Pattern Search Optimization Research Articles

Optimizing fluid–structure interaction systems with immersogeometric analysis and surrogate modeling: Application to a hydraulic arresting gear

This work describes a fluid–structure interaction (FSI) design optimization framework and applies it to improving the structural performance of a water brake used to stop aircraft landing on short runways. Inside the water brake, a dissipative torque is exerted on a rotor through interactions between rotor blades and a surrounding fluid. We seek to optimize blade shape over a parameterized design space, to prevent potentially-damaging stress concentrations without compromising performance. To avoid excessive numbers of costly simulations while exploring the design space, we use a surrogate management framework that combines derivative-free pattern search optimization with automated construction of a low-fidelity surrogate model, requiring only a handful of high-fidelity FSI simulations. We avoid the difficult problem of generating fluid and structure meshes at new points in the design space by using immersogeometric FSI analysis. The structure is analyzed isogeometrically: its design geometry also serves as a computational mesh. This geometry is then immersed in an unfitted fluid mesh that does not depend on the structure’s design parameters. We use this framework to make significant improvements to a baseline design found in the literature. Specifically, there is a 35% reduction of von Mises stress variance and a 25% reduction of maximum of stress, while the resisting torque and mass of the optimized blades remain uncompromised.

A novel MPPT design using generalized pattern search for partial shading

This paper proposes a new Maximum Power Point Tracking (MPPT) technique for photovoltaic (PV) systems especially under Partial Shading (PS). This technique is based on Generalized Pattern Search (GPS) optimization algorithm. The proposed technique adopts the GPS algorithm owing to the reason that its major features like simple implementation, derivative free nature, fast convergence and high accuracy are ideally suited to deal with PS conditions. Furthermore, these features of GPS can well resolve the limitations and drawbacks of previous MPPT techniques, such as fluctuation, low efficiency, high complexity and slow convergence. For comparison, our proposed MPPT algorithm, the Particle Swarm Optimization (PSO) algorithm and the Perturb and Observe (P&O) algorithm have been implemented in MATLAB/Simulink. The comparison results demonstrate that the proposed MPPT technique can efficiently detect the global maximum (GM) in a fewer number of voltage samples, which is nearly 4 times less than the number of voltage samples required by PSO. Moreover, the steady state efficiency of the proposed MPPT is 99.8%, which is higher than the other MPPT techniques. Therefore, the proposed technique distinguishes itself from other MPPT techniques due to its superior quality in terms of convergence speed, dynamic and steady state efficiencies and low complexity.

A general method to select representative models for decision making and optimization under uncertainty

The optimization of subsurface flow processes under geological uncertainty technically requires flow simulation to be performed over a large set of geological realizations for each function evaluation at every iteration of the optimizer. Because flow simulation over many permeability realizations (only permeability is considered to be uncertain in this study) may entail excessive computation, simulations are often performed for only a subset of ‘representative' realizations. It is however challenging to identify a representative subset that provides flow statistics in close agreement with those from the full set, especially when the decision parameters (e.g., time-varying well pressures, well locations) are unknown a priori, as they are in optimization problems. In this work, we introduce a general framework, based on clustering, for selecting a representative subset of realizations for use in simulations involving ‘new’ sets of decision parameters. Prior to clustering, each realization is represented by a low-dimensional feature vector that contains a combination of permeability-based and flow-based quantities. Calculation of flow-based features requires the specification of a (base) flow problem and simulation over the full set of realizations. Permeability information is captured concisely through use of principal component analysis. By computing the difference between the flow response for the subset and the full set, we quantify the performance of various realization-selection methods. The impact of different weightings for flow and permeability information in the cluster-based selection procedure is assessed for a range of examples involving different types of decision parameters. These decision parameters are generated either randomly, in a manner that is consistent with the solutions proposed in global stochastic optimization procedures such as GA and PSO, or through perturbation around a base case, consistent with the solutions considered in pattern search optimization. We find that flow-based clustering is preferable for problems involving new well settings (e.g., time-varying well bottom-hole pressures) or small changes in well configuration, while both permeability-based and flow-based clustering provide similar results for (new) random multiwell configurations. We also investigate the use of efficient tracer-type simulations for obtaining flow-based features and demonstrate that this treatment performs nearly as well as full-physics simulations for the cases considered. The various procedures are applied to select realizations for use in production optimization under uncertainty, which greatly accelerates the optimization computations. Optimization performance is shown to be consistent with the realization-selection results for cases involving new decision parameters.

