Search

Background Traditional optimization methods often struggle to balance global exploration and local refinement, particularly in complex real-world problems. To address this challenge, we introduce a novel hybrid optimization strategy that integrates the Nelder-Mead (NM) technique and the Genetic Algorithm (GA), named the Genetic and Nelder-Mead Algorithm (GANMA). This hybrid approach aims to enhance performance across various benchmark functions and parameter estimation tasks. Methods GANMA combines the global search capabilities of GA with the local refinement strength of NM. It is first tested on 15 benchmark functions commonly used to evaluate optimization strategies. The effectiveness of GANMA is also demonstrated through its application to parameter estimation problems, showcasing its practical utility in real-world scenarios. Results GANMA outperforms traditional optimization methods in terms of robustness, convergence speed, and solution quality. The hybrid algorithm excels across different function landscapes, including those with high dimensionality and multimodality, which are often encountered in real-world optimization issues. Additionally, GANMA improves model accuracy and interpretability in parameter estimation tasks, enhancing both model fitting and prediction. Conclusions GANMA proves to be a flexible and powerful optimization method suitable for both benchmark optimization and real-world parameter estimation challenges. Its capability to efficiently explore parameter spaces and refine solutions makes it a promising tool for scientific, engineering, and economic applications. GANMA offers a valuable solution for improving model performance and effectively handling complex optimization problems.

The Dwarf Mongoose Optimization Algorithm (DMO) is inspired by the behaviour of Dwarf Mongoose which can strike the ideal balance throughout research between exploration and exploitation. In this article, we combine algorithms of the Dwarf Mongoose Optimization Algorithm and the Nelder-Mead Algorithm (DMONM). In addition, the statistically evaluated functions is utilized by calculating the average and the standard deviation values that are used to validate the suggested algorithm's performance. The experimental results are on high-efficiency optimization functions with various dimensions. The hybrid algorithm produces good, encouraging, and better outcomes than the original algorithms. The results show that the proposed algorithm could enhance the effects of DMO when it used to solve the optimization issues of the multi-objective reliability system. [ABSTRACT FROM AUTHOR]

In this article, a new hybrid improved differential evolution and Nelder-Mead (IDE-NM) is introduced for optimizing the multi-objective machining process during the turning operation under three modes of lubrication conditions. The fitness functions are the tangential cutting force, the surface roughness, and the cutting power. Five mixed design variables are considered in the optimization procedure including the cutting speed, feed rate, and depth of cut, mode of lubrication, and the type of cutting material. The mathematical expressions of the three objectives are created based on experimental results and modeled using the artificial neural network (ANN). In the first step, the proposed method is examined by solving seven mechanical engineering design problems. The comparison results illustrate that the IDE-NM algorithm outperforms other state-of-the-art optimization methods considered in the literature. Moreover, for the turning operation problem, the results of IDE-NM are compared with those of four recent metaheuristics. The results show that the proposed method outperforms the four compared algorithms in terms of robustness, high success rate, and can provide effective solutions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao is the property of NUDT Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Wildfires pose significant threats globally, affecting human life, wildlife, and ecosystems. The detrimental effects of these fires are often exacerbated by inaccuracies in fire positioning systems and delays in alarm response, leading to rapid and uncontrolled fire spread, consequently affecting emergency response times. This paper presents a novel, real-time wildfire localization system that employs an optimized trilateration technique. The technique leverages anchor node technology for the localization process. The Nelder-Mead (NM) optimization algorithm is utilized to augment the trilateration technique, thereby enhancing the accuracy of the estimated coordinates for unidentified nodes. The proposed localization algorithm is deployed using cloud computing and the Internet of Things (IoT) MQTT communication protocol. Simulation results demonstrate that the proposed method maintains accurate localization performance, with an enhancement in fire localization accuracy of up to 83% with the optimized trilateration based on the NM algorithm. As a result, the proposed approach offers potential time-savings in the early detection of wildfires, thus contributing to more efficient emergency response measures. [ABSTRACT FROM AUTHOR]

Featured Application: The findings of this study can be applied in the design and experimental investigation of line-start permanent magnet motors. Line-start permanent magnet synchronous motors (LSPMSMs) are of great interest to researchers because of their high energy efficiency, due to the growing interest of manufacturers in energy-efficient units. However, LSPMSMs face some difficulties in starting and synchronization processes. The LSPMSM lumped parameter model is applicable to estimating the successfulness of starting and further synchronization. The parameters of such a model can be determined using computer-aided identification algorithms applied to real motor transient processes' curves. This problem demands significant computational time. A comparison between two algorithms, differential evolution and Nelder–Mead, is presented in this article. The algorithms were used for 0.55 kW, 1500 rpm LSPMSM parameter identification. Moreover, to increase computational speed, it is proposed to stop and restart the algorithms' procedures, changing their parameters after a certain number of iterations. A significant advantage of the Nelder–Mead algorithm is shown for the solving of the considered problem. [ABSTRACT FROM AUTHOR]

