Advanced Optimization Methods Research Articles

Optimizing selective laser sintering process by grey relational analysis and soft computing techniques

Selective laser sintering (SLS) is a novel fabrication technique with multiple industrial applications in different industrial sectors. Choosing optimum combination of elements which lead to the best component properties and lower process cost are required in the SLS process. In this study, we focused on advanced modeling and optimization method developed for obtaining the best mechanical properties of SLS produced glass filled polyamide parts. The key processing parameters examined were part bed temperature, laser power, scan speed, scan spacing, and scan length. Response output properties measured were elongation and ultimate tensile strength. Five factors with three levels according to the central composite design were trailed. Adaptive neuro-fuzzy inference system (ANFIS) was employed to generate a mapping relationship between the process factors and the experimentally observed responses. In order to achieve best mechanical characteristics, the acquired model was used by simulated annealing algorithm as an objective function. Grey relational analysis (GRA) as a multi-response optimization technique was also applied to evaluate which modeling technique could perform best for defining the process elements to obtain the highest mechanical properties. In comparing the two optimization methods, the results indicated that the ANFIS-SA system outperformed the GRA in finding optimal solutions for the SLS process applied for glass fiber reinforced part production.

GPU parallel neural hierarchical multi objective solver for burst routing and wavelength assignment

Optical Burst Switching (OBS) is a promising technology for next generation of Transparent Optical Networks (TON). However, many scientific challenges remain to be overcome such as the problem of Burst Routing and Wavelength Assignment (BRWA) with several conflicting objectives and constraints. In this paper, we first formulate the BRWA as a Multi Objective Integer Linear Programming (MO-ILP) optimization problem.In the formulated problem, the proposed BRWA policy will satisfy several constraints in order to guarantee a high-speed management of processes, required by the transparent optical traffic. Then, since the obtained ILP problem contains a large number of optical constraints and conflicting objectives, we propose to use an exact parallel Neural Hierarchical (epNH) MO-ILP solution with Graphics Processing Unit (GPU) parallel implementation using Compute Unified Device Architecture (CUDA). This also allows doing a concurrent search for multiple solutions, reducing processing cost, making hybrid interfaces to other search techniques, and achieving better overall effectiveness.In addition, our architecture based on Artificial Neural Networks (ANN) allows flexibility and scalability. The processing time remains fixed regardless of the input size. Our BRWA GPU-based epNH MO-ILP solver is based on the joint use of advanced MO-ILP optimization methods, ANN large-scale inherent parallelism and CUDA-GPU High-Performance Computing (HPC) architecture.

Mixture Design of NiCoMo Ternary Alloy Nanoparticles Assembled on Graphene Nanosheets and Decorated with Ru Nanoparticles: A Pt/C-like Activity for Hydrogen Evolution Reaction

The development of nanocomposites with high activity and stability to generate hydrogen as a green fuel is an interesting challenge for several industrial requirements. Here, ternary alloy nanoparticles of NiCoMo are fabricated electrochemically on graphene nanosheets (GNs) as new nanocomposites using a glassy carbon electrode (GC) base, and then the prepared electrode systems are decorated with ruthenium nanoparticles (Ru NPs). The electrodes are characterized by several surface analysis techniques. The electrocatalytic activity of the electrodes is monitored toward the hydrogen evolution reaction (HER) in alkaline solutions, and the fabrication conditions are optimized systematically based on advanced optimization methods such as central composite design (CCD) and simplex-lattice mixture design (SLMD). Initially, the electrodeposition bath parameters are investigated and optimized by CCD. Then, the SLMD analysis is utilized to obtain the optimal mole ratio of metal ions using HER kinetic data. The resul...

Mechanisms Controlling Global Mean Sea Surface Temperature Determined From a State Estimate

AbstractGlobal mean sea surface temperature ( ) is a variable of primary interest in studies of climate variability and change. The temporal evolution of can be influenced by surface heat fluxes ( ) and by diffusion ( ) and advection ( ) processes internal to the ocean, but quantifying the contribution of these different factors from data alone is prone to substantial uncertainties. Here we derive a closed budget for the period 1993–2015 based on a global ocean state estimate, which is an exact solution of a general circulation model constrained to most extant ocean observations through advanced optimization methods. The estimated average temperature of the top (10‐m thick) level in the model, taken to represent , shows relatively small variability at most time scales compared to , , or , reflecting the tendency for largely balancing effects from all the latter terms. The seasonal cycle in is mostly determined by small imbalances between and , with negligible contributions from . While seems to simply damp at the annual period, a different dynamical role for at semiannual period is suggested by it being larger than . At periods longer than annual, contributes importantly to variability, pointing to the direct influence of the variable ocean circulation on and mean surface climate.

