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1 – 10 of 68Tarik Kucukdeniz and Sakir Esnaf
The purpose of this paper is to propose hybrid revised weighted fuzzy c-means (RWFCM) clustering and Nelder–Mead (NM) simplex algorithm, called as RWFCM-NM, for generalized…
Abstract
Purpose
The purpose of this paper is to propose hybrid revised weighted fuzzy c-means (RWFCM) clustering and Nelder–Mead (NM) simplex algorithm, called as RWFCM-NM, for generalized multisource Weber problem (MWP).
Design/methodology/approach
Although the RWFCM claims that there is no obligation to sequentially use different methods together, NM’s local search advantage is investigated and performance of the proposed hybrid algorithm for generalized MWP is tested on well-known research data sets.
Findings
Test results state the outstanding performance of new hybrid RWFCM and NM simplex algorithm in terms of cost minimization and CPU times.
Originality/value
Proposed approach achieves better results in continuous facility location problems.
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Bourahla Kheireddine, Belli Zoubida and Hacib Tarik
This study aims to improve the bat algorithm (BA) performance for solving optimization problems in electrical engineering.
Abstract
Purpose
This study aims to improve the bat algorithm (BA) performance for solving optimization problems in electrical engineering.
Design/methodology/approach
For this task, two strategies were investigated. The first one is based on including the crossover technique into classical BA, in the same manner as in the genetic algorithm method. Therefore, the newly generated version of BA is called the crossover–bat algorithm (C-BA). In the second strategy, a hybridization of the BA with the Nelder–Mead (NM) simplex method was performed; it gives the NM-BA algorithm.
Findings
First, the proposed strategies were applied to solve a set of two standard benchmark problems; then, they were applied to solve the TEAM workshop problem 25, where an electromagnetic field was computed by use of the 2D non-linear finite element method. Both optimization algorithms and finite element computation tool were implemented under MATLAB.
Originality/value
The two proposed optimization strategies, C-BA and NM-BA, have allowed good improvements of classical BA, generally known for its poor solution quality and slow convergence rate.
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Brijesh Upadhaya, Paavo Rasilo, Lauri Perkkiö, Paul Handgruber, Anouar Belahcen and Antero Arkkio
Improperly fitted parameters for the Jiles–Atherton (JA) hysteresis model can lead to non-physical hysteresis loops when ferromagnetic materials are simulated. This can be…
Abstract
Purpose
Improperly fitted parameters for the Jiles–Atherton (JA) hysteresis model can lead to non-physical hysteresis loops when ferromagnetic materials are simulated. This can be remedied by including a proper physical constraint in the parameter-fitting optimization algorithm. This paper aims to implement the constraint in the meta-heuristic simulated annealing (SA) optimization and Nelder–Mead simplex (NMS) algorithms to find JA model parameters that yield a physical hysteresis loop. The quasi-static B(H)-characteristics of a non-oriented (NO) silicon steel sheet are simulated, using existing measurements from a single sheet tester. Hysteresis loops received from the JA model under modified logistic function and piecewise cubic spline fitted to the average M(H) curve are compared against the measured minor and major hysteresis loops.
Design/methodology/approach
A physical constraint takes into account the anhysteretic susceptibility at the origin. This helps in the optimization decision-making, whether to accept or reject randomly generated parameters at a given iteration step. A combination of global and local heuristic optimization methods is used to determine the parameters of the JA hysteresis model. First, the SA method is applied and after that the NMS method is used in the process.
Findings
The implementation of a physical constraint improves the robustness of the parameter fitting and leads to more physical hysteresis loops. Modeling the anhysteretic magnetization by a spline fitted to the average of a measured major hysteresis loop provides a significantly better fit with the data than using analytical functions for the purpose. The results show that a modified logistic function can be considered a suitable anhysteretic (analytical) function for the NO silicon steel used in this paper. At high magnitude excitations, the average M(H) curve yields the proper fitting with the measured hysteresis loop. However, the parameters valid for the major hysteresis loop do not produce proper fitting for minor hysteresis loops.
