scholarly journals Non-Deterministic Methods and Surrogates in the Design of Rockfill Dams

2021 ◽  
Vol 11 (8) ◽  
pp. 3699
Author(s):  
Rajeev Das ◽  
Azzedine Soulaimani
Keyword(s):  
Inverse Analysis ◽  
Polynomial Regression ◽  
Optimization Technique ◽  
Constitutive Models ◽  
Optimization Methods ◽  
Local Optima ◽  
Actual Performance ◽  
Deterministic Algorithms ◽  
Deterministic Methods ◽  
Deterministic Framework

The parameters of the constitutive models used in the design of rockfill dams are associated with a high degree of uncertainty. This occurs because rockfill dams are comprised of numerous zones, each with different soil materials, and it is not feasible to extract materials from such structures to accurately ascertain their behavior or their respective parameters. The general approach involves laboratory tests using small material samples or empirical data from the literature. However, such measures lack an accurate representation of the actual scenario, resulting in uncertainties. This limits the suitability of the model in the design process. Inverse analysis provides an option to better understand dam behavior. This procedure involves the use of real monitored data, such as deformations and stresses, from the dam structure via installed instruments. Fundamentally, it is a non-destructive approach that considers optimization methods and actual performance data to determine the values of the parameters by minimizing the differences between simulated and observed results. This paper considers data from an actual rockfill dam and proposes a surrogate assisted non-deterministic framework for its inverse analysis. A suitable error/objective function that measures the differences between the actual and simulated displacement values is defined first. Non-deterministic algorithms are used as the optimization technique, as they can avoid local optima and are more robust when compared to the conventional deterministic methods. Three such approaches, the genetic algorithm, differential evolution, and particle swarm optimization are evaluated to identify the best strategy in solving problems of this nature. A surrogate model in the form of a polynomial regression is studied and recommended in place of the actual numerical model of the dam to reduce computation cost. Finally, this paper presents the relevant dam parameters estimated by the analysis and provides insights into the performance of the three procedures to solve the inverse problem.

scholarly journals Application of Genetic Algorithm in Common Optimization Problems

2019 ◽  
Vol 8 (1) ◽  
pp. 17-21
Author(s):  
Nika Topuria ◽  
Omar Kikvidze
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Local Optima ◽  
Deterministic Algorithms ◽  
Complex Optimization ◽  
Short Time ◽  
Fitness Parameter

Use of non-deterministic algorithms for solving multi-variable optimization problems is widely used nowadays. Genetic Algorithm belongs to a group of stochastic biomimicry algorithms, it allows us to achieve optimal or near-optimal results in large optimization problems in exceptionally short time (compared to standard optimization methods). Major advantage of Genetic Algorithm is the ability to fuse genes, to mutate and do selection based on fitness parameter. These methods protect us from being trapped in local optima (Most of deterministic algorithms are prone to getting stuck on local optima). In this paper we experimentally show the upper hand of Genetic Algorithms compared to other traditional optimization methods by solving complex optimization problem.

scholarly journals Clustering by Hybrid K-Harmonic Means and Biogeography Based Optimization Algorithm for Medical Data

2020 ◽  
Vol 9 (6) ◽  
pp. 517-522
Keyword(s):  
Clustering Algorithm ◽  
Fitness Function ◽  
Optimization Technique ◽  
Optimization Methods ◽  
Medical Data ◽  
Local Optima ◽  
Computation Cost ◽  
Actual Creation ◽  
Partition Clustering ◽  
Harmonic Means

