Hybrid Optimization Procedure Research Articles

Pyrolysis characteristics and kinetic reaction parameters estimation of sassafras wood via thermogravimetric modeling calculation coupled with hybrid optimization methodology

The thermokinetics of sassafras wood pyrolysis were studied via thermogravimetric inverse modeling. Model-free combined with model-fitting methods were used to explore the optimal kinetics. Results indicate the obtained averaged activation energy and pre-exponential factor are 215.12 kJ/mol and 8.65 × 1017s−1, while the reaction model in charge is g(α) = [-ln(1-α)]1/3. In the following, Kissinger-based K–K method was adopted to separate the single reaction kinetics for each pseudo components. The analysis of K–K method provided the basis of the search range for each kinetic parameters. Finally, a new optimization algorithm was put forward, and the multi parallel reactions scheme incorporated into the Grey Wolf Optimization coupled with the Least Squared Fitting procedure was adopted to perform the optimization. The predicted MLR curves based on the simultaneously optimized kinetic parameters fit well with the experimental data not only at heating rates of 10, 20 and 30 K/min, but also at 5 and 40 K/min which were not used in the optimization. Such results indicated the excellent applicability of the optimized parameters and efficiency of the hybrid optimization procedure. Results from the present work could be a guidance for further biomass pyrolysis evaluation under more complex practical scenarios.

Optimization of Simultaneous Delivery and Pickup Wagon Scheme on Hybrid Siding Network of Railway Terminal

Aiming at the local freight train transit system in railway terminal, the optimization of simultaneous delivery and pickup wagon scheme on hybrid siding network is presented. The problem is formulated as a mathematical programming model. The objective function intends to minimize shunting engines’ operating cost, wagons’ travelling cost, and penalty cost for exceeding the retrieval time window. Some constraints indicating the relationship between delivery and pickup wagons, relationship between radial branches and operation batches, relationship between local wagon groups’ retrieved time and outbound train departure time, and relationship between tasks and operation batches are considered in the model. As an NP problem, using traditional method to solve the model is difficult and inefficient. A novel two-stage hybrid optimization procedure is proposed. Firstly, the three-phase approach composed of coding task sequence, dividing task batch, and generating access order sequence population is developed to generate initial solution. Secondly, an asynchronous iteration heuristic is provided. The iteration object is given as the expression form of the solution representing the delivery and pickup wagon sequence and then the initial iteration population is generated. Furthermore, the primary updating iteration population with pending and remaining groups is introduced to optimize iteration object Then, the senior updating iteration population with Lévy flight searching is also introduced to optimize further iteration object. Finally, the experimental scenarios are designed to test the proposed approach. Also, the proposed approach is compared with some other approaches and the performance of the proposed approach is evaluated by some different sized instances.

Hybrid two-step optimization of internally heat-integrated distillation columns

Heat integration is one of the most used energy conservation methods in chemical processes, and it typically involves the heat transfer from the vapor stream entering a condenser and the liquid stream entering a reboiler. Nevertheless, this work exploits the idea of heat integration between stages in which heat is supplied at a temperature higher than that at the condenser in a high-pressure column and heat is received at a temperature lower than that at the reboiler in a low-pressure column. Since stages between column are subject to heat integration, a large combinatorial problem arises. Thus, this work proposes a hybrid optimization procedure consisting of two steps: The solution of a deterministic optimization (i.e., MILP problem) and stochastic optimization (e.g., Simulated annealing) to take advantage of the strong points from each optimization technique.

