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Overbreak prediction and optimization in tunnel using neural network and bee colony techniques

ENGINEERING WITH COMPUTERS 2019年第4期35卷 1191-1202页

作者： Koopialipoor, Mohammadreza Ghaleini, Ebrahim Noroozi Haghighi, Mojtaba Kanagarajan, Sujith Maarefvand, Parviz Mohamad, Edy Tonnizam Amirkabir Univ Technol Fac Civil & Environm Engn Tehran 15914 Iran Amirkabir Univ Technol Fac Min & Met Tehran Iran Bharathidhasan Univ Annai Vailankanni Arts & Sci Coll Dept Comp Sci Thanjavur 613007 Tamil Nadu India Amirkabir Univ Technol Fac Min & Met Tehran Iran Univ Teknol Malaysia Fac Civil Engn Ctr Trop Geoengn GEOTROPIK Johor Baharu 81310 Malaysia

Overbreak is an undesirable phenomenon in blasting operations. The causing factors of overbreak can be generally divided as blasting and geological parameters. Due to multiplicity of effective parameters and complexity of interactions among these parameters, empirical methods may not be fully appropriated for blasting pattern design. In this research, artificial neural network (ANN) as a powerful tool for solving such complicated problems is developed to predict overbreak induced by blasting operations in the Gardaneh Rokh tunnel, Iran. To develop an ANN model, an established database comprising of 255 datasets has been utilized. A three-layer ANN was found as an optimum model for prediction of overbreak. The coefficient of determination (R-2) and root mean square error (RMSE) values of the selected model were obtained as 0.921, 0.4820, 0.923 and 0.4277 for training and testing, respectively, which demonstrate a high capability of ANN in predicting overbreak. After selecting the best model, the selected model was used for optimization purpose using artificial bee colony (ABC) algorithm as one of the most powerful optimization algorithms. Considering this point that overbreak is one of the main problems in tunneling, reducing its amount causes to have a good tunneling operation. After making several models of optimization and variations in its weights, the optimum amount for the extra drilling was 1.63 m(2), which is 47% lower than the lowest value (3.055 m(2)). It can be concluded that ABC algorithm can be introduced as a new optimizing algorithm to minimize overbreak induced by tunneling.

关键词： Tunneling Overbreak Artificial neural network Artificial bee colony optimization algorithms

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Assessment of optimization Methods Used to Determine Plasticity Parameters Based on DIC and back Calculation Methods

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EXPERIMENTAL TECHNIQUES 2019年第4期43卷 385-396页

作者： Kowalewski, L. Gajewski, M. Warsaw Univ Technol Fac Civil Engn Al Armii Ludowej 16 PL-00637 Warsaw Poland

In this paper, we analyse various minimization algorithms applied to the problem of determining elasto-plastic material parameters using an inverse analysis and digital image correlation (DIC) system. As the DIC system, ARAMIS is used, while for the finite element solution of boundary value problems, Abaqus software is applied. Different minimization algorithms, implemented in the SciPy Python library, were initially juxtaposed, compared and evaluated based on benchmark functions. Next the proper evaluation of the algorithms was performed to determine the material parameters for isotropic metal plasticity with the Huber-Mises yield criterion and isotropic or combined kinematic-isotropic plastic hardening models. For all researchers utilizing back calculation methods based on a DIC measuring system, such analysis results may be interesting. It was concluded that among the local minimization methods, derivative free optimization algorithms, especially the Powell algorithm, perform the most efficiently.

关键词： Plasticity DIC Inverse analysis optimization algorithms Python Abaqus

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Augmented Single Loop Single Vector Algorithm Using Nonlinear Approximations of Constraints in Reliability-Based Design optimization

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JOURNAL OF MECHANICAL DESIGN 2019年第10期141卷 101403页

作者： Lind, Petter N. Olsson, Marten Royal Inst Technol KTH Dept Solid Mech SE-10044 Stockholm Sweden

Reliability-based design optimization (RBDO) aims at minimizing a function of probabilistic design variables, given a maximum allowed probability of failure. The most efficient methods available for solving moderately nonlinear problems are single loop single vector (SLSV) algorithms that use a first-order approximation of the probability of failure in order to rewrite the inherently nested structure of the loop into a more efficient single loop algorithm. The research presented in this paper takes off from the fundamental idea of this algorithm. An augmented SLSV algorithm is proposed that increases the rate of convergence by making nonlinear approximations of the constraints. The nonlinear approximations are constructed in the following way: first, the SLSV experiments are performed. The gradient of the performance function is known, as well as an estimate of the most probable failure point (MPP). Then, one extra experiment, a probe point, per performance function is conducted at the first estimate of the MPP. The gradient of each performance function is not updated but the probe point facilitates the use of a natural cubic spline as an approximation of an augmented MPP estimate. The SLSV algorithm using probing (SLSVP) also incorporates a simple and effective move limit (ML) strategy that also minimizes the heuristics needed for initiating the optimization algorithm. The size of the forward finite difference design of experiment (DOE) is scaled proportionally with the change of the ML and so is the relative position of the MPP estimate at the current iteration. Benchmark comparisons against results taken from the literature show that the SLSVP algorithm is more efficient than other established RBDO algorithms and converge in situations where the SLSV algorithm fails.