We propose a pioneering approach that integrates optimization algorithms and technology computer-aided design to automatically optimize laterally-diffused metal-oxide-semiconductors (LDMOS) with a field-oxide structure. We define the ratio of the square of the breakdown voltage divided by the specific on-resistance as the figure-of-merit (FOM) and the objective function of our optimization. We compare the performance of three different algorithms: Nelder-Mead, Powell, and Bayesian Optimization. We show how the LDMOS performance evolves as each of the three optimization algorithms reach their optimized structure. We show that a straightforward Nelder-Mead optimization leads to a local optimum when optimizing over six input parameters. We find that Bayesian Optimization is the most data-efficient method to find the global optimized structure in the multi-domain design space.

Currently, the lateral electric field excited resonators are used for the creation of various sensors. We have recently proposed a new type of acoustic resonator called radial electric field excited disk acoustic resonator. The advantage of this type of resonator is its high sensitivity to mechanical and electrical boundary conditions on its free surface. This makes it possible to determine both the acoustic and electrical properties of a thin layer of material deposited on the free end of the resonator. In this work, we used a radial electric field excited disk acoustic resonator of Russian-made barium plumbum zirconate titanate (BPZT) piezoceramics. With the help of this resonator, the material constants for the piezoceramic sample were refined, and their temperature dependencies were determined. Then, this resonator was used to determine the elastic modulus, viscosity, and conductivity of the chitosan acetate film in air and ammonia vapors of various concentrations. It was shown that the chitosan acetate film under the influence of ammonia vapor significantly changes its mechanical properties and increases its electrical conductivity thousands of times, and then completely restores its properties. [ABSTRACT FROM AUTHOR]

Control of multi input and multi output (MIMO) process with interaction is often encountered in process industry. Such MIMO processes are controlled using conventional sliding mode controller (SMC) and tuned by integral square error (ISE) minimizing criterion based Nelder-Mead algorithm. SMC tuned by integral time absolute error (ITAE) minimization criterion based Nelder-Mead algorithm is proposed in this work. Three categories of two inputs and two outputs (TITO) process models are represented in the matrix form, with each of the matrix element representing a first order plus dead time (FOPDT) process. These TITO models are categorized based on the ratio ε, between dead time and time constant of the FOPDT model which forms the matrix element of the TITO model. The performance of conventional SMC is evaluated for these three categories of TITO models, in which the TITO process models with the ratio ε greater than the one, exhibited by poor closed loop performance, whereas the proposed SMC when applied to the these process models delivered superior closed loop performance. [ABSTRACT FROM AUTHOR]

Summary: Fog computing allows for local data processing at the edge of the network. This allows for reduced link delays between mobile users and access points. Here, fog nodes are collocated with radio remote heads (RRHs) to provide local processing capabilities, that is, creating fog radio access network (F‐RAN) that can support ultralow latency for cellular networks that operate on millimeter wave (mmWave) bands. Here, mobile stations (MS) demand various network functions (NFs) that correspond to different service requests. Hence, it is critical to study function popularity and allow content caching at the F‐RAN. Given the limited resources at the fog nodes, it is important to efficiently manage the resources to improve network operations and enhance the capacity at reduced delays and cost. Therefore, caching in mmWave F‐RAN requires node allocation that can accommodate the highest number of cached functions. Hence, this paper proposes a novel node placement scheme that leverages Nelder–Mead meta‐heuristic. Results show that the proposed scheme yields in reduced delay, cost, and power and energy consumption. [ABSTRACT FROM AUTHOR]

This paper presents an automatic tuning method based on a stepping search algorithm for bandpass cavity filters. This method imitates human tuning manipulation without recourse to a theoretical model of filter. To a certain extent, this implementation can replace and optimize the tuning work rely on engineers' experience. The return loss and insert loss are the key indexes to measure whether the tuning terminated, and the hierarchical optimization objectives used as evaluation function of the method. To verify this approach, two screw- tuned cavity filters tuning instance are given with five screws and eleven screws respectively. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel methodology that combines the opposition Nelder–Mead algorithm and the selection phase of the genetic algorithm. This integration aims to enhance the performance of the overall algorithm. To evaluate the effectiveness of our methodology, we conducted a comprehensive comparative study involving 11 state-of-the-art algorithms renowned for their exceptional performance in the 2022 IEEE Congress on Evolutionary Computation (CEC 2022). Following rigorous analysis, which included a Friedman test and subsequent Dunn’s post hoc test, our algorithm demonstrated outstanding performance. In fact, our methodology exhibited equal or superior performance compared to the other algorithms in the majority of cases examined. These results highlight the effectiveness and competitiveness of our proposed approach, showcasing its potential to achieve state-of-the-art performance in solving optimization problems.

