FEs. Coupled with an existing vector-based decomposition framework, HIDG further significantly reduces the total number of FEs for decomposition. Experiments on two sets of LSGO benchmark functions verified the effectiveness and efficiency of HIDG. © 2018 Association for Computing Machinery.

Keyword :

Benchmark functions Cooperative co-evolution Decomposition algorithm Effectiveness and efficiencies Fitness evaluations Grouping algorithm Historical interdependency Large scale global optimizations

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GB/T 7714 Chen, An , Yang, Yang , Ren, Zhigang et al. A historical interdependency based differential grouping algorithm for large scale global optimization [C] . 2018 : 1711-1715 .
MLA Chen, An et al. "A historical interdependency based differential grouping algorithm for large scale global optimization" . (2018) : 1711-1715 .
APA Chen, An , Yang, Yang , Ren, Zhigang , Liang, Yongsheng , Pang, Bei . A historical interdependency based differential grouping algorithm for large scale global optimization . (2018) : 1711-1715 .
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