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An algorithm for numerical nonlinear optimization: Fertile Field algorithm (FFA)

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2020年第2期11卷 865-878页

作者： Mohammadi, M. Khodaygan, S. Sharif Univ Technol Dept Mech Engn Tehran Iran

Nature, as a rich source of solutions, can be an inspirational guide to answer scientific expectations. Seed dispersal mechanism as one of the most common reproduction method among the plants is a unique technique with millions of years of evolutionary history. In this paper, inspired by plants survival, a novel method of optimization is presented, which is called Fertile Field algorithm. One of the main challenges of stochastic optimization methods is related to the efficiency of the searching process for finding the global optimal solution. Seeding procedure is the most common reproduction method among all the plants. In the proposed method, the searching process is carried out through a new algorithm based on the seed dispersal mechanisms by the wind and the animals in the field. The proposed algorithm is appropriate for continuous nonlinear optimization problems. The efficiency of the proposed method is examined in details through some of the standard benchmark functions and demonstrated its capability in comparison to other nature-inspired algorithms. Obtained results show that the proposed algorithm is efficient and accurate to find optimal solutions for multimodal optimization problems with few optimal points. To evaluate the effects of the key parameters of the proposed algorithm on the results, a sensitivity analysis is carried out. Finally, to illustrate the applicability of FFA, a continuous constrained single-objective optimization problem as an optimal engineering design is considered and discussed.

关键词： stochastic optimization algorithm Fertile field algorithm Evolutionary algorithm Nonlinear optimization

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Critical Temperature Prediction for a Superconductor: A Variational Bayesian Neural Network Approach

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IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY 2020年第4期30卷 1-5页

作者： Thanh Dung Le Noumeir, Rita Huu Luong Quach Kim, Ji Hyung Kim, Jung Ho Kim, Ho Min Ecole Technol Super Biomed Informat Proc Lab Montreal PQ H3C 1K3 Canada Jeju Natl Univ Appl Superconducting Lab Jeju Si 690756 South Korea Univ Wollongong Australian Inst Innovat Mat Inst Superconducting & Elect Mat Wollongong NSW 2522 Australia

Much research in recent years has focused on using empirical machine learning approaches to extract useful insights on the structure-property relationships of superconductor material. Notably, these approaches are bringing extreme benefits when superconductivity data often come from costly and arduously experimental work. However, this assessment cannot be based solely on an open black-box machine learning, which is not fully interpretable, because it can be counter-intuitive to understand why the model may give an appropriate response to a set of input data for superconductivity characteristic analyses, e.g., critical temperature. The purpose of this study is to describe and examine an alternative approach for predicting the superconducting transition temperature T-c from SuperCon database obtained by Japan's National Institute for Materials Science. We address a generative machine-learning framework called Variational Bayesian Neural Network using superconductors chemical elements and formula to predict Tc. In such a context, the importance of the paper in focus is twofold. First, to improve the interpretability, we adopt a variational inference to approximate the distribution in latent parameter space for the generative model. It statistically captures the mutual correlation of superconductor compounds and;then, gives the estimation for the Tc. Second, a stochastic optimization algorithm, which embraces a statistical inference named Monte Carlo sampler, is utilized to optimally approximate the proposed inference model, ultimately determine and evaluate the predictive performance. As a result, in comparison with the standard evaluation metrics, the results are promising and also agree with the existing models prevalent in the field. The R-2 value obtained is very close to the best model (0.94), whereas a considerable improvement is seen in the RMSE value (3.83 K). Notably, the proposed model is known as the first of its kind for predicting a superconductor's Tc.