关键词： Probability optimization algorithms Design Probes Splines Failure Reliability-based optimization Approximation algorithms

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A Convex optimization Approach for Computing Correlated Choice Probabilities With Many Alternatives

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2019年第1期64卷 190-205页

作者： Ahipasaoglu, Selin Damla Li, Xiaobo Natarajan, Karthik Singapore Univ Technol & Design Engn Syst & Design Singapore 487372 Singapore Natl Univ Singapore Dept Ind Syst Engn & Management Singapore 117576 Singapore

A popular discrete choice model that incorporates correlation information is themultinomial probit (MNP) model where the random utilities of the alternatives are chosen from a multivariate normal distribution. Computing the choice probabilities is challenging in the MNP model when the number of alternatives is large. Mishra et al. (IEEE Transactions on Automatic Control, 2012) have proposed a semidefinite optimization approach to compute choice probabilities for the distribution of the random utilities that maximizes expected agent utility given only the mean, variance, and covariance information. Their model is referred to as the cross moment (CMM) model. Computing the choice probabilities with many alternatives is challenging in the CMM model, since one needs to solve large-scale semidefinite programs. We develop a simpler formulation as a representative agent model by maximizing over the choice probabilities in the unit simplex where the objective function is the sum of the expected utilities and a strongly concave perturbation function. By characterizing the perturbation function for the CMM model and its gradient, we develop a simple first-order gradient method with inexact line search to compute choice probabilities. We establish local linear convergence of this algorithm under mild assumptions on the choice probabilities. An implication of our results is that inverting the choice probabilities to compute the mean utilities is straightforward given any positive-definite covariance matrix. Numerical experiments show that this method can compute choice probabilities for a large number of alternatives within a reasonable amount of time while explicitly capturing the correlation information. Comparisons with simulationmethods for MNP and semidefinite programming methods for CMM indicate the efficacy of the method.

关键词： Choice probability optimization optimization algorithms stochastic systems

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Comparative implementation of the benchmark Dejong 5 function using flower pollination algorithm and the African buffalo optimization

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CURRENT SCIENCE 2019年第5期117卷 871-877页

作者： Odili, Julius Beneoluchi Noraziah, A. Anchor Univ Lagos Dept Math Sci Lagos Nigeria Univ Malaysia Pahang Fac Comp Syst & Software Engn Kuantan 26300 Malaysia Univ Malaysia Pahang IBM Ctr Excellence Kuantan 26300 Malaysia

This communication presents experimental research findings on the application of the flower pollination algorithm (FPA) and the African buffalo optimization (ABO) to implement the complex and fairly popular benchmark Dejong 5 function. The study aims to unravel the untapped potential of FPA and the ABO in providing good solutions to optimization problems. In addition, it explores the Dejong 5 function with the hope of attracting the attention of the research community to evaluate the capacity of the two comparative algorithms as well as the Dejong 5 function. We conclude from this study that in implementing FPA and ABO for solving the benchmark Dejong 5 problem, a population of 10 search agents and using 1000 iterations can produce effective and efficient outcomes.

关键词： Benchmark comparative implementation iteration optimization algorithms search agents test functions

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Balancing Communication and Computation in Distributed optimization

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2019年第8期64卷 3141-3155页

作者： Berahas, Albert S. Bollapragada, Raghu Keskar, Nitish Shirish Wei, Ermin Lehigh Univ Dept Ind & Syst Engn Bethlehem PA 18015 USA Northwestern Univ Dept Ind Engn & Management Sci Evanston IL 60208 USA Northwestern Univ Evanston IL 60208 USA Salesforce Res Palo Alto CA 94301 USA Northwestern Univ Dept Elect Engn & Comp Sci Evanston IL 60208 USA

Methods for distributed optimization have received significant attention in recent years owing to their wide applicability in various domains including machine learning, robotics, and sensor networks. A distributed optimization method typically consists of two key components: communication and computation. More specifically, at every iteration (or every several iterations) of a distributed algorithm, each node in the network requires some form of information exchange with its neighboring nodes (communication) and the computation step related to a (sub)-gradient (computation). The standard way of judging an algorithm via only the number of iterations overlooks the complexity associated with each iteration. Moreover, various applications deploying distributed methods may prefer a different composition of communication and computation. Motivated by this discrepancy, in this paper, we propose an adaptive cost framework that adjusts the cost measure depending on the features of various applications. We present a flexible algorithmic framework, where communication and computation steps are explicitly decomposed to enable algorithm customization for various applications. We apply this framework to the well-known distributed gradient descent (DGD) method, and show that the resulting customized algorithms, which we call DGD(t), NEAR-DGD(t), and NEAR-DGD(+), compare favorably to their base algorithms, both theoretically and empirically. The proposed NEAR-DGD(+) algorithm is an exact first-order method where the communication and computation steps are nested, and when the number of communication steps is adaptively increased, the method converges to the optimal solution. We test the performance and illustrate the flexibility of the methods, as well as practical variants, on quadratic functions and classification problems that arise in machine learning, in terms of iterations, gradient evaluations, communications, and the proposed cost framework.