Originality/value
The physical constraint is added in the SA and NMS optimization algorithms. The optimization algorithms are taken from the GNU Scientific Library, which is available from the GNU project. The methods described in this paper can be applied to estimate the physical parameters of the JA hysteresis model, particularly for the unidirectional alternating B(H) characteristics of NO silicon steel.
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Ritu Arora, Anubhav Pratap Singh, Renu Sharma and Anand Chauhan
The awareness for protecting the environment has resulted in remanufacturing and recycling policies in manufacturing industries. Carbon emission is one of the most important…
Abstract
Purpose
The awareness for protecting the environment has resulted in remanufacturing and recycling policies in manufacturing industries. Carbon emission is one of the most important elements affecting the environment. Carbon emission due to production and transportation creates complicated situations for the manufacturing firms by affecting the manufacturer's carbon quota. The ecological consequences posed in a reverse logistic model are the subject of this study.
Design/methodology/approach
The present study explores the fuzzy model of economic production for both remanufacturing and recycling with uncertain cost parameters under the cap-and-trade rule to control the carbon emission due to different modes of transportation. Due to imprecise cost parameters, the hexagonal fuzzy numbers are set to fuzzify the overall cost, which leads to correct decisions in a more confident way. The result is defuzzified by using graded mean integration.
Findings
This study offers an explicit condition to control the carbon emission of the manufacturer and reduce the optimum cost. The findings indicate that the collection of used goods that can be remanufactured must be increased. The model is validated numerically. Sensitivity analysis explores the various aspects of different parameters on net cost to accomplish the fuzzy production model.
Originality/value
Under fuzzy inference, the research offers a relevant contribution in the field of recycling with controlling carbon emission by using the cap-and-trade policy. This study provides a trading strategy for a manufacturer's decision to avoid losses.
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Alivarani Mohapatra, Byamakesh Nayak and Kanungo Barada Mohanty
This paper aims to propose a simple, derivative-free novel method named as Nelder–Mead optimization algorithm to estimate the unknown parameters of the photovoltaic (PV) module…
Abstract
Purpose
This paper aims to propose a simple, derivative-free novel method named as Nelder–Mead optimization algorithm to estimate the unknown parameters of the photovoltaic (PV) module considering the environmental conditions.
Design/methodology/approach
At a particular temperature and irradiation, experimental current-voltage (I-V) and power-voltage (P-V) characteristics are drawn and considered as a reference model. The PV system model with unknown model parameters is considered as the adaptive model whose unknown model parameters are to be adapted so that the simulated characteristics closely matches with the experimental characteristics. A single diode (Rsh) model with five unknown model parameters is considered here for the parameter estimation.
Findings
The key advantages of this method are that parameters are estimated considering environmental conditions. Experimental characteristics are considered for parameter estimation which gives accurate results. Parameters are estimated considering both I-V and P-V curves as most of the applications demand extraction of the actual power from the PV module.
Originality/value
The proposed model is compared with other three well-known models available in the literature considering various statistical errors. The results show the superiority of the proposed model with a minimum error for both I-V and P-V characteristics.
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Michele Forzan, Sergio Lupi and Ezio Toffano
The purpose of this paper is to present a calculation optimization method that is able to achieve the best induced power profile (and subsequent temperature distribution) in a…
Abstract
Purpose
The purpose of this paper is to present a calculation optimization method that is able to achieve the best induced power profile (and subsequent temperature distribution) in a disk or billet workpiece processed by induction heating.
Design/methodology/approach
A volume integral method, also known as the mutually coupled circuits method, is implemented in MatLab® environment to solve axial‐symmetrical induction systems. It is completed with an optimization procedure based on Nelder‐Mead simplex algorithm, with the goal of obtaining a specified distribution of the induced power in the load. In this way, it is possible to predict current amplitudes for implementing the so‐called “zone controlled induction heating” (ZCIH) process.