关键词： Critical transition temperature machine learning bayesian neural network variational inference stochastic optimization algorithm high temperature superconducting (HTS)

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Surrogate synthesis of excitation systems for frame tangential eddy current probes

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ARCHIVES OF ELECTRICAL ENGINEERING 2021年第4期70卷 743-757页

作者： Halchenko, Volodymyr Yakovych Trembovetska, Ruslana Volodymyrivna Tychkov, Volodymyr Volodymyrovych Cherkasy State Technol Univ Cherkassy Ukraine

Existing scientific studies devoted to the design of eddy-current probes with a priori given configuration of the electromagnetic excitation field, which provide a uniform eddy current density distribution, consider a wide class of such, but are limited to the case when the probe is stationary relative to the testing object. Therefore, the actual problem is the synthesis of moving tangential eddy current probes with a frame excitation system that provides a uniform eddy current density distribution in the testing object, the solution of which is proposed in this study. Amathematical method for nonlinear surrogate synthesis of excitation systems for frame moving tangential surface eddy current probes, which implements a uniform eddy current density distribution of the testing zone object, is proposed. A metamodel of the volumetric structure of the excitation system of the frame tangential eddy current probe, applied in the process of surrogate optimal parametric synthesis, has been created. The examples of nonlinear synthesis of excitation systems using modern metaheuristic stochastic algorithms for finding the global extremum are considered. The numerical results of the obtained solutions of the problems are presented. The efficiency of the synthesized structures of excitation systems in comparison with classical analogs is shown on the graphs of the eddy current density distribution on the object surface in the testing zone.

关键词： additive neural network regression eddy current probe stochastic optimization algorithm surrogate optimization uniform eddy current density distribution velocity effect

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Dispersed spectrum sensing and scheduling in cognitive radio network based on SSOA-RR

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2020年第16期33卷 e4588-e4588页

作者： Dinesh, G. Venkatakrishnan, P. Jeyanthi, K. Meena Alias Easwari Engn Coll Dept Comp Sci & Engn Chennai Tamil Nadu India Dept Elect & Commun Engn CMR Tech Campus Hyderabad India PSNA Coll Engn & Technol Dept Elect & Commun Engn Dindigul India

Wireless communication is an emerging technology in recent days which involves the transmission of data or information over a wide range of distance. The wireless network is capable of using the unlicensed spectrum for the transmission of data for various applications like medical, science, and industries. There are cases where the licensed spectrum is not utilized up to the level. In such cases, the cognitive radio network (CRN) technology allows cognitive devices to sense it and further enables the dynamic access of the scarce resource for proper utilization. However, the excessive number of bandwidth access may lead to the occurrence of interferences among the system. This is the major issue faced in all CRNs. To resolve this, effective band scheduling mechanism in CRN has been proposed. In this research, efficient spectrum sensing is performed using stochastic optimization algorithm called Salp Swarm optimization algorithm (SSOA). This SSOA algorithm utilizes the best fitness function through three phases such as leaving, contention and joining of bands and provides a list of nonacquired list of bands. From the list of bands, the best band with majority bandwidth has been selected using Round Robin (RR) algorithm. Here, the load system is allotted dynamically. Based on the load factor, the spectrum is scheduled. As a matter of fact, the whole spectrum scheduling methodology depends primarily on the base contributions made by the operation of spectrum sensing. Finally, the performance analysis is estimated for the throughput, settling time, number of bands occupied by base station (BS), and the bands occupied by each BS. From the performance analysis, the proposed system attains better results than other conventional approaches.

关键词： cognitive radio networks stochastic optimization algorithm SSOA Round Robin algorithm spectrum sensing

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AI-based optimization of PEM fuel cell catalyst layers for maximum power density via data-driven surrogate modeling

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ENERGY CONVERSION AND MANAGEMENT 2020年第0期205卷 112460-000页

作者： Wang, Bowen Xie, Biao Xuan, Jin Jiao, Kui Tianjin Univ State Key Lab Engines 135 Yaguan Rd Tianjin 300350 Peoples R China Loughborough Univ Dept Chem Engn Loughborough Leics England

