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< Page ,Total 23 >
Surrogate model assisted cooperative coevolution for large scale optimization EI Scopus SCIE
期刊论文 | 2019 , 49 (2) , 513-531 | Applied Intelligence
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Abstract :

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, since this method needs to invoke the original high dimensional simulation model when evaluating each sub-solution, thus requiring many computation resources. To alleviate this issue, this study proposes a novel surrogate model assisted cooperative coevolution (SACC) framework. SACC constructs a surrogate model for each sub-problem and employs it to evaluate corresponding sub-solutions. The original simulation model is only adopted to reevaluate a small number of promising sub-solutions selected by surrogate models, and these really evaluated sub-solutions will in turn be employed to update surrogate models. By this means, the computation cost could be greatly reduced without significantly sacrificing evaluation quality. By taking the radial basis function (RBF) and the success-history based adaptive differential evolution (SHADE) as surrogate model and optimizer, respectively, this study further designs a concrete SACC algorithm named RBF-SHADE-SACC. RBF and SHADE have only been proved to be effective on small and medium scale problems. This study scales them up to LSOPs under the SACC framework, where they are tailored to a certain extent for adapting to the characteristics of LSOPs and SACC. Empirical studies on IEEE CEC 2010 benchmark functions demonstrate that SACC can significantly enhance the sub-solution evaluation efficiency, and even with much fewer computation resources, RBF-SHADE-SACC can find much better solutions than traditional CC algorithms. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

Keyword :

Adaptive differential evolutions Cooperative co-evolution Large-scale optimization Radial Basis Function(RBF) Surrogate model

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GB/T 7714 Ren, Zhigang , Pang, Bei , Wang, Muyi et al. Surrogate model assisted cooperative coevolution for large scale optimization [J]. | Applied Intelligence , 2019 , 49 (2) : 513-531 .
MLA Ren, Zhigang et al. "Surrogate model assisted cooperative coevolution for large scale optimization" . | Applied Intelligence 49 . 2 (2019) : 513-531 .
APA Ren, Zhigang , Pang, Bei , Wang, Muyi , Feng, Zuren , Liang, Yongsheng , Chen, An et al. Surrogate model assisted cooperative coevolution for large scale optimization . | Applied Intelligence , 2019 , 49 (2) , 513-531 .
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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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Abstract :

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 et al. "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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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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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 et al. "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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Sparse Recursive Least Mean p-Power Extreme Learning Machine for Regression EI SCIE Scopus
期刊论文 | 2018 , 6 , 16022-16034 | IEEE ACCESS
WoS CC Cited Count: 2 SCOPUS Cited Count: 2
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Abstract :

Real industrial processes usually are equipped with onboard control or diagnostic systems and limit to store a complicated model. Also, measurement samples from real processes are contaminated with noises of different statistical characteristics and are produced by one-by-one way. In this case, learning algorithms with better learning performance and compact model for systems with noises of various statistics are necessary. This paper proposes a new online extreme learning machine (ELM) algorithm, namely, sparse recursive least mean p-power ELM (SRLMP-ELM). In SRLMP-ELM, a novel cost function, i.e., the sparse least mean p-power (SLMP) error criterion, provides a mechanism to update the output weights sequentially and automatically tune some parameters of the output weights to zeros. The SLMP error criterion aims to minimize the combination of the mean p-power of the errors and a sparsity penalty constraint of the output weights. For real industrial system requirements, the proposed on-line learning algorithm is able to provide more higher accuracy, compact model, and better generalization ability than ELM and online sequential ELM, whereas the non-Gaussian noises impact the processes, especially impulsive noises. Simulations are reported to demonstrate the performance and effectiveness of the proposed methods.

Keyword :

alpha-stable noises extreme learning machine non-gaussian noises online sequential learning Sparse recursive least mean p-power

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GB/T 7714 Yang, Jing , Xu, Yi , Rong, Hai-Jun et al. Sparse Recursive Least Mean p-Power Extreme Learning Machine for Regression [J]. | IEEE ACCESS , 2018 , 6 : 16022-16034 .
MLA Yang, Jing et al. "Sparse Recursive Least Mean p-Power Extreme Learning Machine for Regression" . | IEEE ACCESS 6 (2018) : 16022-16034 .
APA Yang, Jing , Xu, Yi , Rong, Hai-Jun , Du, Shaoyi , Chen, Badong . Sparse Recursive Least Mean p-Power Extreme Learning Machine for Regression . | IEEE ACCESS , 2018 , 6 , 16022-16034 .
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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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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 et al. "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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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 et al. "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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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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Abstract :

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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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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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 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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Abstract :

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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