Findings
Some examples of calculation results are given, both for disc and billet loads. By the excitation of the inductor coils with a set of currents of appropriate amplitude and phase values, it is possible to achieve an optimized profile of induced power distributions.
Originality/value
This paper validates a method to predict currents and phases in a load‐inductor ZCIH system, confirming the possibility of obtaining specified induced power density distributions, according to the process requirements, e.g. for compensation of the load edge‐effect.
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Sławomir Stępień and Jakub Bernat
The purpose of this paper is to present a method of modeling the variable reluctance stepper motor using the time‐stepping finite element technique. The proposed model is used to…
Abstract
Purpose
The purpose of this paper is to present a method of modeling the variable reluctance stepper motor using the time‐stepping finite element technique. The proposed model is used to minimize the step response overshoots considering the stator and rotor tooth geometry.
Design/methodology/approach
A strongly coupled field‐circuit model considering magnetic nonlinearity of the stepper motor is presented. As the main contribution, the Nelder‐Mead method of the motor geometry optimization that minimize the step response overshoots and positioning error is proposed.
Findings
The proposed method can be applied to obtain the optimal tooth/pole geometry of the stepper motor which is efficient to perform the possibly accurate positioning.
Originality/value
The paper examines the application of the presented optimization method to minimize the positioning error of the four‐phased variable reluctance stepper motor.
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Ney Rafael Secco and Bento Silva de Mattos
Multidisciplinary design frameworks elaborated for aeronautical applications require considerable computational power that grows enormously with the utilization of higher fidelity…
Abstract
Purpose
Multidisciplinary design frameworks elaborated for aeronautical applications require considerable computational power that grows enormously with the utilization of higher fidelity tools to model aeronautical disciplines like aerodynamics, loads, flight dynamics, performance, structural analysis and others. Surrogate models are a good alternative to address properly and elegantly this issue. With regard to this issue, the purpose of this paper is the design and application of an artificial neural network to predict aerodynamic coefficients of transport airplanes. The neural network must be fed with calculations from computational fluid dynamic codes. The artificial neural network system that was then developed can predict lift and drag coefficients for wing-fuselage configurations with high accuracy. The input parameters for the neural network are the wing planform, airfoil geometry and flight condition. An aerodynamic database consisting of approximately 100,000 cases calculated with a full-potential code with computation of viscous effects was used for the neural network training, which is carried out with the back-propagation algorithm, the scaled gradient algorithm and the Nguyen–Wridow weight initialization. Networks with different numbers of neurons were evaluated to minimize the regression error. The neural network featuring the lowest regression error is able to reduce the computation time of the aerodynamic coefficients 4,000 times when compared with the computing time required by the full potential code. Regarding the drag coefficient, the average error of the neural network is of five drag counts only. The computation of the gradients of the neural network outputs in a scalable manner is possible by an adaptation of back-propagation algorithm. This enabled its use in an adjoint method, elaborated by the authors and used for an airplane optimization task. The results from that optimization were compared with similar tasks performed by calling the full potential code in another optimization application. The resulting geometry obtained with the aerodynamic coefficient predicted by the neural network is practically the same of that designed directly by the call of the full potential code.
Design/methodology/approach
The aerodynamic database required for the neural network training was generated with a full-potential multiblock-structured code. The training process used the back-propagation algorithm, the scaled-conjugate gradient algorithm and the Nguyen–Wridow weight initialization. Networks with different numbers of neurons were evaluated to minimize the regression error.
Findings
A suitable and efficient methodology to model aerodynamic coefficients based on artificial neural networks was obtained. This work also suggests appropriate sizes of artificial neural networks for this specific application. We demonstrated that these metamodels for airplane optimization tasks can be used without loss of fidelity and with great accuracy, as their local minima might be relatively close to the minima of the original design space defined by the call of computational fluid dynamics codes.