Augmented Lagrangian Pattern Search Based Multi‐Agent Model Predictive Control of Rhine‐Meuse Delta

AbstractIn Large Scale Systems the concept of centrality fails due to the lack of centralized computing capability. The control of such systems has to be performed using multiple control agents. In this case, the matter of interactions among neighboring subsystems needs to be considered. In this paper, a water control system in the Netherlands is studied as a real large scale system. A multi‐agent scheme is applied to control the flow through the system which is decomposed into two interconnected subsystems. Each agent employs a model‐based predictive control (MPC) technique. The model of this large scale system is nonlinear and nonconvex. Therefore, an augmented Lagrangian pattern search optimization algorithm is used to implement multi‐agent MPC for this system. This proposed algorithm is applied by each control agent to solve its own interconnected optimization problem, at each subsystem of whole the water system. Simulation results show the effectiveness of the proposed approach.

Pattern search algorithm based automatic online parameter estimation for AGC with effects of wind power

The increasing portion of wind energy in the power system puts forward stability challenges to the power grid. With introduction of wind generating systems, maintaining constant frequency became one of the important problems taking stability into account. The system could be maintained with constant frequency with the help of controllers. The controller gains are usually designed with fixed values for various scenarios of power system which considerably vague due to existing system complexity. Also, the usage of conventional techniques consumes a lot of computational time and does not possesses accurate control gain parameters. To address the aforementioned challenges, an automatic online gain estimation algorithm by using pattern search optimization technique has been proposed in this article. This method computes the parameters on its own based on variable wind power output and controller gains will adjust automatically to achieve the best desired performance. The proposed method was tested in a modified IEEE 39 bus system for scenarios like system with 3% and 10% GRC (Generator Rate Constraint) with 10% and 20% SLP (Step Load Perturbation). Following that the system with proposed method was investigated with variable wind power and results demonstrate significant better results which comply with IEEE standards.

Thermodynamic and economic optimization of a solar-powered Stirling engine for micro-cogeneration purposes

Micro-cogeneration systems are a promising technology for improving the energy efficiency near the end user, allowing the optimal use of the primary energy sources and significant reductions in carbon emissions. Its use, still incipient, has a great potential for applications in the residential sector. This study aims to develop a methodology for the thermal-economic optimization of micro cogeneration units using Stirling engine as prime mover and concentrated solar energy as the heat source. The thermal-economic optimization was formulated considering the maximization of the annual worth from the system operation, subjected to the nonlinear thermodynamic and economic constraints. The physical model includes the limitations in the heat transfer processes and losses due to the pumping effects and the costing methodology was defined considering a purchase cost equation representative of each system component. Geometric and operational parameters were selected as decision variables. Numerical simulations were developed in MatLab® programming language and the Generalized Pattern Search optimization algorithm with MADSPositiveBasis2N was used in the determination of the optimal solution. A positive annual worth for the defined input simulation conditions and the economic analysis disclosed a system, economically attractive, with a payback period of approximately 10 years.

Yin-Yang-pair Optimization: A novel lightweight optimization algorithm

In this work, a new metaheuristic, Yin-Yang-Pair Optimization (YYPO), is proposed which is based on maintaining a balance between exploration and exploitation of the search space. It is a low complexity stochastic algorithm which works with two points and generates additional points depending on the number of decision variables in the optimization problem. It has three user defined parameters that provide flexibility to the users to govern its search. The performance of the proposed algorithm is evaluated on the set of problems used for the Single Objective Real Parameter Algorithm competition that was held as part of the Congress on Evolutionary Computation 2013. The results are compared with that of other traditional and recent algorithms such as Artificial Bee Colony, Ant Lion Optimizer, Differential Evolution, Grey Wolf Optimizer, Multidirectional Search, Pattern Search and Particle Swarm Optimization. Based on nonparametric statistical tests, YYPO is shown to provide highly competitive performance relative to the other algorithms while having a significantly lower time complexity. In addition, the performance of YYPO is showcased on three classical constrained engineering problems from literature.