: In today trendy world hybrid based optimized data clustering is unique and imperative clustering tool in the area of data mining, which is dynamic research of actual creation problems. The oldest and furthermost commonly used popular clustering technique is the K-means(KM) algorithm, which is very complex and for the initialization of the cluster centroid and it will easily go for premature converge. This initialization problem of K-means can be evaded by built in boost function of K-Harmonic Means, which is centroid based clustering algorithm and also unresponsive for collection of initial partition clustering , but it can easily go for pre-matured conjunction in local optima. To avoid this convergence problem, this proposed algorithm uses Boosting K-harmonic means(KHM) algorithm with BBO to produce more precise, robust, better clustering solution in few number of iterations, evade conning in local optima and simply convergence to relate with Harmonic Means, BBO algorithms. Biogeography based algorithm works with the concept of emigration and immigration of inhabitants from one location to another location, Which has high computation cost. For avoiding this high computation cost in this hybrid optimization technique Biogeography-Based Optimization (BBO) is integrated with K-Harmonic means algorithm to produce optimum and effective clustering solution with faster convergence. BBO is universal optimization methods to solve utmost of the optimization problem, which is an production based generation of evolutionary algorithm (EA)that augments a function by stochastically and re peatedly improving the clustering solution of quality, or fitness function. The experimental results of this paper shown as the projected method is very resourceful and faster to afford better clustering solution in less number of repetitions for medical data

Optimizing the Learning Process of Feedforward Neural Networks Using Lightning Search Algorithm

2016 ◽  
Vol 25 (06) ◽  
pp. 1650033 ◽  
Author(s):  
Hossam Faris ◽  
Ibrahim Aljarah ◽  
Nailah Al-Madi ◽  
Seyedali Mirjalili
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Back Propagation ◽  
Feedforward Neural Networks ◽  
Training Algorithms ◽  
Local Optima ◽  
Local Solutions

Evolutionary Neural Networks are proven to be beneficial in solving challenging datasets mainly due to the high local optima avoidance. Stochastic operators in such techniques reduce the probability of stagnation in local solutions and assist them to supersede conventional training algorithms such as Back Propagation (BP) and Levenberg-Marquardt (LM). According to the No-Free-Lunch (NFL), however, there is no optimization technique for solving all optimization problems. This means that a Neural Network trained by a new algorithm has the potential to solve a new set of problems or outperform the current techniques in solving existing problems. This motivates our attempts to investigate the efficiency of the recently proposed Evolutionary Algorithm called Lightning Search Algorithm (LSA) in training Neural Network for the first time in the literature. The LSA-based trainer is benchmarked on 16 popular medical diagnosis problems and compared to BP, LM, and 6 other evolutionary trainers. The quantitative and qualitative results show that the LSA algorithm is able to show not only better local solutions avoidance but also faster convergence speed compared to the other algorithms employed. In addition, the statistical test conducted proves that the LSA-based trainer is significantly superior in comparison with the current algorithms on the majority of datasets.

Solving Mono- and Multi-Objective Problems Using Hybrid Evolutionary Algorithms and Nelder-Mead Method

2021 ◽  
Vol 12 (4) ◽  
pp. 98-116
Author(s):  
Noureddine Boukhari ◽  
Fatima Debbat ◽  
Nicolas Monmarché ◽  
Mohamed Slimane
Keyword(s):  
Convergence Rate ◽  
Optimization Problem ◽  
Optimization Problems ◽  
State Of The Art ◽  
Hybrid Approach ◽  
Optimization Methods ◽  
Global Optimum ◽  
Stochastic Methods ◽  
Local Optima ◽  
Portfolio Optimization Problem

Evolution strategies (ES) are a family of strong stochastic methods for global optimization and have proved their capability in avoiding local optima more than other optimization methods. Many researchers have investigated different versions of the original evolution strategy with good results in a variety of optimization problems. However, the convergence rate of the algorithm to the global optimum stays asymptotic. In order to accelerate the convergence rate, a hybrid approach is proposed using the nonlinear simplex method (Nelder-Mead) and an adaptive scheme to control the local search application, and the authors demonstrate that such combination yields significantly better convergence. The new proposed method has been tested on 15 complex benchmark functions and applied to the bi-objective portfolio optimization problem and compared with other state-of-the-art techniques. Experimental results show that the performance is improved by this hybridization in terms of solution eminence and strong convergence.