Research limitations/implications
The present work demonstrated the ability of a metamodel with artificial neural networks to capture the physics of transonic and subsonic flow over a wing-fuselage combination. The formulation that was used was the full potential equation. However, the present methodology can be extended to model more complex formulations such as the Euler and Navier–Stokes ones.
Practical implications
Optimum networks reduced the computation time for aerodynamic coefficient calculations by 4,000 times when compared with the full-potential code. The average absolute errors obtained were of 0.004 and 0.0005 for lift and drag coefficient prediction, respectively. Airplane configurations can be evaluated more quickly.
Social implications
If multidisciplinary optimization tasks for airplane design become more efficient, this means that more efficient airplanes (for instance less polluting airplanes) can be designed. This leads to a more sustainable aviation.
Originality/value
This research started in 2005 with a master thesis. It was steadily improved with more efficient artificial neural networks able to handle more complex airplane geometries. There is a single work using similar techniques found in a conference paper published in 2007. However, that paper focused on the application, i.e. providing very few details of the methodology to model aerodynamic coefficients.
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Jane Chu, Sarah Engelbrecht, Gregory Graf and David W. Rosen
The purpose of this paper is to investigate design synthesis methods for designing lattice cellular structures to achieve desired stiffnesses. More generally, to find appropriate…
Abstract
Purpose
The purpose of this paper is to investigate design synthesis methods for designing lattice cellular structures to achieve desired stiffnesses. More generally, to find appropriate design problem formulations and solution algorithms for searching the large, complex design spaces associated with cellular structures.
Design/methodology/approach
Two optimization algorithms were tested: particle swarm optimization (PSO) and Levenburg‐Marquardt (LM), based on a least‐squares minimization formulation. Two example problems of limited complexity, specifically a two‐dimensional cantilever beam and a two‐dimensional simply‐supported plate, were investigated. Computational characteristics of the algorithms were reported for design problems with hundreds of variables. Constraints from additive manufacturing processes were incorporated to ensure that resulting designs are realizable.
Findings
Both PSO and LM succeeded in searching the design spaces and finding good designs. LM is one to two orders of magnitude more efficient for this class of problems.
Research limitations/implications
Three‐dimensional problems are not investigated in this paper.
Practical implications
LM appears to be a viable algorithm for optimizing structures of complex geometry for minimum weight and desired stiffness.
Originality/value
The testing of design synthesis methods (problem formulations and algorithms) for lattice cellular structures, and the testing of PSO and LM algorithms, are of particular value.
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Farid Shahmiri, Maryam Sargolzehi and Mohammad Ali Shahi Ashtiani
The effects of rotor blade design variables and their mutual interactions on aerodynamic efficiency of helicopters are investigated. The aerodynamic efficiency is defined based on…
Abstract
Purpose
The effects of rotor blade design variables and their mutual interactions on aerodynamic efficiency of helicopters are investigated. The aerodynamic efficiency is defined based on figure of merit (FM) and lift-to-drag responses developed for hover and forward flight, respectively.
Design/methodology/approach
The approach is to couple a general flight dynamic simulation code, previously validated in the time domain, with design of experiment (DOE) required for the response surface development. DOE includes I-optimality criteria to preselect the data and improve data acquisition process. Desirability approach is also implemented for a better understanding of the optimum rotor blade planform in both hover and forward flight.
Findings
The resulting system provides a systematic manner to examine the rotor blade design variables and their interactions, thus reducing the time and cost of designing rotor blades. The obtained results show that the blade taper ratio of 0.3, the point of taper initiation of about 0.64 R within a SC1095R8 airfoil satisfy the maximum FM of 0.73 and the maximum lift-to-drag ratio of about 5.5 in hover and forward flight.
Practical implications
The work shows the practical possibility to implement the proposed optimization process that can be used for the advanced rotor blade design.
Originality/value
The work presents the rapid and reliable optimization process efficiently used for designing advanced rotor blades in hover and forward flight.
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