Combining optimization methods with response spectra curve-fitting toward improved damping ratio estimation

The authors have previously shown that many traditional approaches to operational modal analysis (OMA) struggle to properly identify the modal damping ratios for bridges under traffic loading due to the interference caused by the driving frequencies of the traffic loads. This paper presents a novel methodology for modal parameter estimation in OMA that overcomes the problems presented by driving frequencies and significantly improves the damping estimates. This methodology is based on finding the power spectral density (PSD) of a given modal coordinate, and then dividing the modal PSD into separate regions, left- and right-side spectra. The modal coordinates were found using a blind source separation (BSS) algorithm and a curve-fitting technique was developed that uses optimization to find the modal parameters that best fit each side spectra of the PSD. Specifically, a pattern-search optimization method was combined with a clustering analysis algorithm and together they were employed in a series of stages in order to improve the estimates of the modal damping ratios. This method was used to estimate the damping ratios from a simulated bridge model subjected to moving traffic loads. The results of this method were compared to other established OMA methods, such as Frequency Domain Decomposition (FDD) and BSS methods, and they were found to be more accurate and more reliable, even for modes that had their PSDs distorted or altered by driving frequencies.

Vector quantisation using hybrid teaching learning and pattern search optimisation for image compression

The advantage of vector quantisation (VQ) using hybrid teaching learning-based optimisation and pattern search (hTLBO-PS) for image compression over ant colony and firefly techniques is validated. Further, the efficiency of proposed hTLBO-PS on PSNR and reconstructed image quality over other optimisation is verified. The first efficient VQ is Linde-Buzo-Gray (LBG), generates a local optimal codebook, but undergoes local optimal problem and lower PSNR values. So researchers proposed ant colony and firefly algorithm for global optimisation, but undergoes problems because of considerable tuning parameters and no such brighter firefly in the search respectively. From the literature, TLBO is good in exploitation and pattern search is good in exploration, so we hybridise the TLBO and PS based on their strengths and weakness. Finally, the performance hTLBO-PS is compared with the ACO and FA and proved better in efficient codebook design leads to higher peak signal to noise ratio and excellent reconstructed image quality. The optimised vector quantised codebook and index table are coded through run-length coding followed by Huffman coding at the transmitter section, whereas at receiver section the original image is reconstructed by decoders of Huffman and run-length.

Feature Extraction of the Alzheimer’s Disease Images Using Different Optimization Algorithms

Alzheimer’s disease (AD) is a type of dementia that causes problems with memory, thinking and behavior. The symptoms of the AD are usually developed slowly and got worse over time, till reach to severe enough stage which can’t interfere with daily tasks. This paper extract the most significant features from 3D MRI AD images using different optimization algorithms. Optimization algorithms are stochastic search methods that simulate the social behavior of species or the natural biological evolution. These algorithms had been used to get near-optimum solutions for large-scale optimization problems. This paper compares the formulation and results of five recent evolutionary optimization algorithms: Particle Swarm Optimization, Bat Algorithm, Genetic Algorithm, Pattern Search, and Simulated Annealing. A brief description of each of these five algorithms had been presented. These five optimization algorithm had been applied to two proposed AD feature extraction algorithms to get near-optimum number of features that gives higher accuracy. The comparisons among the algorithms are presented in terms of number of iteration, number of features and metric parameters. The results show that the Pattern Search optimization algorithm gives higher metric parameters values with lower number of iteration and lower number of features as compared to the other optimization algorithms.

Vector quantisation using hybrid teaching learning and pattern search optimisation for image compression

The advantage of vector quantisation (VQ) using hybrid teaching learning-based optimisation and pattern search (hTLBO-PS) for image compression over ant colony and firefly techniques is validated. Further, the efficiency of proposed hTLBO-PS on PSNR and reconstructed image quality over other optimisation is verified. The first efficient VQ is Linde-Buzo-Gray (LBG), generates a local optimal codebook, but undergoes local optimal problem and lower PSNR values. So researchers proposed ant colony and firefly algorithm for global optimisation, but undergoes problems because of considerable tuning parameters and no such brighter firefly in the search respectively. From the literature, TLBO is good in exploitation and pattern search is good in exploration, so we hybridise the TLBO and PS based on their strengths and weakness. Finally, the performance hTLBO-PS is compared with the ACO and FA and proved better in efficient codebook design leads to higher peak signal to noise ratio and excellent reconstructed image quality. The optimised vector quantised codebook and index table are coded through run-length coding followed by Huffman coding at the transmitter section, whereas at receiver section the original image is reconstructed by decoders of Huffman and run-length.

Optimization on start-up process of high-pressure rotor for large power steam turbine

This paper combines thermal-structure coupling technique and pattern search optimization algorithm to establish an optimization system for the start-up process of a turbine unit. Firstly, a finite element model for thermal-structure coupling calculation is established to accurately analyze the transient temperature field and thermal stress field, which can obtain the thermal stress distribution during start-up process. Afterwards, a program of optimization on rotor start-up process is exploited to improve the time allocation in each operating stage of start-up process, which minimizes the maximum equivalent stress of rotor. The maximum equivalent stress has reduced 25.7% after the optimization, which reveals obvious effect.