Hybrid optimization procedure applied to optimal location finding for piezoelectric actuators and sensors for active vibration control

This paper presents an efficient hybrid optimization approach using a new coupling technique for solving the constrained optimization problems. This methodology is based on genetic algorithm, sequential quadratic programming and particle swarm optimization combined with a projected gradient techniques in order to correct the solutions out of domain and send them to the domain’s border. The established procedures have been successfully tested with some well known mathematical and engineering optimization problems, also the obtained results are compared with the existing approaches. It is clearly demonstrated that the solutions obtained by the proposed approach are superior to those of existing best solutions reported in the literature. The main application of this procedure is the location optimization of piezoelectric sensors and actuators for active control, the vibration of plates with some piezoelectric patches is considered. Optimization criteria ensuring good observability and controllability based on some main eigenmodes and residual ones are considered. Various rectangular piezoelectric actuators and sensors are used and two optimization variables are considered for each piezoelectric device: the location of its center and shape orientation. The applicability and effectiveness of the present methodological approach are demonstrated and the location optimization of multiple sensors and actuators are successfully obtained with some main modes and residual ones. The shape orientation optimization of sensors observing various modes as well as the local optimization of multiple sensors and actuators are numerically investigated. The effect of residual modes and the spillover reduction can be easily analyzed for a large number of modes and multiple actuators and sensors.

Path planning for minimally-invasive knee surgery using a hybrid optimization procedure

The aim of this study was to develop a procedure for medical tool path planning in minimally-invasive knee surgery. The collision-free paths for the tool were obtained using the control locations method with a hybrid optimization strategy. The tool and knee elements were described with surface meshes. The knee model allowed for bones displacement and variable incision size and location. The proposed procedure was proven to be effective in path planning for minimally-invasive surgery. It can serve as a valuable aid in surgery planning and may also be used in systems for autonomous or semi-autonomous knee surgery.

Total Internal Reflection-Based High Efficiency Grating Design for a Metal-Free Polarizing Filter Applications Using Hybrid Optimization Procedure

This paper presents a fast and rigorous design method for grating-based metal-free polarizing filter applications using two-step hybrid optimization techniques. Grating structures utilizing the total internal reflection in a lamellar configuration were used to achieve metal-free solution, which is a key technology in the chirped pulse amplification for high power laser system. Here two polarizing filters were designed: polarization sensitive and polarization insensitive. Those polarization performances were characterized by the rigorous coupled-wave analysis (RCWA), and the design parameters of grating structures, pitch, depth, and filling factor were optimized by two-step hybrid optimization procedure because the diffraction characteristics of grating-based polarizing filters are highly sensitive to small changes in design parameters. The Taguchi method is incorporated into selection process in the genetic algorithm, which indicates that the Taguchi method optimizes the design parameters in a coarse manner, and then, coarsely optimized parameters are finely optimized using the genetic algorithm. Therefore the proposed method could solve global numerical optimization problems with continuous variables. The proposed two-step hybrid optimization algorithm could effectively optimize the grating structures for the purpose of polarization filter applications, and the optimized grating structures could selectively filter the incident light up to 99.8% as to TE or TM waves.

ACCURATE COUPLING MATRIX SYNTHESIS FOR MICROWAVE FILTERS WITH RANDOM INITIAL VALUE

A hybrid optimization method that synthesizes coupling matrices for cross-coupled microwave filters is presented. This method consists of a general solvopt algorithm and fmincon algorithm, respectively. To avoid divergence from the coupling matrix, two cost functions are built, where the first one is constructed from the eigenvalues of the coupling matrix and its principal sub- matrices, while another one is dependent on the determinant of the coupling matrix and one of its cofactors. The values of non-zero elements of the coupling matrix serve as the independent variables to minimize the cost functions by using solvopt and fmincon. Although the stochastic initial values are not sufficiently close to the global optimum, the hybrid optimization procedure is still robust to find multiple coupling matrices to overcome the initial problem. It is significant that the suitable coupling matrix can be chosen from the multiple solutions to meet the given requirements in practice. For demonstrating the proposed hybrid optimization algorithm, some extraordinary prototype topologies are provided which validate the efficiency of the proposed synthesis procedure.

