Efficient Adaptive Algorithm Research Articles

A modified Adam algorithm for deep neural network optimization

Deep Neural Networks (DNNs) are widely regarded as the most effective learning tool for dealing with large datasets, and they have been successfully used in thousands of applications in a variety of fields. Based on these large datasets, they are trained to learn the relationships between various variables. The adaptive moment estimation (Adam) algorithm, a highly efficient adaptive optimization algorithm, is widely used as a learning algorithm in various fields for training DNN models. However, it needs to improve its generalization performance, especially when training with large-scale datasets. Therefore, in this paper, we propose HN Adam, a modified version of the Adam Algorithm, to improve its accuracy and convergence speed. The HN_Adam algorithm is modified by automatically adjusting the step size of the parameter updates over the training epochs. This automatic adjustment is based on the norm value of the parameter update formula according to the gradient values obtained during the training epochs. Furthermore, a hybrid mechanism was created by combining the standard Adam algorithm and the AMSGrad algorithm. As a result of these changes, the HN_Adam algorithm, like the stochastic gradient descent (SGD) algorithm, has good generalization performance and achieves fast convergence like other adaptive algorithms. To test the proposed HN_Adam algorithm performance, it is evaluated to train a deep convolutional neural network (CNN) model that classifies images using two different standard datasets: MNIST and CIFAR-10. The algorithm results are compared to the basic Adam algorithm and the SGD algorithm, in addition to other five recent SGD adaptive algorithms. In most comparisons, the HN Adam algorithm outperforms the compared algorithms in terms of accuracy and convergence speed. AdaBelief is the most competitive of the compared algorithms. In terms of testing accuracy and convergence speed (represented by the consumed training time), the HN-Adam algorithm outperforms the AdaBelief algorithm by an improvement of 1.0% and 0.29% for the MNIST dataset, and 0.93% and 1.68% for the CIFAR-10 dataset, respectively.

Goal-oriented error analysis of iterative Galerkin discretizations for nonlinear problems including linearization and algebraic errors

We consider the goal-oriented error estimates for a linearized iterative solver for nonlinear partial differential equations. For the adjoint problem and iterative solver we consider, instead of the differentiation of the primal problem, a suitable linearization which guarantees the adjoint consistency of the numerical scheme. We derive error estimates and develop an efficient adaptive algorithm which balances the errors arising from the discretization and use of iterative solvers. Several numerical examples demonstrate the efficiency of this algorithm.

An adaptive large neighborhood search algorithm for the tugboat scheduling problem

Seaports are important connections between inland and maritime transportation. Tugboats, which can provide enough horsepower and mitigate safety risks when leaving and entering seaports for container ships, are important and limited resources. Therefore, it is essential to schedule tugboats in the management of seaports since most container ships can only enter and leave ports under the assistance of tugboats with reasonable costs. The tugboat scheduling problem considering the multiple service at multiple waypoints pattern which can better utilize the limited tugboats, is studied. In this paper, we develop an efficient adaptive large neighborhood search algorithm with an efficient feasibility check procedure to solve large-scale instances. The computational experiments verify the effectiveness of the proposed algorithm, and some managerial insights are obtained based on the sensitivity analysis.

WEND: An Efficient WSN-Assisted Emergency Navigation Algorithm for Dynamic Hazardous Ship Indoor Environments

A reliable and efficient emergency evacuation of a damaged cruise ship is essential for passenger safety. Although many advanced evacuation approaches based on wireless sensor networks (WSNs) are capable of exploring dynamic environmental hazards and provide real-time navigation service, such approaches are mainly used in buildings on land, taking no account of the complex ship structure, deadline for ship survival, and dynamic ship inclination, and therefore may fail to evacuate passengers before ship capsizing. This article proposes WEND, an efficient WSN-assisted emergency navigation algorithm for dynamic hazardous ship indoor environment, which informs each passenger about a hazard-avoid route that minimizes the total dynamic typical delay with error bound guarantee while meeting the deadline for ship capsizing in a real-time manner. To achieve this aim, WEND investigates ship interior layout to construct a 3-D topological model and analyzes the time-history of the roll motion of a damaged passenger ship to evaluate ship survival time and the dynamics of pedestrian movement. Then, an efficient adaptive emergency navigation algorithm building on the ideas of Hassin’s algorithm is presented to provide a hazard-avoid path from each passenger’s current location to boarding stations such that the worst-case delay along this path is no greater than the specified deadline, and the total dynamic typical delay is small. We evaluate our algorithm by conducting extensive simulations. The results demonstrate that WEND improves the navigation success ratio by approximately 30% and 10% compared with the state-of-the-art navigation methods, namely the expected number of oscillations (ENO)-based oscillation-free method (OPEN) and the deadline-aware adaptive emergency navigation strategy (ANT), respectively.