The classic measurement-based method for load model parameter identification relies on lots of transient simulations and optimization iterations, which is computationally intensive, and is unsuitable to the terminal devices for recording load characteristic to participate in grid edge computing. To solve this difficulty, a fast identification method for load model parameters based on jumping and steady-state points of measured data is proposed, which greatly reduces the calculation time by avoiding the transient simulations and random optimizations of the classic method, and at the same time, well retains the accuracy of identified parameters. Firstly, the method extracts four points from measured data as calculation points, i.e., the point after voltage sag, two points before and after voltage recovery, and the final steady-state point. Then, the method calculates the state variables and powers at the four points through steady-state calculation, implicit trapezoid integration method and Hermite–Simpson method respectively. Finally, according to the measured powers of the four points, the method provides an initial key load model parameters through polynomial approximation method and finds the optimal parameters through Nelder-Mead algorithm. The accuracy and practicality of the method are demonstrated by test and field case studies, and the computation burden is less than 2.5% of that in the classic method without obvious loss of identification accuracy.

Acoustic coatings with periodically arranged internal cavities have been widely applied to underwater vessels to reduce the underwater sound scattering. In this study, the simulation results from the finite element method (FEM) have been compared with the theoretical solutions based on the transfer matrix theory (TMT), and the reliability of the FEM has been verified. The Nelder-Mead algorithm has been employed to optimize the structure of the coatings and the material parameters with the sound absorption coefficient as the primary optimization objective. A function that characterizes the shape of a two-dimensional axisymmetric cavity has been proposed, and the peak value of the absorption coefficient can be successfully moved to the target frequency by changing the weighting strategy. The results show that the sound absorption coefficient of the optimized coating increases and the peak shape widens in the middle and low frequency band. The optimized axisymmetric cavity significantly improves the sound absorption performance of the anechoic coatings. The optimization algorithm of the cavity structure and material parameters proposed in this study provide an effective pathway for the optimal design of the anechoic coatings. [ABSTRACT FROM AUTHOR]

Line-start permanent magnet synchronous motors (LSPMSMs) are of great interest to researchers because of their high energy efficiency, due to the growing interest of manufacturers in energy-efficient units. However, LSPMSMs face some difficulties in starting and synchronization processes. The LSPMSM lumped parameter model is applicable to estimating the successfulness of starting and further synchronization. The parameters of such a model can be determined using computer-aided identification algorithms applied to real motor transient processes’ curves. This problem demands significant computational time. A comparison between two algorithms, differential evolution and Nelder–Mead, is presented in this article. The algorithms were used for 0.55 kW, 1500 rpm LSPMSM parameter identification. Moreover, to increase computational speed, it is proposed to stop and restart the algorithms’ procedures, changing their parameters after a certain number of iterations. A significant advantage of the Nelder–Mead algorithm is shown for the solving of the considered problem.

Although proposed more than half a century ago, the Nelder–Mead simplex search algorithm is still widely used. Four numeric constants define the operations and behavior of the algorithm. The algorithm with the original constant values performs fine on most low-dimensional, but poorly on high-dimensional, problems. Therefore, to improve its behavior in high dimensions, several adaptive schemas setting the constants according to the problem dimension were proposed in the past. In this work, we present a novel adaptive schema obtained by a meta-optimization procedure. We describe a schema candidate with eight parameters subject to meta-optimization and define an objective function evaluating the candidate's performance. The schema is optimized on up to 100-dimensional problems using the Parallel Simulated Annealing with Differential Evolution global method. The obtained global minimum represents the proposed schema. We compare the performance of the optimized schema with the existing adaptive schemas. The data profiles on the Gao–Han modified quadratic, Moré–Garbow–Hilstrom, and CUTEr (Constrained and Unconstrained Testing Environment, revisited) benchmark problem sets show that the obtained schema outperforms the existing adaptive schemas in terms of accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

This paper presents an optimal design for the soft-landing of the fully variable valve system in internal combustion engines based on Nelder-Mead algorithm and integrated simulation. The system is used to enhance the engine performance in terms of emissions and fuel economy. The electromagnetic linear actuator (EMLA) and the magnetorheological (MR) buffer are modelled with COMSOL Multiphysics, and a co-simulation platform is constructed with MATLAB/Simulink. The prototype test results are basically consistent with the integrated simulation, indicating the feasibility of the integrated simulation platform. After that, the structure of the magnetorheological buffer is optimized based on the Nelder-Mead algorithm. The optimization objective is the adjustable coefficient and the optimized parameters are the effective length, the annular gap width, the piston diameter, the groove depth and length of the coil. The adjustable coefficient of the optimized MR buffer was increased from 8.8 to 25.46. And the co-simulation results showed that the optimized system has a great improvement in soft-landing performance, with the valve seat velocity reduced from 0.51 m/s to 0.03 m/s and the bounce height reduced from 0.6 mm to nearly 0 when the lift is 8 mm. [ABSTRACT FROM AUTHOR]

