This paper investigates the multimodal network design problem (MMNDP) that optimizes the auto network expansion scheme and bus network design scheme in an integrated manner. The problem is formulated as a single-level...
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This paper investigates the multimodal network design problem (MMNDP) that optimizes the auto network expansion scheme and bus network design scheme in an integrated manner. The problem is formulated as a single-level mathematical program with complementarity constraints (MPCC). The decision variables, including the expanded capacity of auto links, the layout of bus routes, the fare levels and the route frequencies, are transformed into multiple sets of binary variables. The layout of transit routes is explicitly modeled using an alternative approach by introducing a set of complementarity constraints. The congestion interaction among different travel modes is captured by an asymmetric multimodal user equilibrium problem (MUE). An active-set algorithm is employed to deal with the MPCC, by sequentially solving a relaxed MMNDP and a scheme updating problem. Numerical tests on nine-node and Sioux Falls networks are performed to demonstrate the proposed model and algorithm. (C) 2014 Elsevier Ltd. All rights reserved.
In this study, an advance computational intelligence scheme is designed and implemented to solve third-order nonlinear multiple singular systems represented with Emden-Fowler differential equation (EFDE) by exploiting...
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In this study, an advance computational intelligence scheme is designed and implemented to solve third-order nonlinear multiple singular systems represented with Emden-Fowler differential equation (EFDE) by exploiting the efficacy of artificial neural networks (ANNs), genetic algorithms (GAs) and active-set algorithm (ASA), i.e., ANN-GA-ASA. In the scheme, ANNs are used to discretize the EFDE for formulation of mean squared error-based fitness function. The optimization task for ANN models of nonlinear multi-singular system is performed by integrated competency GA and ASA. The efficiency of the designed ANN-GA-ASA is examined by solving five different variants of the singular model to check the effectiveness, reliability and significance. The statistical investigations are also performed to authenticate the precision, accuracy and convergence.
An optimization of the thermal behavior of a high-power salient-pole electrical machine is presented. Temperatures are calculated with the lumped method, which provides the thermal trends with relatively low computati...
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An optimization of the thermal behavior of a high-power salient-pole electrical machine is presented. Temperatures are calculated with the lumped method, which provides the thermal trends with relatively low computational cost. This model is used to define an aggregated objective function of our nonlinear thermal optimization problem by combining the mean solid temperature with the maximum temperature criteria. The 13 design variables correspond to the main volumetric flow rates in the electrical machine, which are bounded and subjected to a nonlinear constraint, assuming a fixed geometry. Two MATLAB optimization algorithms were tested: the active-set (FMINCON solver) and the genetic algorithm (GA). Due to the strong nonlinearities of the model and the resulting nonconvex optimization problem, the GA is likely to give better results. Minimizing the mean solid temperature was demonstrated to be more important than the maximum temperature criterion. A strategic flow configuration is found to send fresh air to the second half of the cooling circuit, where air usually arrives heated. This optimal configuration provides better cooling than its current modeled configuration. This methodology should be of interest during the development phase.
We consider the problem of solving constrained numerical optimization problems where the objective function is a black box, but the constraint functions are known explicitly. A recently proposed active-set approach im...
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ISBN:
(纸本)9781450349208
We consider the problem of solving constrained numerical optimization problems where the objective function is a black box, but the constraint functions are known explicitly. A recently proposed active-set approach implemented in an evolution strategy that interleaves the evolution of the activeset with the search for better candidate solutions is able to solve unimodal problems from a commonly used test function set with relatively small numbers of objective function evaluations. We observe that the algorithm may under some conditions exhibit long phases of stagnation and propose a novel policy for considering constraints for release from the activeset. The algorithm using the revised policy is seen to be able to avoid the stagnation observed in runs of the original strategy.
Nonnegative matrix factorization (NMF) has been successfully used as a clustering method especially for flat partitioning of documents. In this paper, we propose an efficient hierarchical document clustering method ba...
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ISBN:
(纸本)9781450321747
Nonnegative matrix factorization (NMF) has been successfully used as a clustering method especially for flat partitioning of documents. In this paper, we propose an efficient hierarchical document clustering method based on a new algorithm for rank-2 NMF. When the two block coordinate descent framework of nonnegative least squares is applied to computing rank-2 NMF, each subproblem requires a solution for nonnegative least squares with only two columns in the matrix. We design the algorithm for rank-2 NMF by exploiting the fact that an exhaustive search for the optimal activeset can be performed extremely fast when solving these NNLS problems. In addition, we design a measure based on the results of rank-2 NMF for determining which leaf node should be further split. On a number of text data sets, our proposed method produces high-quality tree structures in significantly less time compared to other methods such as hierarchical K-means, standard NMF, and latent Dirichlet allocation.
In this investigation, nature-inspired heuristic strategy exploiting moth flame optimization (MFO) algorithm combined with active-set algorithm (ASA), interior point algorithm (IPA) and sequential quadratic programmin...
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In this investigation, nature-inspired heuristic strategy exploiting moth flame optimization (MFO) algorithm combined with active-set algorithm (ASA), interior point algorithm (IPA) and sequential quadratic programming (SQP) are presented to take care of the enhancement issues of economic load dispatch (ELD) problem involving valve point loading effect (VPLE) and stochastic wind (SW). The strength of MFO algorithm is used as a global search mechanism that explore and exploit the entire search space while ASA, IPA and SQP are responsible for refinement of local optimum. The performance of the design system is based on 40 generating units including 37 thermal and 3 wind power units and is evaluated to verify the effectiveness of the scheme. The worth of the design integrated heuristic of MFO algorithm is endorsed through outcomes of the state of the art counterpart solvers in case of ELD problems integrated with wind power units in terms of cost minimization and computational complexity parameters. (C) 2021 Elsevier B.V. All rights reserved.
This research work is to design a neural-swarming heuristic procedure for numerical investigations of Singular Multi-Pantograph Delay Differential (SMP-DD) equation by applying the function approximation aptitude of A...
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This research work is to design a neural-swarming heuristic procedure for numerical investigations of Singular Multi-Pantograph Delay Differential (SMP-DD) equation by applying the function approximation aptitude of Artificial Neural Networks (ANNs) optimized efficient swarming mechanism based on Particle Swarm Optimization (PSO) integrated with convex optimization with activeset (AS) algorithm for rapid refinements, named as ANN-PSO-AS. A merit function (MF) on mean squared error sense is designed by using the differential ANN models and boundary condition. The optimization of this MF is executed with the global PSO and local search AS approaches. The planned ANN-PSO-AS approach is instigated for three different SMP-DD model-based equations. The assessment with available standard results relieved the effectiveness, robustness and precision that is further authenticated through statistical investigations of Variance Account For, Root Mean Squared Error, Semi-Interquartile Range and Theil's inequality coefficient performances.
Interface crack problems arising in quasibrittle fracture due to contact with cohesion or plasticity between the crack faces are considered. These problems are described by a hemivariational inequality. Its solvabilit...
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Interface crack problems arising in quasibrittle fracture due to contact with cohesion or plasticity between the crack faces are considered. These problems are described by a hemivariational inequality. Its solvability is guaranteed by the variational principle, which yields minimization of a nonconvex and nondifferentiable objective functional associated to the total potential energy. To compute solutions of the hemivariational inequality, a primal-dual active-set algorithm is suggested, which obeys global and monotone convergence properties. A numerical example of the quasibrittle fracture is presented.
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