An efficient adaptive degree-based heuristic algorithm for influence maximization in hypergraphs

Influence maximization (IM) has shown wide applicability in immense fields over the past decades. Previous researches on IM mainly focused on the dyadic relationship but lacked the consideration of higher-order relationship between entities, which has been constantly revealed in many real systems. An adaptive degree-based heuristic algorithm, i.e., Hyper Adaptive Degree Pruning (HADP) which aims to iteratively select nodes with low influence overlap as seeds, is proposed in this work to tackle the IM problem in hypergraphs. Furthermore, we extend algorithms from ordinary networks as baselines. Results on 8 empirical hypergraphs show that HADP surpasses the baselines in terms of both effectiveness and efficiency with a maximally 46.02% improvement. Moreover, we test the effectiveness of our algorithm on synthetic hypergraphs generated by different degree heterogeneity. It shows that the improvement of our algorithm effectiveness increases from 2.66% to 14.67% with the increase of degree heterogeneity, which indicates that HADP shows high performance especially in hypergraphs with high heterogeneity, which is ubiquitous in real-world systems.

A Gaussian Type-2 Fuzzy Programming Approach for Multicrowd Congestion-Relieved Evacuation Planning

Large-scale emergencies occur frequently around the world, causing serious casualties. Emergency evacuation is one of the top priorities after a disaster. Due to the uncertain and complex evacuation processes, evacuation plans obtained in a deterministic context may not meet the requirements of practical engineering applications. This research considers a multi-crowd congestion-relieved evacuation problem in Gaussian Type-2 fuzzy environments. A path-based one destination network flow (P-ODNF) model is developed for the problem formulation. To relieve the traffic congestion, a multi-point diversion evacuation strategy is employed in the model’s construction. The uncertainties associated with the evacuation process are expressed as Gaussian Type-2 fuzzy variables. A critical value-based defuzzification technique is adopted to handle the Type-2 fuzziness. Based on the credibility measure, the uncertain P-ODNF model is transformed into its deterministic counterpart. An efficient adaptive chaos particle swarm optimization algorithm (A-CPSO) is designed for model solving. Several numerical experiments are performed to demonstrate the proposed methodology. A sensitivity analysis is performed to illustrate the logical correctness of the proposed defuzzification process. The computational results show that A-CPSO is competitive in both its effectiveness and efficiency. The proposed methodology can coordinate multiple simultaneous evacuation processes to relieve congestion and improves the overall evacuation efficiency.

An efficient adaptive MCMC algorithm for Pseudo-Bayesian quantum tomography

We revisit the Pseudo-Bayesian approach to the problem of estimating density matrix in quantum state tomography in this paper. Pseudo-Bayesian inference has been shown to offer a powerful paradigm for quantum tomography with attractive theoretical and empirical results. However, the computation of (Pseudo-)Bayesian estimators, due to sampling from complex and high-dimensional distribution, pose significant challenges that hamper their usages in practical settings. To overcome this problem, we present an efficient adaptive MCMC sampling method for the Pseudo-Bayesian estimator by exploring an adaptive proposal scheme together with subsampling method. We show in simulations that our approach is substantially computationally faster than the previous implementation by at least two orders of magnitude which is significant for practical quantum tomography.

Active Classification With Uncertainty Comparison Queries.

Noisy pairwise comparison feedback has been incorporated to improve the overall query complexity of interactively learning binary classifiers. The positivity comparison oracle is extensively used to provide feedback on which is more likely to be positive in a pair of data points. Because it is impossible to determine accurate labels using this oracle alone without knowing the classification threshold, existing methods still rely on the traditional explicit labeling oracle, which explicitly answers the label given a data point. The current method conducts sorting on all data points and uses explicit labeling oracle to find the classification threshold. However, it has two drawbacks: (1) it needs unnecessary sorting for label inference and (2) it naively adapts quick sort to noisy feedback. In order to avoid these inefficiencies and acquire information of the classification threshold at the same time, we propose a new pairwise comparison oracle concerning uncertainties. This oracle answers which one has higher uncertainty given a pair of data points. We then propose an efficient adaptive labeling algorithm to take advantage of the proposed oracle. In addition, we address the situation where the labeling budget is insufficient compared to the data set size. Furthermore, we confirm the feasibility of the proposed oracle and the performance of the proposed algorithm theoretically and empirically.