In this paper the results of an experimental validation of a technique for leak detection in polymeric pipes based on the inverse transient analysis (ITA) are presented. In the proposed ITA the Nelder–Mead algorithm is used as a calibration tool. Experimental tests have been carried out in an intact and leaky high-density polyethylene (HDPE) single pipe installed at the Water Engineering Laboratory (WEL) of the University of Perugia, Italy. Transients have been generated by the fast and complete closure of a valve placed at the downstream end section of the pipe. In the first phase of the calibration procedure, the proposed algorithm has been used to estimate both the viscoelastic parameters of a generalized Kelvin–Voigt model and the unsteady-state friction coefficient, by minimizing the difference between the numerical and experimental results. In the second phase of the procedure, the calibrated model allowed the evaluation of leak size and location with an acceptable accuracy. Precisely, in terms of leak location the relative error was smaller than 5%. HIGHLIGHTS An inverse transient analysis, based on the Nelder–Mead algorithm and laboratory data, is used for viscoelastic parameter estimation of polymeric pipes.; The same approach is followed for evaluating leak location and size.; The effect of several parameters, such as the unsteady-state friction, the length in time of the pressure signal, and the location and number of the measurement sections is analyzed.;

Nonlinear regression analysis is an important statistical method widely used in many fields of science to model the complex relationships between variables. Therefore, many studies have been conducted to estimate the parameters of nonlinear regression models using various iterative techniques. In this study, an efficient hybrid algorithm, namely PSONM, by combining the exploration capability of Particle Swarm Optimization (PSO) and the exploitation capability of the Nelder-Mead (NM) algorithm is proposed to obtain parameter estimates of nonlinear regression models. To show the performance of the proposed hybrid algorithm, 20 nonlinear regression tasks with various levels of difficulty, and real data sets in the agriculture field have been tested. The experimental results indicated that the suggested hybrid algorithm provides accurate estimates, and its performance is much superior to those of NM and PSO algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a novel soft computing algorithm is designed for the numerical solution of third-order nonlinear multi-singular Emden–Fowler equation (TONMS-EFE) using the strength of universal approximation capabilities of Legendre polynomials based Legendre neural networks supported with optimization power of the Whale Optimization Algorithm (WOA) and Nelder-Mead (NM) algorithm. Unsupervised error functions are constructed in terms of mean square error for governing TONMS-EF equations of first and second order. Unknown designed parameters in LeNN structure are optimized initially by WOA for global search while NM algorithm further enhances the rapid local search convergence. The proposed algorithm’s objective is to show the accuracy and robustness in solving challenging problems like TONMS-EFE. To study our designed scheme’s performance and effectiveness, LeNN-WOA-NM is implemented on four cases of TONMS-EFE. The results obtained by the proposed algorithm are compared with the Particle Swarm Optimization (PSO) algorithm, Cuckoo search algorithm (CSA), and WOA. Extensive graphical and statistical analysis for fitness value, absolute errors, and performance indicators in terms of mean, median, and standard deviations show the proposed algorithm’s efficiency and accuracy.

• Development of comprehensive 3D CFD model for commercial hot wall reactor. • Understanding of the critical parameters affecting the film performance. • Correlation of growth patterns and operating conditions using support vector machine. • Optimal design using machine learning combined with Nelder-Mead algorithms. Chemical vapour deposition (CVD) of pyrocarbon (PyC) effectively fabricates advanced carbon materials. Controlling the nanotexture of PyC is critical for the desired application. Reactor operating conditions, including temperature, pressure, inlet flow rate, reactant concentration, govern thickness, and film uniformity. The optimal film performance can be obtained by selecting appropriate process conditions. However, the optimisation of the CVD reactor is challenging due to the highly nonlinear and multi-variable nature of the process. In this study, the support vector machine, a robust supervised learning algorithm and Nelder-Mead algorithm are coupled to optimise the CH 4 pyrolysis in a commercial CVD reactor. To this end, a comprehensive CFD model for CH 4 pyrolysis in a commercial CVD reactor is first constructed. The model accuracy is improved by considering temperature-dependent transport properties of gas mixture and incorporating the detailed gas and surface chemistry (14 species and 32 reactions). The deposition rate and film uniformity are then obtained using a parametric study. Subsequently, the support vector machine (SVM) is employed to deduce the correlation between the PyC growth rate/film uniformity and operating variables. It has been found that the accuracy of SVM is better than the linear regression model. Finally, SVM coupled with the Nelder-Mead algorithm is proposed to optimise the CVD process parameters to maximise the PyC film quality. [ABSTRACT FROM AUTHOR]