关键词： Communication distributed optimization network optimization optimization algorithms

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An Efficient Solution to Structured optimization Problems using Recursive Matrices

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COMPUTER GRAPHICS FORUM 2019年第8期38卷 33-39页

作者： Rueckert, D. Stamminger, M. Univ Erlangen Nurnberg Erlangen Germany

We present a linear algebra framework for structured matrices and general optimization problems. The matrices and matrix operations are defined recursively to efficiently capture complex structures and enable advanced compiler optimization. In addition to common dense and sparse matrix types, we define mixed matrices, which allow every element to be of a different type. Using mixed matrices, the low- and high-level structure of complex optimization problems can be encoded in a single type. This type is then analyzed at compile time by a recursive linear solver that picks the optimal algorithm for the given problem. For common computer vision problems, our system yields a speedup of 3-5 compared to other optimization frameworks. The BLAS performance is benchmarked against the MKL library. We achieve a significant speedup in block-SPMV and block-SPMM. This work is implemented and released open-source as a header-only extension to the C+ + math library Eigen.

关键词： CCS Concepts center dot Computing methodologies -> Symbolic and algebraic algorithms Linear algebra algorithms optimization algorithms

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Cardinal Optimizer (COPT) User Guide

arXiv

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arXiv 2022年

作者： Ge, Dongdong Huangfu, Qi Wang, Zizhuo Wu, Jian Ye, Yinyu

Cardinal Optimizer is a high-performance mathematical programming solver forefficiently solving largescale optimization problem. This documentationprovides basic introduction to the Cardinal Optimizer. © 2022, CC... 详细信息

关键词： optimization algorithms

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Complexity and performance for two classes of noise-tolerant first-order algorithms

arXiv

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arXiv 2022年

作者： Gratton, S. Jerad, S. Toint, Ph.L. Université de Toulouse INP IRIT Toulouse France ANITI Université de Toulouse INP IRIT Toulouse France NAXYS University of Namur Namur Belgium

Two classes of algorithms for optimization in the presence of noise are presented, that do not require the evaluation of the objective function. The first generalizes the well-known Adagrad method. Its complexity is then analyzed as a function of its parameters. A second class of algorithms is then derived whose complexity is at least as good as that of the first class. Initial numerical experiments on finite-sum problems arising from deep-learning applications suggest that methods of the second class may outperform those of the *** Codes 90C60, 90C30, 90C15, 90C26, 49N30 Copyright © 2022, The Authors. All rights reserved.

关键词： optimization algorithms

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A Novel Robust Smart Energy Management and Demand Reduction for Smart Homes Based on Internet of Energy

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SENSORS 2021年第14期21卷 4756页

作者： Alhasnawi, Bilal Naji Jasim, Basil H. Rahman, Zain-Aldeen S. A. Siano, Pierluigi Basrah Univ Elect Engn Dept Basrah 61001 Iraq Southern Tech Univ Qurna Tech Inst Dept Elect Tech Basra 61016 Iraq Salerno Univ Management & Innovat Syst Dept I-84084 Fisciano SA Italy Univ Johannesburg Dept Elect & Elect Engn Sci ZA-2024 Johannesburg South Africa

In residential energy management (REM), Time of Use (ToU) of devices scheduling based on user-defined preferences is an essential task performed by the home energy management controller. This paper devised a robust REM technique capable of monitoring and controlling residential loads within a smart home. In this paper, a new distributed multi-agent framework based on the cloud layer computing architecture is developed for real-time microgrid economic dispatch and monitoring. In this paper the grey wolf optimizer (GWO), artificial bee colony (ABC) optimization algorithm-based Time of Use (ToU) pricing model is proposed to define the rates for shoulder-peak and on-peak hours. The results illustrate the effectiveness of the proposed the grey wolf optimizer (GWO), artificial bee colony (ABC) optimization algorithm based ToU pricing scheme. A Raspberry Pi3 based model of a well-known test grid topology is modified to support real-time communication with open-source IoE platform Node-Red used for cloud computing. Two levels communication system connects microgrid system, implemented in Raspberry Pi3, to cloud server. The local communication level utilizes IP/TCP and MQTT is used as a protocol for global communication level. The results demonstrate and validate the effectiveness of the proposed technique, as well as the capability to track the changes of load with the interactions in real-time and the fast convergence rate.

关键词： dynamic electricity price optimization algorithms home devices Internet of things MQTT protocol

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