Hybridization of multi-objective evolutionary algorithms and artificial neural networks for optimizing the performance of electrical drives

Performance optimization of electrical drives implies a lot of degrees of freedom in the variation of design parameters, which in turn makes the process overly complex and sometimes impossible to handle for classical analytical optimization approaches. This, and the fact that multiple non-independent design parameter have to be optimized synchronously, makes a soft computing approach based on multi-objective evolutionary algorithms (MOEAs) a feasible alternative. In this paper, we describe the application of the well known Non-dominated Sorting Genetic Algorithm II (NSGA-II) in order to obtain high-quality Pareto-optimal solutions for three optimization scenarios. The nature of these scenarios requires the usage of fitness evaluation functions that rely on very time-intensive finite element (FE) simulations. The key and novel aspect of our optimization procedure is the on-the-fly automated creation of highly accurate and stable surrogate fitness functions based on artificial neural networks (ANNs). We employ these surrogate fitness functions in the middle and end parts of the NSGA-II run (→hybridization) in order to significantly reduce the very high computational effort required by the optimization process. The results show that by using this hybrid optimization procedure, the computation time of a single optimization run can be reduced by 46–72% while achieving Pareto-optimal solution sets with similar, or even slightly better, quality as those obtained when conducting NSGA-II runs that use FE simulations over the whole run-time of the optimization process.

Vibration suppression of curved beam-type structures using optimal multiple tuned mass dampers

This paper established a thorough optimization procedure of the multiple tuned-mass-damper system to suppress the vibration levels of the curved beam-type structures with multiple vibration dominant modes. A hybrid optimization methodology, which combines the global optimization method based on the Genetic Algorithm and the local optimization method based on Sequential Quadratic Programming, has been developed. The established hybrid optimization procedure is then utilized to find the optimum values of the design parameters, namely, the spring stiffness, damping factor and the position of the attached tuned-mass-damper systems, in order to suppress the vibration amplitude either at a particular mode or at several modes simultaneously.

Identification of validated multibody vehicle models for crash analysis using a hybrid optimization procedure

The design of components, or particular safety devices, for road vehicles often requires that many analyses are performed to appraise different solutions. Multibody approaches for structural crashworthiness provide alternative methodologies to the use of detailed finite element models reducing calculation time by several orders of magnitude while providing results with the necessary quality. The drawbacks on the use of multibody approaches for crashworthiness are the cumbersome model development process and difficulty of model validation. This work proposes an optimization procedure to assist multibody vehicle model development and validation. First the topological structure of the multibody system is devised representing the structural vehicle components and describing the most relevant mechanisms of deformation. The uncertainty in the model development resides on the constitutive behavior of the plastic hinges used to represent the structural deformations of the vehicle, identified by using the classic multibody approach. The constitutive relations are identified by using an optimization procedure based on the minimization of the deviation between the observed response of the vehicle model and a reference response, obtained by experimental testing or using more detailed models of the vehicle. The identification is supported by a genetic optimization algorithm that enables the characterization of several initial sets of parameters that characterize the plastic hinge constitutive relations in the vicinity of an Edgeworth-Pareto front. Afterwards, a deterministic optimization algorithm is used to identify the accurate minimum. The procedure is demonstrated by the identification of the multibody model of a large family car suitable for front impact.

ATHENA: A knowledge-based hybrid backpropagation-grammatical evolution neural network algorithm for discovering epistasis among quantitative trait Loci.

BackgroundGrowing interest and burgeoning technology for discovering genetic mechanisms that influence disease processes have ushered in a flood of genetic association studies over the last decade, yet little heritability in highly studied complex traits has been explained by genetic variation. Non-additive gene-gene interactions, which are not often explored, are thought to be one source of this "missing" heritability.MethodsStochastic methods employing evolutionary algorithms have demonstrated promise in being able to detect and model gene-gene and gene-environment interactions that influence human traits. Here we demonstrate modifications to a neural network algorithm in ATHENA (the Analysis Tool for Heritable and Environmental Network Associations) resulting in clear performance improvements for discovering gene-gene interactions that influence human traits. We employed an alternative tree-based crossover, backpropagation for locally fitting neural network weights, and incorporation of domain knowledge obtainable from publicly accessible biological databases for initializing the search for gene-gene interactions. We tested these modifications in silico using simulated datasets.ResultsWe show that the alternative tree-based crossover modification resulted in a modest increase in the sensitivity of the ATHENA algorithm for discovering gene-gene interactions. The performance increase was highly statistically significant when backpropagation was used to locally fit NN weights. We also demonstrate that using domain knowledge to initialize the search for gene-gene interactions results in a large performance increase, especially when the search space is larger than the search coverage.ConclusionsWe show that a hybrid optimization procedure, alternative crossover strategies, and incorporation of domain knowledge from publicly available biological databases can result in marked increases in sensitivity and performance of the ATHENA algorithm for detecting and modelling gene-gene interactions that influence a complex human trait.