In order to reduce the wave-making resistance of ships, this paper proposes an optimization design method for bulbous bow form based on potential flow theory. In this paper, we restrict our interest to parametric deformation of the bow, at the same time constrain the displacement and the length of the ship. Based on Rankine source method, the wave-making resistances of DTMB5512 and ITTC Ship A-2 are calculated. For optimization, Sobol algorithm is selected to create sample space; based on it, we use Nelder-Mead algorithm to obtain the optimal hull form in the space. Additionally, we compare and analyze the hydrodynamic performance of the ship. The results show that the optimization design method reduces the wave-making resistance of DTMB5512 and ITTC Ship A-2, which verifies the feasibility of the method. [ABSTRACT FROM AUTHOR]

Copyright of Guangdong Architecture Civil Engineering is the property of Guangdong Architecture Civil Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Localization is considered as an important research concept for underwater acoustic sensor networks (UASNs). It performs significant role in diverse routing methods, estimating the node position and node recovery. In UASNs, localization methods have different characteristics compared with the terrestrial networks. The challenges involved in UASNs are varying water temperature and pressure, time synchronization of beacon nodes, complicated ocean currents, and positioning of nodes. To overcome these challenges, a virtual node is deployed using the Nelder–Mead algorithm with the static localization method. In this study, two types of localization methods namely static and dynamic methods are considered and a virtual node is deployed in a static localization manner. Since anchor nodes cannot communicate to the entire network for localization additionally, virtual nodes are deployed to measure the received signal strength indicator and error ratio for effective transmission. In addition "GPS node" is equipped with a ship for easy deployment without communication overhead. The simulation result justifies that static localization for an autonomous underwater sensor networks perform with better coverage rate without time synchronization and acoustic transmission overhead. [ABSTRACT FROM AUTHOR]

This paper presents an accurate sampling method for suspension-bridge finite element (FE) model updating. The proposed method utilizes a coupled suspension-bridge modeling approach, which integrates a numerical shape-finding method with conventional FE analysis to efficiently achieve parametric modeling during the sampling process. This study also employs the Nelder-Mead algorithm (NMA) to ensure the efficiency and robustness of the numerical shape-finding calculation. First, the accuracy of the NMA-based coupled modeling approach is examined by numerical investigation of the baseline model of an actual suspension bridge. Then, the computational results of two groups of samples with different sample sizes are compared to test the efficiency of the proposed method. Finally, by relying on the structural health monitoring (SHM) system of this bridge, the measured natural frequencies over half a year are identified, and almost all of them are within the calculated sample space. Therefore, the feasibility of the proposed suspension-bridge FE model sampling method is validated by the uncertainties of the measured responses over half a year. [ABSTRACT FROM AUTHOR]

This paper introduces a novel method for estimation of the parameters of the constant elasticity of variance model. To do this, the likelihood function will be constructed based on the approximate density function. Then, to estimate the parameters, some optimization algorithms will be applied. [ABSTRACT FROM AUTHOR]

Copyright of Engineering Mechanics / Gongcheng Lixue is the property of Engineering Mechanics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper aims to improve one of the recently proposed metaheuristic approaches known as Lévy flight distribution (LFD) algorithm by adopting a well-known simplex search algorithm named Nelder-Mead (NM) method. Three new strategies were utilized to demonstrate the improved capability of the original LFD algorithm. In the first strategy, NM was run twice as much the number of iterations of LFD after the latter completes its task. In the second strategy, NM was applied after each iterations of LFD instead of waiting for the completion of the latter. Lastly, in the third strategy, NM was applied after each iterations of LFD and run for the total number of current iterations of the latter algorithm. Well-known unimodal and multimodal benchmark functions were adopted, and statistical analysis was performed for performance evaluation. Further assessment was carried out through a nonparametric statistical test. The obtained results have shown the proposed versions of LFD algorithm provide significant performance improvement in general. In addition, the efficiency of the third strategy was found to be better for NM modified LFD algorithm which has greater balance between global and local search stages and can be used as an effective tool for function optimization. [ABSTRACT FROM AUTHOR]