Random Barzilai–Borwein step size for mini-batch algorithms

Mini-batch algorithms, a well-studied, highly popular approach in stochastic optimization methods, are used by practitioners because of their ability to accelerate training through better use of parallel processing power and reduction of stochastic variance. However, mini-batch algorithms often employ either a diminishing step size or a tuning step size by hand, which, in practice, can be time consuming. In this paper, we propose using the improved Barzilai–Borwein (BB) method to automatically compute step sizes for the state of the art mini-batch algorithm (mini-batch semi-stochastic gradient descent (mS2GD) method), which leads to a new algorithm: mS2GD-RBB. We theoretically prove that mS2GD-RBB converges with a linear convergence rate for strongly convex objective functions. To further validate the efficacy and scalability of the improved BB method, we introduce it into another modern mini-batch algorithm, Accelerated Mini-Batch Prox SVRG (Acc-Prox-SVRG) method. In a machine learning context, numerical experiments on three benchmark data sets indicate that the proposed methods outperform some advanced stochastic optimization methods.

Experimental Investigation and Parameter Optimization of Al2O3-40% TiO2 Atmospheric Plasma Spray Coating on SS316 Steel Substrate

SS316 steel is a commercial stainless steel used in various applications. Ceramic coating of the wear areas of SS316 steel enhances the MTBF. The ultimate quality of the coating depends mainly on the coating thickness, surface roughness, coating microhardness, abrasion rate and coating porosity. Study depicts an experimental investigation to optimize the Atmospheric Plasma Spray process input parameters of Al2O3-40% TiO2 ceramic coatings to get the best quality of coating on SS316 steel substrate. The experiments are conducted with a three-level L18 Orthogonal Array (OA) design of experiments (DoE). Critical input parameters considered are: nozzle distance, substrate speed, arc current, carrier gas flow and coating powder flow rate and output parameters considered are: coating thickness, surface roughness, coating microhardness, abrasion rate, and% of porosity. Mathematical models are generated for individual output parameters. The Analytic Hierarchy Process (AHP) is applied to generate weights for individual output parameters considering them as objective functions and a combined objective function is generated. An advanced optimization method, Teaching-Learning-Based Optimization algorithm (TLBO), is applied to the combined objective function to optimize the values of input parameters to get the best output parameters. The influence of spray input parameters on output parameters are studied in detail.

GPU Neural Mutli Objective Solver for Optical Burst Grooming

Transparent Optical Networks has been known as a potential solution for high speed flexible optical backbone networks since a long time ago. However, many scientific challenges remain to be overcome such as the problem of Optical Burst Grooming (OBG) with several conflicting objectives and constraints.OBG is a fundamental problem in the engineering, control, and management of optical traffic networks, and arises in most network design applications, including optical burst routing, survivability design, and traffic scheduling. The traffic grooming problem is to coalesce several high and low speed sub bursts close in time together to form a larger burst that will be switched as one unit.In this paper, we first formulate OBG as a Multi Objective Integer Linear Programming (MO-ILP) optimization problem. Then we propose to use a parallel and hierarchical solver based on Artificial Neural Networks (ANN) with Graphics Processing Unit (GPU) parallel implementation using Compute Unified Device Architecture (CUDA). The processing time remains fixed regardless of the input size and the number of optical constraints and conflicting objectives.Our OBG solver benefits of the joint use of advanced ILP-MO optimization methods, ANN large-scale inherent parallelism and CUDA-GPU High-Performance Computing (HPC) architecture. Through a comprehensive simulation study, we show that, our proposed grooming approach can significantly improve the optical traffic performance, resulting in less contention and low burst lost probability.

Optimal Placement and Sizing of Renewable Distributed Generations and Capacitor Banks into Radial Distribution Systems

In recent years, renewable types of distributed generation in the distribution system have been much appreciated due to their enormous technical and environmental advantages. This paper proposes a methodology for optimal placement and sizing of renewable distributed generation(s) (i.e., wind, solar and biomass) and capacitor banks into a radial distribution system. The intermittency of wind speed and solar irradiance are handled with multi-state modeling using suitable probability distribution functions. The three objective functions, i.e., power loss reduction, voltage stability improvement, and voltage deviation minimization are optimized using advanced Pareto-front non-dominated sorting multi-objective particle swarm optimization method. First a set of non-dominated Pareto-front data are called from the algorithm. Later, a fuzzy decision technique is applied to extract the trade-off solution set. The effectiveness of the proposed methodology is tested on the standard IEEE 33 test system. The overall results reveal that combination of renewable distributed generations and capacitor banks are dominant in power loss reduction, voltage stability and voltage profile improvement.

BIM-based PV system optimization and deployment

Distributed PV systems have been widely deployed on building rooftops or facade in more complex environments thanks to their low-carbon footprint and commercial value. As a fact, the shapes and locations of building exteriors as well as their surrounding obstacles highly impact the power output of distributed PV systems. Since they are complex and correlated in nature, these environmental factors have not been precisely dealt with so far in the current manual design practice. Actually, it is evident that if in the design stage these factors can be fully taken into account, the power output versus investment would be largely enhanced. To this end, this paper proposes an automatic distributed PV system design tool, which fully utilizes and integrates the existing information and modeling techniques, i.e. information from Building Information Model (BIM), as well as advanced optimization methods, to facilitate precise PV system simulation and optimization. The proposed tool firstly makes the detailed shade and radiation analysis on the basis of the existing BIM model, and, according to these results, executes an automatic PV design process oriented by the goal of the minimal cost-to-power ratio. Afterwards, a coordinate transform mechanism is designed to automatically transfer the calculated PV design into the BIM model, which facilitates the realization of automatic deployment. This tool has been applied to a roof PV project and verified via both numerical and empirical studies. Results show that compared to human-based design, this tool can improve up to 265% design efficiency, increase 36.1% power output and reduce around 4.5% capital investment per unit power output.