Retinal blood vessel localization approach based on bee colony swarm optimization, fuzzy c-means and pattern search

Accurate segmentation of retinal blood vessels is an important task in computer aided diagnosis and surgery planning of retinopathy. Despite the high resolution of photographs in fundus photography, the contrast between the blood vessels and retinal background tends to be poor. Furthermore, pathological changes of the retinal vessel tree can be observed in a variety of diseases such as diabetes and glaucoma. Vessels with small diameters are much liable to effects of diseases and imaging problems. In this paper, an automated retinal blood vessels segmentation approach based on two levels optimization principles is proposed. The proposed approach makes use of the artificial bee colony optimization in conjunction with fuzzy cluster compactness fitness function with partial belongness in the first level to find coarse vessels. The dependency on the vessel reflectance is problematic as the confusion with background and vessel distortions especially for thin vessels, so we made use of a second level of optimization. In the second level of optimization, pattern search is further used to enhance the segmentation results using shape description as a complementary feature. Thinness ratio is used as a fitness function for the pattern search optimization. The pattern search is a powerful tool for local search while artificial bee colony is a global search with high convergence speed. The proposed retinal blood vessels segmentation approach is tested on two publicly available databases DRIVE and STARE of retinal images. The results demonstrate that the performance of the proposed approach is comparable with state of the art techniques in terms of sensitivity, specificity and accuracy.

Statistical Analysis and Pattern Search Optimization on the Impact Properties of Talk Particulates Impregnated Sisal Fiber Reinforced Polyester Composites

This investigation is aimed at studying the effect of talk particles impregnation on the impact properties of sisal fiber reinforced polyester composites. The composite sheets were fabricated using compression molding machine by varying the curing temperature (60, 80 and 100 °C) and particulate weight content (ranging from 5 to 25 %) in the composites. The influences of curing temperature and talk weight content on the impact strength of the composites were experimentally determined and the fracture mechanisms of the composites were studied using scanning electron microscope. Statistical analyses were made on the obtained experimental values and mathematical model was developed to correlate the relationship between fabrication parameters and impact strength of the composites. Pattern search optimization technique was used to optimize the impact strength of the talk particles impregnated sisal fiber reinforced polyester composites. The values were found to be significant and the investigation resulted with effective improvement in the impact strength of the talk particles impregnated sisal fiber reinforced polyester composites.

Design and optimisation of a zeroth order resonant antenna along with experimental verification for wireless applications

In this paper, optimisation and experimental verification of a compact zeroth order resonant antenna is presented. It is observed that an antenna can be tuned to a desired working frequency by adjusting the inductive via radius. The antenna is subsequently optimised using HFSS simulation software. Pattern search optimisation is carried out to determine the optimum via radius to achieve minimum reflection coefficient with enhanced bandwidth. Using optimisation we achieved dual band backward wave with a bandwidth of about 2% at 4.28 GHz. After designing and optimisation the antenna is analysed for its performance. The proposed antenna is measured at Antenna Measurement Facility (AMF), ISRO, Ahmedabad. A good agreement between the measured and the simulated results validates the design. The simulated bandwidth at 3.695 GHz covers the IEEE 802.11 wireless local–area network (WLAN) standards. The designed compact antenna is most suitable for precise wireless applications having good radiation characteristics with wide coverage area.

Cooperation or Coordination of Underwater Glider Networks? An Assessment from Observing System Simulation Experiments in the Ligurian Sea

Abstract The coordinated and cooperative-unaware networking of glider fleets have been proposed to obtain a performance gain in ocean sampling over naïve collective behavior. Whether one of these implementations results in a more efficient sampling of the ocean variability remains an open question. This article aims at a performance evaluation of cooperative-unaware and coordinated networks of gliders to reduce the uncertainty in operational temperature model predictions. The evaluation is based on an observing system simulation experiment (OSSE) implemented in the northern Ligurian Sea (western Mediterranean) from 21 August to 1 September 2010. The OSSE confronts the forecast skills obtained by the Regional Ocean Modeling System (ROMS) when assimilating data gathered from a cooperative and unaware network of three gliders with the prediction skill obtained when data comes from a coordinated configuration. An asynchronous formulation of the ensemble Kalman filter with a 48-h window is used to assimilate simulated temperature observations. Optimum sampling strategies of the glider networks, based on a pattern search optimization algorithm, are computed for each 48-h forecasting period using a covariance integrated in time and in the vertical direction to reduce the dimensionality of the problem and to enable a rapid resolution. Perturbations of the depth-averaged current field in glider motions are neglected. Results indicate a better performance of the coordinated network configuration due to an enhanced capacity to capture an eddy structure that is responsible for the largest forecast error in the experimental domain.