Elite Archive-Assisted Adaptive Memetic Algorithm for a Realistic Hybrid Differentiation Flowshop Scheduling Problem

This research presents an original and efficient elite archive-assisted adaptive memetic algorithm (EAMA) to deal with a realistic hybrid differentiation flowshop scheduling problem (HDFSP) with the objective of total completion time minimization. In this scheduling problem, each job consists of multiple parts and the jobs are divided into different types. The manufacturing of a job is comprised of three consecutive stages: 1) parts fabrication on first-stage parallel machines; 2) parts assembly on second-stage single machine; and 3) job differentiation on one of third-stage dedicated machines. We provide a mixed-integer programming model, derive three lower bounds, and further present the EAMA metaheuristic for HDFSP. The EAMA is initialized heuristically, and its global exploration is performed by a differential evolution, which includes three newly designed operators: 1) elite-driven discretized differential mutation; 2) probability crossover; and 3) biased selection. To enhance the local search, an external elite archive is set and evolved in parallel with global exploration by a meta-Lamarckian learning-based adaptive multistage local search and a variable length-based adaptive block-insertion local search. After the global exploration and local exploitation, an elite sharing strategy is used to exchange the excellent information between population and elite archive, and an adaptive restart strategy is used to diversify the population. The influence of parameter setting on EAMA is surveyed by using an improved design-of-experiment. The statistical results from extensive computational experiments demonstrate the effectiveness of the special designs and show that EAMA performs more efficient than the existing algorithms in solving the problem under consideration.

An Efficient Adaptive and Steep-Convergent Sidelobes Simultaneous Reduction Algorithm for Massive Linear Arrays

Antenna arrays have become an essential part of most wireless communications systems. In this paper, the unwanted sidelobes in the symmetric linear array power pattern are reduced efficiently by utilizing a faster simultaneous sidelobes processing algorithm, which generates nulling sub-beams that are adapted to control and maintain steep convergence toward lower sidelobe levels. The proposed algorithm is performed using adaptive damping and heuristic factors which result in learning curve perturbations during the first few loops of the reduction process and is followed by a very steep convergence profile towards deep sidelobe levels. The numerical results show that, using the proposed adaptive sidelobes simultaneous reduction algorithm, a maximum sidelobe level of −50 dB can be achieved after only 10 iteration loops (especially for very large antenna arrays formed by 256 elements, wherein the processing time is reduced to approximately 25% of that required by the conventional fixed damping factor case). On the other hand, the generated array weights can be applied to practical linear antenna arrays under mutual coupling effects, which have shown very similar results to the radiation pattern of the isotropic antenna elements with very deep sidelobe levels and the same beamwidth.

An efficient adaptive space partitioning algorithm for electromagnetic scattering calculation of complex 3D models

The space partitioning algorithm based on the rounding and addressing operations has been proved to be an efficient space partitioning algorithm with the potential for real-time calculation. An improvement on this kind of space partitioning algorithms for solving complex 3D models is presented. Numerical examples show that the efficiency of the improved algorithm is better than that of the original method. When the size of most target elements is smaller than the size of spatial grids, the efficiency of the improved method can be more than four times of that of the original method. An adaptive method of space partitioning based on the improved algorithm is developed by taking the surface element density or the curvature as the threshold for deep partitioning and conducting the deep partitioning using the octree method. A computer program implementation for applying the method in some typical applications is discussed, and the performance in terms of the efficiency, reliability, and resource use is evaluated. Application testing shows that the results of the adaptive spacing partitioning are more convenient for the follow-up use than that of the basic uniform space partitioning. Furthermore, when it is used to calculate the electromagnetic scattering of complex targets by the ray tracing (RT) method, the adaptive space partitioning algorithm can reduce the calculation time of the RT process by more than 40% compared with the uniform space segmentation algorithm.