Although proposed more than half a century ago, the Nelder–Mead simplex search algorithm is still widely used. Four numeric constants define the operations and behavior of the algorithm. The algorithm with the original constant values performs fine on most low-dimensional, but poorly on high-dimensional, problems. Therefore, to improve its behavior in high dimensions, several adaptive schemas setting the constants according to the problem dimension were proposed in the past. In this work, we present a novel adaptive schema obtained by a meta-optimization procedure. We describe a schema candidate with eight parameters subject to meta-optimization and define an objective function evaluating the candidate’s performance. The schema is optimized on up to 100-dimensional problems using the Parallel Simulated Annealing with Differential Evolution global method. The obtained global minimum represents the proposed schema. We compare the performance of the optimized schema with the existing adaptive schemas. The data profiles on the Gao–Han modified quadratic, Moré–Garbow–Hilstrom, and CUTEr (Constrained and Unconstrained Testing Environment, revisited) benchmark problem sets show that the obtained schema outperforms the existing adaptive schemas in terms of accuracy and convergence speed.

Abstract The adaptive Fourier decomposition (AFD) uses an adaptive basis instead of a fixed basis in the rational analytic function and thus achieves a fast energy convergence rate. At each decomposition level, an important step is to determine a new basis element from a dictionary to maximize the extracted energy. The existing basis searching method, however, is only the exhaustive searching method that is rather inefficient. This paper proposes four methods to accelerate the AFD algorithm based on four typical optimization techniques including the unscented Kalman filter (UKF) method, the Nelder-Mead (NM) algorithm, the genetic algorithm (GA), and the particle swarm optimization (PSO) algorithm. In the simulation of decomposing four representative signals and real ECG signals, compared with the existing exhaustive search method, the proposed schemes can achieve much higher computation speed with a fast energy convergence, that is, in particular, to make the AFD possible for real-time applications.

In this paper, a new measurement method is proposed to estimate simultaneously the complex permittivity and the complex permeability for a magneto-dielectric sample, which may contain several layers, using a X-band rectangular waveguide WR90. The Sij-parameters at the reference planes in the rectangular waveguide loaded by a material sample are measured as a function of frequency using the E8634A Vector Network Analyzer. Also, by applying the two-dimensional finite difference in time domain (2D-FDTD), the expressions for these parameters as a function of complex permittivity and complex permeability are calculated even if the sample do not cover all the section of the waveguide. The Nelder-Mead algorithm is then used to estimate the complex permittivity and complex permeability by matching the measured and calculated Sijparameters. This method has been validated by estimating, at the X-band, the complex permittivity and complex permeability of two materials such as FR4 Epoxy and Titanium Carbide powder (TiC). [ABSTRACT FROM AUTHOR]

Fluorescence kinetics of some substituted Zn- and Mg-porphyrins in solid polymeric films upon excitation by two-step-wise rectangular laser pulses and at 293 K are studied by methods of the inverse kinetic problem. The experimental nonmonotonic kinetic curves are approximated by simulated ones within the framework of a six-level energy scheme describing reversible interconversions of two complexes. The maximum correspondence between experimental and simulated curves is obtained by iterative optimization using the Nelder–Mead algorithm. Based on statistically estimated values of rate constants and parameters of the considered models, an interpretation of the observed kinetics is presented and a conclusion is drawn that the interconversions are caused by the process of reversible extraligation of the central metal ion; the process occurs in the excited triplet T1 state. [ABSTRACT FROM AUTHOR]

Introduction-- In this work large areas with sources of pollution emissions are considered, and in most cases they go far beyond industrial areas. Using modeling, monitoring or forecasting in the optimization algorithms, the pollution propagation parameters can be analyzed and ajusted with the negative impact of industrial areas. Objective-- The objective is to optimize the proposed model using three subsets (reformed without restrictions, with restrictions and not reformed) of pollution sources and their validation. Methodology-- For the minimization, the Nelder-Mead procedure of local classical optimization was used, which by changing the pitch can find the extremes quite accurate and, in addition, serves for multi-parameter optimization. Results-- The results obtained allow: the choice of industrial zones size, the location of industries with conditional sources and without restrictions, optimization limits according to the number of iterations or according to the integral of the emissions and valuation of the economic consequences of the solution. Conclusions-- The mathematical model and algorithm are quite simple in their application and are open for more complexity. [ABSTRACT FROM AUTHOR]

We consider a three-level parallelisation scheme. The second and third levels define a classical two-level parallelisation scheme and some load balancing algorithm is used to distribute tasks among processes. It is well-known that for many applications the efficiency of parallel algorithms of these two levels starts to drop down after some critical parallelisation degree is reached. This weakness of the twolevel template is addressed by introduction of one additional parallelisation level. As an alternative to the basic solver some new or modified algorithms are considered on this level. The idea of the proposed methodology is to increase the parallelisation degree by using possibly less efficient algorithms in comparison with the basic solver. As an example we investigate two modified Nelder-Mead methods. For the selected application, a Schro¨dinger equation is solved numerically on the second level, and on the third level the parallel Wang’s algorithm is used to solve systems of linear equations with tridiagonal matrices. A greedy workload balancing heuristic is proposed, which is oriented to the case of a large number of available processors. The complexity estimates of the computational tasks are model-based, i.e. they use empirical computational data.