Catalyst layer (CL) is the core electrochemical reaction region of proton exchange membrane fuel cells (PEMFCs). Its composition directly determines PEMFC output performance. Existing experimental or modeling methods are still insufficient on the deep optimization of CL composition. This work develops a novel artificial intelligence (AI) framework combining a data-driven surrogate model and a stochastic optimization algorithm to achieve multi-variables global optimization for improving the maximum power density of PEMFCs. Simulation results of a three-dimensional computational fluid dynamics (CFD) PEMFC model coupled with the CL agglomerate model constitutes the database, which is then used to train the data-driven surrogate model based on Support Vector Machine (SVM), a typical AI algorithm. Prediction performance shows that the squared correlation coefficient (R-square) and mean percentage error in the test set are 0.9908 and 3.3375%, respectively. The surrogate model has demonstrated comparable accuracy to the physical model, but with much greater computation-resource efficiency: the calculation of one polarization curve will be within one second by the surrogate model, while it may cost hundreds of processor-hours by the physical CFD model. The surrogate model is then fed into a Genetic algorithm (GA) to obtain the optimal solution of CL composition. For verification, the optimal CL composition is returned to the physical model, and the percentage error between the surrogate model predicted and physical model simulated maximum power densities under the optimal CL composition is only 1.3950%. The results indicate that the proposed framework can guide the multi-variables optimization of complex systems.

关键词： Proton exchange membrane fuel cell Catalyst layer composition Agglomerate model Data-driven surrogate model stochastic optimization algorithm

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A new hybrid denoising model based on PDEs

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MULTIMEDIA TOOLS AND APPLICATIONS 2018年第10期77卷 12057-12072页

作者： Mohamadi, Neda Soheili, Ali R. Toutounian, Faezeh Ferdowsi Univ Mashhad Fac Math Sci Dept Appl Math Mashhad Iran

In this paper, a novel denoising algorithm based on the denoising methods of partial differential equations is presented. The proposed algorithm is obtained by using a stochastic algorithm for combining two denoising methods based on partial differential equations. The model provides a new approach for solving the contradiction in the image restoration. The new hybrid model has more ability to restore the image in terms of peak signal to noise ratio, blind/referenceless image spatial quality evaluator and visual quality, compared with each of denoising methods separately used. Experimental results show that our approach is more efficient in image denoising than the used denoising methods.

关键词： Image denoising stochastic optimization algorithm PDE-based model

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Learning Deep Neural Network Based Kernel Functions for Small Sample Size Classification 24th

Learning Deep Neural Network Based Kernel Functions for Smal...

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24th International Conference on Neural Information Processing (ICONIP)

作者： Zheng, Tieran Han, Jiqing Zheng, Guibin Harbin Inst Technol Sch Comp Sci & Technol Harbin Peoples R China

ISBN: (纸本)9783319700878;9783319700861

Kernel learning is to learn a kernel function based on the set of all sample pairs from training data. Even for small sample size classification tasks, the set size is mostly large enough to make a complex kernel that holds lots of parameters being well optimized. Hence, the complex kernel can be helpful in improving classification performance via providing more meaningful feature representation in kernel induced feature space. In this paper, we propose to embed a deep neural network (DNN) into kernel functions, taking its output as kernel parameter to adjust the feature representations adaptively. Two kind of DNN based kernels are defined, and both of them are proved to satisfy the Mercer theorem. Considering the connection between kernel and classifier, we optimize the proposed DNN based kernels by exploiting the GMKL alternating optimization framework. A stochastic gradient descent (SGD) based algorithm is also proposed, which still implements alternating optimization in each iteration. Furthermore, an incremental batch size method is given to reduce gradient noise gradually in optimization process. Experimental results show that our method performed better than the typical methods.