scholarly journals Numerical Simulation of the Fracture Behaviour of High-Performance Fibre Reinforced Concrete by Using a Cohesive-Crack-Based Inverse Analysis

2021 ◽  
Author(s):  
Alejandro Enfedaque ◽  
Marcos G. Alberti ◽  
Jaime C. Gálvez ◽  
Pedro Cabanas
Keyword(s):  
Mechanical Properties ◽  
Reinforced Concrete ◽  
Inverse Analysis ◽  
High Performance ◽  
Fracture Behaviour ◽  
Constitutive Models ◽  
Cohesive Crack ◽  
Fibre Reinforced Concrete ◽  
Steel Fibres ◽  
Structural Applications

Fibre reinforced concrete (FRC) has become an alternative for structural applications due its outstanding mechanical properties. The appearance of new types of fibres and the fibre cocktails that can be configured mixing them has created FRC that clearly exceed the minimum mechanical properties required in the standards. Consequently, in order to take full advantage of the contribution of the fibres in construction projects, it is of great interest to have constitutive models that simulate the behaviour of the materials. This study aimed to simulate the fracture behaviour of five types of FRC, three with steel hooked fibres, one with a combination of two types of steel fibres and one with a combination of polyolefin fibres and two types of steel fibres, by means of an inverse analysis based on the cohesive crack approach. The results of the numerical simulations defined the softening functions of each FRC formulation and have pointed out the synergies that are created through use of fibre cocktails. The information obtained might suppose a remarkable advance for designers using high-performance FRC in structural elements.

scholarly journals Online Optimization for Networked Distributed Energy Resources with Time-Coupling Constraints

2019 ◽  
Author(s):  
Shuai Fan ◽  
guangyu he ◽  
Xinyang Zhou ◽  
Mingjian Cui
Keyword(s):  
Optimization Technique ◽  
Optimization Methods ◽  
Distribution Networks ◽  
Distributed Energy ◽  
Lyapunov Optimization ◽  
Operation Conditions ◽  
Coupling Constraints ◽  
Online Implementation ◽  
Algorithmic Framework ◽  
Utility Loss

This paper proposes a Lyapunov optimization-based <a><b> </b></a>online distributed (LOOD) algorithmic framework for active distribution networks with numerous photovoltaic inverters and invert air conditionings (IACs). In the proposed scheme, ADNs can track an active power setpoint reference at the substation in response to transmission-level requests while concurrently minimizing the utility loss and ensuring the security of voltages. In contrast to conventional distributed optimization methods that employ the setpoints for controllable devices only when the algorithm converges, the proposed LOOD can carry out the setpoints immediately relying on the current measurements and operation conditions. Notably, the time-coupling constraints of IACs are decoupled for online implementation with Lyapunov optimization technique. An incentive scheme is tailored to coordinate the customer-owned assets in lieu of the direct control from network operators. Optimality and convergency are characterized analytically. Finally, we corroborate the proposed method on a modified version of 33-node test feeder. <div><br></div>

An energy-efficient scalar control taking core losses into account

2018 ◽  
Vol 37 (2) ◽  
pp. 849-867 ◽  
Author(s):  
Gabriel Khoury ◽  
Ragi Ghosn ◽  
Flavia Khatounian ◽  
Maurice Fadel ◽  
Mathias Tientcheu
Keyword(s):  
Energy Efficient ◽  
Mechanical Load ◽  
Dynamic Performance ◽  
Optimization Technique ◽  
Optimization Methods ◽  
Variable Speed ◽  
Variable Speed Drives ◽  
Content Type ◽  
Scalar Control ◽  
Core Losses