We propose a free selective simplex for the downhill Nelder Mead simplex algorithm (1965), rather than the determinant simplex that forces its elements to perform a single operation, such as reflection. Unlike the Nelder-Mead algorithm, the elements of the proposed simplex select various operations of the algorithm to form the next simplex. In this way, we allow non-isometric reflections similar to that of the Nelder Mead, triangle simplex, but with rotation through an angle, permitting the proposed algorithm to have more control over the simplex, to change its size and direction for better performance. As a consequence, the solution that comes from the proposed simplex is always dynamic adaptive in size and orientation to different landscapes of mathematical functions. The proposed algorithm is examined in a large collection of different structures and classes of optimization problems. Additionally, comparisons are made with two enhanced, up-to-date versions of the Nelder-Mead algorithm. The numerical results show that Hassan Nelder Mead is stable due to non-dependence on the number of parameters processed. It also performs a higher accuracy for high dimensions compared with the other algorithms and a faster convergence rate toward global minima with respect to the number of simplex gradient estimates.

In spite of being one of the most popular optimization methods, Nelder–Mead's simplex search algorithm with the default choice of parameters performs poorly on high-dimensional problems. The work presented here concerns such values of the Nelder–Mead algorithm's parameters that help improve the convergence and success rate of the algorithm in high dimensions. In this work, a novel way of assigning parameters to the Nelder–Mead simplex search algorithm is proposed. The proposed scheme is based on Chebyshev spacing points and adapts itself to the dimension of the problem. The numerical experiments conducted for this study show that the proposed scheme is better not just in comparison with the original Nelder–Mead algorithm but it outperforms the other existing adaptive schemes as well. [ABSTRACT FROM AUTHOR]

We consider a three-level parallelisation scheme. The second and third levels define a classical two-level parallelisation scheme and some load balancing algorithm is used to distribute tasks among processes. It is well-known that for many applications the efficiency of parallel algorithms of these two levels starts to drop down after some critical parallelisation degree is reached. This weakness of the twolevel template is addressed by introduction of one additional parallelisation level. As an alternative to the basic solver some new or modified algorithms are considered on this level. The idea of the proposed methodology is to increase the parallelisation degree by using possibly less efficient algorithms in comparison with the basic solver. As an example we investigate two modified Nelder-Mead methods. For the selected application, a Schrödinger equation is solved numerically on the second level, and on the third level the parallel Wang's algorithm is used to solve systems of linear equations with tridiagonal matrices. A greedy workload balancing heuristic is proposed, which is oriented to the case of a large number of available processors. The complexity estimates of the computational tasks are model-based, i.e. they use empirical computational data. [ABSTRACT FROM AUTHOR]

The acoustic stealth shape design of the underwater vehicle has become a topic of considerable current interest to the experts engaging underwater acoustic countermeasure technology in recent years. During tracking the target, the echo strength of head shape is easily exposed and detected for the underwater vehicle, which directly affects its acoustic stealth performance. This paper proposes an optimal design method for the acoustic stealth shape of the underwater vehicle head with relatively lower target strength (TS), based on the Nelder–Mead optimization algorithm and finite element method. In this method, Bernstein polynomials are used to describe the underwater vehicle head shape deviation, and the optimization objective function is given as the weighting function of the TS of underwater vehicles head with different angles and frequencies. Then, the two optimized results with different situations are given, which are evaluated using the angle detecting rate. Finally, the 2D axisymmetric calculation method of the underwater acoustic scattering field is used to verify the TS calculation in this paper. The optimization design method may provide guidance for the lower TS shape design of the underwater vehicle, which may realize the automatic design of the lower TS shape with incident plane waves at given angles and given frequency bands. [ABSTRACT FROM AUTHOR]