关键词： Kernel learning Small sample size classification Deep neural network stochastic optimization algorithm

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On the Convergence of Smoothed Functional stochastic optimization algorithms

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IFAC-PapersOnLine 2015年第28期48卷 229-233页

作者： Chen, Xing-Min Gao, Chao School of Mathematical Sciences Dalian University of Technology Dalian116024 China School of Information Science and Engineering Dalian Polytechnic University Dalian116034 China

Smoothed functional gradient algorithm with perturbations distributed according to the Gaussian distribution is considered for stochastic optimization problem with additive noise. A stochastic approximation algorithm with expanding truncations that uses either one-sided or two-sided gradient estimate is given. At each iteration of the algorithm only two observations are required. The algorithm is shown to be convergent under only some mild conditions © 2015

关键词： Approximation algorithms Additive noise Approximation theory Iterative methods optimization stochastic systems Functional estimate Functional gradient Simultaneous perturbation stochastic approximation stochastic approximation algorithms stochastic optimization algorithm stochastic optimization problems stochastic optimizations Strong consistency

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Conflict Resolution for Wind-Optimal Aircraft Trajectories in North Atlantic Oceanic Airspace with Wind Uncertainties 35

Conflict Resolution for Wind-Optimal Aircraft Trajectories i...

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35th IEEE/AIAA Digital Avionics Systems Conference (DASC)

作者： Rodionova, Olga Sridhar, Banavar Ng, Hok K. NASA Ames Res Ctr Mountain View CA 94043 USA Univ Calif Santa Cruz Santa Cruz CA 95064 USA

ISBN: (纸本)9781509025237

Air traffic in the North Atlantic oceanic airspace (NAT) experiences very strong winds caused by jet streams. Flying wind-optimal trajectories increases individual flight efficiency, which is advantageous when operating in the NAT. However, as the NAT is highly congested during peak hours, a large number of potential conflicts between flights are detected for the sets of wind-optimal trajectories. Conflict resolution performed at the strategic level of flight planning can significantly reduce the airspace congestion. However, being completed far in advance, strategic planning can only use predicted environmental conditions that may significantly differ from the real conditions experienced further by aircraft. The forecast uncertainties result in uncertainties in conflict prediction, and thus, conflict resolution becomes less efficient. This work considers wind uncertainties in order to improve the robustness of conflict resolution in the NAT. First, the influence of wind uncertainties on conflict prediction is investigated. Then, conflict resolution methods accounting for wind uncertainties are proposed.

关键词： North Atlantic oceanic airspace wind-optimal trajectories conflict detection and resolution wind uncertainties strategic flight planning stochastic optimization algorithm

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DYNAMIC AIRSPACE CONFIGURATIONS GENERATED BY EVOLUTIONARY algorithmS 34

DYNAMIC AIRSPACE CONFIGURATIONS GENERATED BY EVOLUTIONARY AL...

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IEEE/AIAA 34th Digital Avionics Systems Conference (DASC)

作者： Sergeeva, Marina Delahaye, Daniel Zerrouki, Leila Schede, Nick ENAC Toulouse Toulouse France EUROCONTROL Bretigny Sur Orge France

ISBN: (纸本)9781479989409

This paper focuses on the process of generating a sequence of sector configurations composed of two airspace component types Sharable Airspace Modules (SAMs) and Sectors Building Blocks (SBBs). An algorithm has been developed that manages the main features of the dynamic sectors configuration (including sector design criteria). In order to make it run efficiently a pre-processing step will be presented to create a graph modelling of the inputs. Based on this initial graph, a mathematical model is defined which can be summarized by a multi-periods geometric graph partitioning problem. State, space, objective function and constraints will be also presented. Due to the induced complexity, a stochastic optimization algorithm based on artificial evolution is then proposed. A two layer chromosome is used for such a genetic algorithm for which recombination operators are proposed. Evaluation of the algorithm will be presented with a comparison to existing tools and operational approach.

关键词： air traffic control computational complexity genetic algorithms geometry graph theory mathematical operators stochastic programming SAM SBB airspace component types artificial evolution complexity analysis constraint factor dynamic airspace configurations dynamic sector configuration evolutionary algorithms genetic algorithm graph modelling mathematical model multiperiod geometric graph partitioning problem objective function factor recombination operators sector building blocks sector configuration sequence generation sector design criteria sharable airspace modules space factor state factor stochastic optimization algorithm two-layer chromosome algorithm design and analysis Atmospheric modeling Complexity theory Heuristic algorithms Mathematical model optimization Partitioning algorithms

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