Shape optimization of pulsatile ventricular assist devices using FSI to minimize thrombotic risk

In this paper we perform shape optimization of a pediatric pulsatile ventricular assist device (PVAD). The device simulation is carried out using fluid---structure interaction (FSI) modeling techniques within a computational framework that combines FEM for fluid mechanics and isogeometric analysis for structural mechanics modeling. The PVAD FSI simulations are performed under realistic conditions (i.e., flow speeds, pressure levels, boundary conditions, etc.), and account for the interaction of air, blood, and a thin structural membrane separating the two fluid subdomains. The shape optimization study is designed to reduce thrombotic risk, a major clinical problem in PVADs. Thrombotic risk is quantified in terms of particle residence time in the device blood chamber. Methods to compute particle residence time in the context of moving spatial domains are presented in a companion paper published in the same issue (Comput Mech, doi: 10.1007/s00466-013-0931-y , 2013). The surrogate management framework, a derivative-free pattern search optimization method that relies on surrogates for increased efficiency, is employed in this work. For the optimization study shown here, particle residence time is used to define a suitable cost or objective function, while four adjustable design optimization parameters are used to define the device geometry. The FSI-based optimization framework is implemented in a parallel computing environment, and deployed with minimal user intervention. Using five SEARCH/POLL steps the optimization scheme identifies a PVAD design with significantly better throughput efficiency than the original device.

Developing a hybrid genetic algorithm solution to optimise multi-echelon supply chains

The design of dynamic supply chain (DSC) models has received a great attention in the last two decades. In this paper, a forward supply chain network (SCN) model is developed in which both dynamic facility locations and dynamic distributed quantities of materials and products are assumed. The problem is formulated mathematically using mixed integer linear programming (MILP). The solution methodology adopted is the hybrid genetic algorithm (hGA) comprising genetic algorithm (GA) and pattern search (PS) optimisation techniques. Two experimental studies are conducted to analyse the developed SCN model and the developed hGA. The results of the hGA analysis show that the accuracy of the proposed hGA is improved in the cases of large number of nodes of the SC's echelons, small number of manufactured products and large quantities of products' demands.

Predictive pre-cooling of thermo-active building systems with low-lift chillers

This article describes the development and experimental validation of a data-driven model predictive control algorithm that optimizes the operation of a low-lift chiller, a variable-capacity chiller run at low pressure ratios, serving a single zone with a thermo-active building system. The predictive control algorithm incorporates new elements lacking in previous chiller pre-cooling control optimization methods, including a model of temperature and load-dependent chiller performance extending to low-pressure and part-load ratios and a data-driven zone temperature response model that accounts for the transient thermal response of a concrete-core radiant floor thermo-active building system. Data-driven models of zone and concrete-core thermal response are identified from monitored zone temperature and thermal load data and combined with an empirical model of a low-lift chiller to implement model predictive control. The energy consumption of the cooling system, including the chiller compressor, condenser fan, and chilled-water pump energy, is minimized over a 24-h look-ahead moving horizon using the thermo-active building system for thermal storage and radiant distribution. A generalized pattern-search optimization over compressor speed is performed to identify optimal chiller control schedules at every hour, thereby accomplishing load shifting, efficient part-load operation, and cooling energy savings. Results from testing the system's sensible cooling efficiency in an experimental test chamber subject to the typical summer week of two climates, Atlanta, GA, and Phoenix, AZ, show sensible cooling energy savings of 25% and 19%, respectively, relative to a high efficiency, variable-speed split-system air conditioner.

Optimal extraction of solar cell parameters using pattern search

Abstract This paper presents an application of pattern search optimization technique for extracting the parameters of different solar cell models. These models are single diode, double diode, and photovoltaic module. The solar cell parameters estimation is viewed and formulated as a multivariate non-linear optimization problem. The proposed technique is used to solve a transcendental function that governs the current–voltage relationship of a solar cell, as no direct general analytical solution exists. Several cases were investigated to test and validate the consistency of accurately estimating various parameters of different solar cell models. Comparison among different parameter estimation techniques is presented to show the effectiveness of the developed approach. Moreover, error and statistical analyses are carried out to measure the accuracy of the estimated parameters and model suitability.