Adaptive Diagnosis of Hamiltonian Networks under the Comparison Model

Adaptive diagnosis is an approach in which tests can be scheduled dynamically during the diagnosis process based on the previous test outcomes. Naturally, reducing the number of test rounds as well as the total number of tests is a major goal of an efficient adaptive diagnosis algorithm. The adaptive diagnosis of multiprocessor systems under the PMC model has been widely investigated, while adaptive diagnosis using comparison model has been independently discussed only for three networks, including hypercube, torus, and completely connected networks. In addition, adaptive diagnosis of general Hamiltonian networks is more meaningful than that of special graph. In this paper, the problem of adaptive fault diagnosis in Hamiltonian networks under the comparison model is explored. First, we propose an adaptive diagnostic scheme which takes five to six test rounds. Second, we derive a dynamic upper bound of the number of fault nodes instead of setting a value like normal. Finally, we present an algorithm such that at least one sequence obtained from cycle partition can be picked out and all nodes in this sequence can be identified based on the previous upper bound.

An efficient automatic adaptive algorithm for cohesive crack propagation modeling of concrete structures using matrix-free unstructured Galerkin Finite Volume Method

Using Galerkin Finite Volume method, a 2D adaptive algorithm is developed for crack propagation simulation of concrete structures under mixed mode loading. The matrix free Finite Volume formulation is applied to implement a crack propagation model in a fully automatic adaptive mesh refinement framework for unstructured meshes. Cohesive cracks are implemented using nonlinear interface elements with bilinear softening constitutive equation. On contrary to existing cohesive crack models where the Newton-Raphson techniques are required to simulate the nonlinear behavior in the fracture process zone, the need for such a computational costly technique is relaxed. The matrix-free Finite Volume formulation method can provide better computational performance than the matrix-base methods. The introduced Method can also relax the problems of the standard Finite Element base methods with high aspect-ratio elements. The present Finite Volume model accurately predicts the crack propagation path and correctly evaluates the load-displacement curve while consumes low computational CPU time.

English

We propose a new and efficient nonmonotone adaptive trust region algorithm to solve unconstrained optimization problems. This algorithm incorporates two novelties: it benefits from a radius dependent shrinkage parameter for adjusting the trust region radius that avoids undesirable directions and exploits a new strategy to prevent sudden increments of objective function values in nonmonotone trust region techniques. Global convergence of this algorithm is investigated under some mild conditions. Numerical experiments demonstrate the efficiency and robustness of the proposed algorithm in solving a collection of unconstrained optimization problems from the CUTEst package.

Goal-oriented mesh adaptation method for nonlinear problems including algebraic errors

We deal with the goal-oriented error estimates and mesh adaptation for nonlinear partial differential equations. The setting of the adjoint problem and the resulting estimates are not based on a differentiation of the primal problem but on a suitable linearization which guarantees the adjoint consistency of the numerical scheme. Furthermore, we develop an efficient adaptive algorithm which balances the errors arising from the discretization and the use of nonlinear as well as linear iterative solvers. Several numerical examples demonstrate the efficiency of this algorithm.

EAMA: Efficient Adaptive Migration Algorithm for Cloud Data Centers (CDCs)

The rapid demand for Cloud services resulted in the establishment of large-scale Cloud Data Centers (CDCs), which ultimately consume a large amount of energy. An enormous amount of energy consumption eventually leads to high operating costs and carbon emissions. To reduce energy consumption with efficient resource utilization, various dynamic Virtual Machine (VM) consolidation approaches (i.e., Predictive Anti-Correlated Placement Algorithm (PACPA), Resource-Utilization-Aware Energy Efficient (RUAEE), Memory-bound Pre-copy Live Migration (MPLM), m Mixed migration strategy, Memory/disk operation aware Live VM Migration (MLLM), etc.) have been considered. Most of these techniques do aggressive VM consolidation that eventually results in performance degradation of CDCs in terms of resource utilization and energy consumption. In this paper, an Efficient Adaptive Migration Algorithm (EAMA) is proposed for effective migration and placement of VMs on the Physical Machines (PMs) dynamically. The proposed approach has two distinct features: first, selection of PM locations with optimum access delay where the VMs are required to be migrated, and second, reduces the number of VM migrations. Extensive simulation experiments have been conducted using the CloudSim toolkit. The results of the proposed approach are compared with the PACPA and RUAEE algorithms in terms of Service-Level Agreement (SLA) violation, resource utilization, number of hosts shut down, and energy consumption. Results show that proposed EAMA approach significantly reduces the number of migrations by 16% and 24%, SLA violation by 20% and 34%, and increases the resource utilization by 8% to 17% with increased number of hosts shut down from 10% to 13% as compared to the PACPA and RUAEE, respectively. Moreover, a 13% improvement in energy consumption has also been observed.