A rewriting grammar for heat exchanger network structure evolution with stream splitting

The design of cost optimal heat exchanger networks is a difficult optimization problem owing both to the nonlinear models required and the combinatorial size of the search space. When stream splitting is considered, the combinatorial aspects make the problem even harder. This article describes the implementation of a two-level evolutionary algorithm based on a string rewriting grammar for the evolution of the heat exchanger network structure. A biological analogue of genotypes and phenotypes is used to describe structures and specific solutions, respectively. The top-level algorithm evolves structures while the lower level optimizes specific structures. The result is a hybrid optimization procedure that can identify the best structures including stream splitting. Case studies from the literature are presented to demonstrate the capabilities of the novel procedure.

The Multi-Objective Optimal Design of the Disc Type Piezoelectric Motor with Stability Constraint

A verified hybrid optimization procedure, which combines the genetic algorithm (GA) with traditional optimization methods, is presented in this paper with a disc type piezoelectric motor. The optimization objectives include minimizing the frictional loss of the rotor and maximizing the efficiency and spin speed of the piezoelectric motor. The behavior constraints include spin speed, natural frequency, and stability. The Runge-Kutta method is applied to determine the dynamic response of this motor, and the Floquet theory is employed to analyze the motor's stability. The optimization algorithm (hybrid genetic algorithm, HGA) applies GAs to provide an initial design variables set, thereby avoiding the trial process; thereafter, traditional algorithms are employed to determine optimum results. With the single-objective and multi-objective optimizations by using the HGA, both the efficiency and spin speed of the motor can be improved, and the frictional loss of the coating can be decreased effectively, either individually or simultaneously. The designs of the designated performances can be derived as well.

Generalized Regression Neural Networks With Multiple-Bandwidth Sharing and Hybrid Optimization

This paper proposes a novel algorithm for function approximation that extends the standard generalized regression neural network. Instead of a single bandwidth for all the kernels, we employ a multiple-bandwidth configuration. However, unlike previous works that use clustering of the training data for the reduction of the number of bandwidths, we propose a distinct scheme that manages a dramatic bandwidth reduction while preserving the required model complexity. In this scheme, the algorithm partitions the training patterns to groups, where all patterns within each group share the same bandwidth. Grouping relies on the analysis of the local nearest neighbor distance information around the patterns and the principal component analysis with fuzzy clustering. Furthermore, we use a hybrid optimization procedure combining a very efficient variant of the particle swarm optimizer and a quasi-Newton method for global optimization and locally optimal fine-tuning of the network bandwidths. Training is based on the minimization of a flexible adaptation of the leave-one-out validation error that enhances the network generalization. We test the proposed algorithm with real and synthetic datasets, and results show that it exhibits competitive regression performance compared to other techniques.

ISOFIT – A program for fitting sorption isotherms to experimental data

Isotherm expressions are important for describing the partitioning of contaminants in environmental systems. ISOFIT ( Isotherm Fitting Tool) is a software program that fits isotherm parameters to experimental data via the minimization of a weighted sum of squared error (WSSE) objective function. ISOFIT supports a number of isotherms, including several dual-mode isotherms that combine Freundlich, Langmuir, and Polanyi expressions with a linear partitioning term. To minimize the WSSE objective function, ISOFIT utilizes a hybrid optimization procedure that combines particle swarm optimization with Levenberg–Marquardt nonlinear regression. An initial swarm optimization step identifies promising solutions while circumventing local minima and the follow-on regression step provides local refinement and facilitates the calculation of numerous regression statistics. To demonstrate ISOFIT and evaluate its performance, the program was applied to a readily available sorption dataset and benchmarked against results generated using the MS Excel solver package.