In this paper, we present an approach for reconstruction of an impact load time history on structures by formulating the associated inverse analysis as a constraint optimization problem by minimizing the error between measured data and predicted data obtained from the forward analysis. The identification process is carried out by solving the formulated constraint optimization problem associated with the inverse analysis of impact load reconstruction. A newly developed hybrid adaptive differential search with dynamic interaction is employed for solving the complex constrained nonlinear multimodal optimization problem. The proposed variant of the differential search technique employs small multiple populations which perform the search independently and exchange the information with the dynamic neighbourhood. The adaptive features and local search ability features are built into the algorithm in order to improve the convergence characteristics of the technique. The constraint optimization formulations can considerably improve the robustness of the inverse problem associated with reconstruction of the impact load. Numerical simulation studies presented to demonstrate the effectiveness of the proposed approach indicate that it can precisely identify the spatial location of the impact load on the structure as well as the impact load time history even in the presence of measurement noise. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm using CSi Application Programming Interface (CSi API), which allows the automation of the structural optimization process for plane frames, linking powerful computational tools such as SAP2000v16 and Matlab. In addition, aspects usually ignored in modelling and structural analysis for structural optimization are included, such as the influence of cracking of concrete elements in their stiffness and the inclusion of a second order analysis. On the other hand, due to the complexity of the objective function, it was necessary to use metaheuristic optimization methods: a hybridization between genetic algorithms and the Nelder-Mead algorithm, instead of the classical methods used in previous studies, obtaining satisfactory results. The results are expressed in the form of design recommendations and they are compared with those obtained in previous investigations. [ABSTRACT FROM AUTHOR]

In this research, a new photonic crystal fiber sensor is proposed based on human brain scan images. As the human brain analyzes information in the cerebellum to make decisions, the use of this design and placement of air holes in the shape of human brain is the main reason of more electromagnetic field limitation in the center of the fiber or pseudo-cerebellum. Finite element method based on mode solver (FEM) is used to analyze this sensor. The size of the air holes and the thickness of the plasmonic layer are proposed in this design based on the Nelder-Mead optimization algorithm. The possible errors caused by the sensor fabrication are investigated and a unique equation is presented to estimate the effect of the size of the air holes on the sensor confinement loss. Numerical inference show that this brain shape sensor has an amazing maximum amplitude sensitivity (AS) of 7208 RIU−1 and appropriate maximum wavelength sensitivity (WS) of 10000 nm/RIU. Therefore, the proposed sensor has great prospects for medical, chemical and pharmaceutical measurements and diagnostic applications because of its high sensitivity, powerful detection and easy fabrication. • In this paper new photonic crystal fiber sensor is proposed based on human brain scan images. • The size of the air holes and the thickness of the plasmonic layer are proposed in this design based on the Nelder-Mead optimization algorithm. • This brain shape sensor has an amazing maximum amplitude sensitivity (AS) of 7208 RIU−1 and appropriate maximum wavelength sensitivity (WS) of 10000 nm/RIU. • Air holes in the shape of human brain is the main reason of more electromagnetic field limitation in the center of the fiber or pseudo-cerebellum. [ABSTRACT FROM AUTHOR]

Pairwise comparison matrices play a prominent role in multiple-criteria decision-making, particularly in the analytic hierarchy process (AHP). Another form of preference modeling, called an incomplete pairwise comparison matrix, is considered when one or more elements are missing. In this paper, an algorithm is proposed for the optimal completion of an incomplete matrix. Our intention is to numerically minimize a maximum eigenvalue function, which is difficult to write explicitly in terms of variables, subject to interval constraints. Numerical simulations are carried out in order to examine the performance of the algorithm. The results of our simulations show that the proposed algorithm has the ability to solve the minimization of the constrained eigenvalue problem. We provided illustrative examples to show the simplex procedures obtained by the proposed algorithm, and how well it fills in the given incomplete matrices.

Snow and ice accumulation induces hazardous driving conditions due to the effect of reduced friction on the road surface. A traditional chemical-based snow melting approach is not environmentally friendly and effective when the temperature is below − 3. 9 ∘ C. Thermal snow melting techniques, such as hydronic heating system, can be an alternative to chemical-based methods. In this paper, a new approach is proposed for sizing the hydronic snow melting system. For this purpose, a transient heat transfer mechanism is considered. A complete energy balance equation including solar radiation, snowfall, and convective heat flux is used at a bridge surface. The Nelder-Mead algorithm is used to minimize the energy consumption used for snow melting by determining an optimum relation between the inlet temperature and flow rate. Since the performance of such systems depends significantly on the weather design, sensitivity analyses were performed to analyze the sensitivity of the optimum design to variations in air temperature, snowfall rate, wind speed and insulating methods. The results showed that the insulating methods and snowfall rate are the most dominant factors influencing the energy consumption of the hydronic heating system. The energy consumption rate is expected to increase by 29% without insulating the bottom and sides of a bridge deck. A 30% increase in snowfall rate leads to a rise in energy consumption by 35%. On the other hand, heat loss due to the variation in air temperature, wind speed, and solar radiation is not significant. • Transient heat transfer mechanism was considered for bridge slab and hydronic heating system. • A simplified model was proposed to evaluate snow-melting loads based on local weather conditions. • The Nelder-Mead algorithm was applied to find optimum inlet high temperature so that overall heating demand was minimum. • The effect of weather conditions on hydronic snow melting performance was thoroughly studied. [ABSTRACT FROM AUTHOR]