Adaptive surrogate-based harmony search algorithm for design optimization of variable stiffness composite materials

Having the advancement in automated fiber placement (AFP) and three-dimensional (3D) printing, fabricating novel composites reinforced with curvilinear fibers, namely variable stiffness composites (VSCs), have gradually become feasible. Contrasted to the traditional straight fiber-reinforced composites, VSCs possess significantly enhanced mechanical properties and increased mechanical performance. However, the optimization of a VSC panel is still a challenging problem, which can be attributed to the high non-convexity of the feasible design space and highly involved computation resources. To accelerate the design and optimization of VSC structures, in this study, we propose an effective and efficient adaptive surrogate-based harmony search algorithm (ASBHSA). The VSC is modeled using the fiber path methodology through which the fiber trajectory is generated by a mathematical formulation. In addition, the manufacturability and quality of the product are guaranteed by considering the curvature constraint. For the ASBHSA, a distance-based infill criterion in combination with a radial basis function (RBF) is proposed to guide the adaptive sampling. The global best harmony search (GHS) with a good ability to find the global minimum is integrated into the developed optimization framework. After solving some popular benchmark optimization problems as well as making comparisons of the optimal designs of VSCs with those in published works, the proposed optimization framework is demonstrated being able to effectively and efficiently find the global minimum. Then, a series of designs and optimizations of laminated VSC plates under various boundary conditions and different loading conditions are carried out systematically. The reported interesting findings may provide some insights and guidance in the design of laminated VSC composites. Moreover, our proposed optimization model provides a useful tool for solving various kinds of optimization problems.

An efficient adaptive genetic algorithm for energy saving in the hybrid flow shop scheduling with batch production at last stage

AbstractThis article deals with energy saving in the hybrid flow shop scheduling problem with batch production at last stage, which has important application in energy‐intensive steelmaking‐continuous casting (SCC) process. We first establish a mixed integer programming model to reduce extra energy consumption, and then adopt genetic algorithm to solving the scheduling problem. Based on traditional genetic algorithm (TGA), the calculation of the fitness function as well as adaptive crossover and mutation are designed. Due to the complexity of the problem in this article, we then propose an efficient adaptive genetic algorithm (EAGA) to improve the search ability of TGA. The EAGA has new features including layered strategies and enhanced adaptive adjustment method. To evaluate the proposed model and algorithm, we conduct computational experiments under practical background and compare the EAGA with the several algorithms presented previously. The results illustrate that scheduling with our model can greatly reduce the extra energy consumption. Meanwhile, the proposed EAGA is very efficient in comparison.

A Portable Electromagnetic System Based on mm-Wave Radars and GNSS-RTK Solutions for 3D Scanning of Large Material Piles.

In this paper, a portable three-dimensional (3D) scanning system for the accurate characterization of large raw material (e.g., cereal grain, coal, etc.) stockpiles is presented. The system comprises an array of high resolution millimeter-wave radars and a cm-level accuracy positioning system to accurately characterize large stockpiles by means of a high-resolution 3D map, making it suitable for automation purposes. A control unit manages the data received by the sensors, which are sent to a computer system for processing. As a proof of concept, the entire sensor system is evaluated in a real environment for electromagnetically scan a scaled stockpile of coal, used in the industry for handling raw materials. In addition, a highly efficient processing adaptive algorithm that may reconstruct the scanned structure in real-time has been introduced, enabling continuous dynamic updating of the information. Results are compared with those from a photogrammetry-like technique, revealing an excellent agreement.

An efficient reliability analysis strategy for low failure probability problems

For engineering, there are two major challenges in reliability analysis. First, to ensure the accuracy of simulation results, mechanical products are usually defined implicitly by complex numerical models that require time-consuming. Second, the mechanical products are fortunately designed with a large safety margin, which leads to a low failure probability. This paper proposes an efficient and high-precision adaptive active learning algorithm based on the Kriging surrogate model to deal with the problems with low failure probability and time-consuming numerical models. In order to solve the problem with multiple failure regions, the adaptive kernel-density estimation is introduced and improved. Meanwhile, a new criterion for selecting points based on the current Kriging model is proposed to improve the computational efficiency. The criterion for choosing the best sampling points considers not only the probability of misjudging the sign of the response value at a point by the Kriging model but also the distribution information at that point. In order to prevent the distance between the selected training points from too close, the correlation between training points is limited to avoid information redundancy and improve the computation efficiency of the algorithm. Finally, the efficiency and accuracy of the proposed method are verified compared with other algorithms through two academic examples and one engineering application.