An Effective Hybrid Approach for the Optimal Synthesis of Monopulse Antennas

The problem of synthesizing "optimal" sum and difference patterns subject to arbitrary sidelobe bounds by means of a simple feeding network is dealt with. It is shown how a hybrid optimization procedure properly acting on both clustering into subarrays and excitations of these latter greatly improves performances of known feeding architectures with respect to existing procedures. Results of the proposed technique, which significantly outperform existing methods, renew the interest of these simple feeding architectures, and emphasize the effectiveness of this kind of hybrid procedures exploiting the convexity of the problem with respect to a part of the unknowns

Liquid crystal Adaptive Optics Visual Simulator: Application to testing and design of ophthalmic optical elements

The concept of Adaptive Optics Visual Simulation applies to the use of an Adaptive Optics system to manipulate ocular aberrations in order to perform visual testing through a modified optics. It can be of interest both to study the visual system and to design new ophthalmic optical elements. In this work, we describe an apparatus based on a liquid crystal programmable phase modulator and explore its capabilities as a tool in the early stages of the design of ophthalmic optical elements with increased depth of field for presbyopic subjects. To illustrate the potential of the instrument, we analyze the performance of two phase profiles obtained by a hybrid optimization procedure. The liquid crystal Adaptive Optics Visual Simulator can be used to experimentally record the point spread function for different vergences in order to objectively measure depth of focus, to perform different psychophysical experiments through the phase profile in order to measure its impact on visual performance, and to study the interaction with the eye's particular aberrations. This approach could save several steps in current procedures of ophthalmic optical design and eventually lead to improved solutions.

Optimization of nonconventional wells under uncertainty using statistical proxies

The determination of the optimal type and placement of a nonconventional well in a heterogeneous reservoir represents a challenging optimization problem. This determination is significantly more complicated if uncertainty in the reservoir geology is included in the optimization. In this study, a genetic algorithm is applied to optimize the deployment of nonconventional wells. Geological uncertainty is accounted for by optimizing over multiple reservoir models (realizations) subject to a prescribed risk attitude. To reduce the excessive computational requirements of the base method, a new statistical proxy (which provides fast estimates of the objective function) based on cluster analysis is introduced into the optimization process. This proxy provides an estimate of the cumulative distribution function (CDF) of the scenario performance, which enables the quantification of proxy uncertainty. Knowledge of the proxy-based performance estimate in conjunction with the proxy CDF enables the systematic selection of the most appropriate scenarios for full simulation. Application of the overall method for the optimization of monobore and dual-lateral well placement demonstrates the performance of the hybrid optimization procedure. Specifically, it is shown that by simulating only 10% or 20% of the scenarios (as determined by application of the proxy), optimization results very close to those achieved by simulating all cases are obtained.

Analysis of x-ray diffraction data using a hybrid stochastic optimization method

An hybrid optimization procedure based on two stochastic algorithms, a genetic algorithm and simulated annealing, is proposed and applied to the problem of extracting a crystal configuration from grazing incidence x-ray diffraction data. Problems like this, with many parameters and very complex fitness landscapes, can be optimized by running a genetic algorithm and then refining the search with a simulated annealing procedure. A case study of finding the structure of a self-assembled monolayer of n-alkyl thiols (n = 10) on the (111) surface of gold is reported.

Efficient method for evaluation of the diffraction efficiency upper bound of diffractive phase elements

The theoretical diffraction efficiency upper limit of diffractive phase elements (DPE's) with finite apertures is investigated. A successful numerical method of evaluating the efficiency upper bound of DPE's is proposed. The method includes a hybrid optimization procedure that combines a genetic algorithm with the conjugate gradient method. This efficient global optimization technique can also be used to design DPE's. Simulation computations are detailed for rotationally symmetric beam shaping in which a Gaussian profile laser beam is converted into a uniform beam. Numerical results demonstrate that the estimated diffraction efficiency upper bound is consistent with the design results.