PurposeIn the need to optimize the energy efficiency, control structures can have a positive effect by tracking the optimal operating point according to the speed and mechanical load of the motor. The purpose of this paper is to present an energy-efficient scalar control for squirrel-cage induction motors (IMs), taking into consideration the effect of core losses. Design/methodology/approachThe proposed technique is based on the modification of the stator flux reference, to track the best efficiency point. The optimal flux values are computed through an improved model of the IM including core losses, then stored in a look-up table. FindingsSimulations of the proposed scalar control are carried out, and results show the efficiency improvement when the flux is optimized especially at low load cases. Results were validated experimentally on two motors compliant with different efficiency standards. Practical implicationsThe proposed approach can be used in several fields and applications using the scalar-controlled IM with a proper implementation in variable speed drives, as in the cases of pumps, compressors and blowers. Originality/valueThe proposed technique is compared to existing optimization methods in literature, and the results show an improvement in the dynamic performance and in the response delays. The approach is also compared to an optimization technique used in industries like Leroy-Somer for variable speed drives, and efficiency improvements are shown.

scholarly journals Application and benchmarking of a direct method to optimize the fuel consumption of a diesel electric locomotive

2018 ◽  
Vol 232 (9) ◽  
pp. 2272-2289 ◽  
Author(s):  
V Macian ◽  
C Guardiola ◽  
B Pla ◽  
A Reig
Keyword(s):  
Optimal Control ◽  
Direct Method ◽  
Minimum Principle ◽  
Optimization Technique ◽  
Direct Methods ◽  
Optimization Methods ◽  
Computational Time ◽  
Electric Locomotive ◽  
Pontryagin Minimum Principle ◽  
A Direct Method

This paper addresses the optimal control of a long-haul passenger train to deliver minimum-fuel operations. Contrary to the common Pontryagin minimum principle approach in railroad-related literature, this work addresses this optimal control problem with a direct method of optimization, the use of which is still marginal in this field. The implementation of a particular direct method based on the Euler collocation scheme and its transcription into a nonlinear problem are described in detail. In this paper, this optimization technique is benchmarked with well-known optimization methods in the literature, namely dynamic programming and the Pontryagin minimum principle, by simulating a real route. The results showed that the direct methods are on the same level of optimality compared with other algorithms while requiring reduced computational time and memory and being able to handle very complex dynamic systems. The performance of the direct method is also compared to the real trajectory followed by the train operator and exhibits up to 20% of fuel saving in the example route.

Study on Optimal Design of a Folding-Type Hatch Cover Considering Material Selection

2021 ◽  
pp. 1-11
Author(s):  
Gerry Liston Putra ◽  
Mitsuru Kitamura ◽  
Akihiro Takezawa
Keyword(s):  
Production Systems ◽  
Material Selection ◽  
Optimization Technique ◽  
Plate Thickness ◽  
Optimization Methods ◽  
Optimization Method ◽  
Production Costs ◽  
Plate Material ◽  
Material Type ◽  
Material Cost

Abstract Most shipyard companies maintain efficiency in all aspects of their business to survive. One of these aspects is ship production costs and their reduction. This study proposes a solution to this problem using an optimization method. A hatch cover composed of plates and stiffeners was selected as a case study. In this study, the mass and material cost of the hatch cover was optimized as an objective function using the Pareto approach with developed optimization methods. Plate thickness t, stiffener shape s, and plate material type m were selected as the design variables in this study along with some constraints. To estimate the optimal plate thickness, an expression of stress equations was Developed using an optimization technique. Furthermore, stiffener shape and plate material type selection were optimized using a genetic algorithm (GA). The results show that the optimization method is effective to decrease the mass and material cost of a hatch cover. Introduction The demand for new shipbuilding has decreased because of the effect of the economic crisis that hit almost every country in the world. Shipyard companies must think innovatively and creatively to survive under the pressure of this crisis by evaluating various studies and improvising new methods to achieve efficiency. One of the studies that has been performed examines the methods to reduce the fabrication cost of ship structures to stay profitable through the optimization of work hours, workflow production systems, and structural design.

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