Multidisciplinary design optimization under correlated uncertainties

Uncertainty analysis is a hot research topic in multidisciplinary design optimization for complex mechanical systems. Existing multidisciplinary design optimization works typically assume that uncertainties are uncorrelated of each other. In real-world engineering systems, however, correlations do exist between different uncertainties. The multidisciplinary design optimization methods without considering correlations between uncertainties may cause inaccuracy and thus misleading optimization results. In this article, we make contributions by proposing a new multidisciplinary design optimization approach based on the ellipsoidal set theory to investigate the characteristics of correlated uncertainties and incorporate their effects in the multidisciplinary design optimization through an advanced collaborative optimization method, where the quantitative model of correlated uncertainties is transformed into constrains of subsystems. Both a mathematical example and a case study of an engineering system are provided to illustrate feasibility and validity of the proposed method.

The influence of secondary effects on global warming and cost optimization of insulation in the building envelope

The relative environmental impact from the building construction phase is increasing compared to the operation phase for new buildings. Therefore, it is important to consider the complete environmental life cycle of energy improvement measures. Many advanced optimization methods have been developed in recent years to assess building life cycle impact. However, these previous studies have not fully addressed the secondary effects, in other words, indirect effects outside the actual design option. This may lead to conclusions of optimization studies based on misleading calculation results.The main purpose this study was to highlight the relevance of including secondary effects in optimization of building design with respect to global warming potential and cost. This was done by conducting a parameter study of the building envelope insulation thickness with regard to global warming potential and life cycle costs, while considering secondary effects induced by the different design options.Findings from this study show that secondary effects influence the system boundary, algorithm architecture, results and the final conclusions of optimal building design. Omitting secondary effects can thus lead to incorrect decision on optimal solutions with regard to global warming potential and life cycle cost. Therefore, it is therefore important to take them into consideration when performing optimization studies of building design options.

Optimized Control and Sizing of Standalone PV-wind Energy Conversion System

Abstract This paper presents an advanced optimization method for a PV-Wind energy conversion system. This method is based on the use of an advanced control strategies of the DC-DC and AC-DC regulator in PV-Wind standalone system. Within this topology a fuzzy logic control is applied to extract the maximum power point of the photovoltaic generator (PVG) and the wind generator (WG). Consequently an optimal sizing of the system by reducing their total cost is designed.

Advanced Pareto Front Non-Dominated Sorting Multi-Objective Particle Swarm Optimization for Optimal Placement and Sizing of Distributed Generation

This paper proposes an advanced Pareto-front non-dominated sorting multi-objective particle swarm optimization (Advanced-PFNDMOPSO) method for optimal configuration (placement and sizing) of distributed generation (DG) in the radial distribution system. The distributed generation consists of single and multiple numbers of active power DG, reactive power DG and simultaneous placement of active-reactive power DG. The optimization problem considers two multi-objective functions, i.e., power loss reduction and voltage stability improvements with voltage profile and power balance as constraints. First, the numerical output results of objective functions are obtained in the Pareto-optimal set. Later, fuzzy decision model is engendered for final selection of the compromised solution. The proposed method is employed and tested on standard IEEE 33 bus systems. Moreover, the results of proposed method are validated with other optimization algorithms as reported by others in the literature. The overall outcome shows that the proposed method for optimal placement and sizing gives higher capability and effectiveness to the final solution. The study also reveals that simultaneous placement of active-reactive power DG reduces more power losses, increases voltage stability and voltage profile of the system.

Robust parameter design for the optimisation of cutting conditions according to energy efficiency criteria

An important objective in process design in the wood-processing industry is to make the system robust - or insensitive - to sources of variability that cannot be entirely controlled once the system is in use. Experimental designs can be used to solve some of these problems. The first techniques were introduced in the 1980s by Genichi Taguchi, a Japanese engineer. The objective of this paper is to apply advanced methods for optimization of cutting conditions using a robust design in the wood-processing industry according to energy efficiency criteria. By showing some numerical simulation results, the effectiveness of the proposed model is illustrated.

Field Measurement-Based System Identification and Dynamic Response Prediction of a Unique MIT Building

Tall buildings are ubiquitous in major cities and house the homes and workplaces of many individuals. However, relatively few studies have been carried out to study the dynamic characteristics of tall buildings based on field measurements. In this paper, the dynamic behavior of the Green Building, a unique 21-story tall structure located on the campus of the Massachusetts Institute of Technology (MIT, Cambridge, MA, USA), was characterized and modeled as a simplified lumped-mass beam model (SLMM), using data from a network of accelerometers. The accelerometer network was used to record structural responses due to ambient vibrations, blast loading, and the October 16th 2012 earthquake near Hollis Center (ME, USA). Spectral and signal coherence analysis of the collected data was used to identify natural frequencies, modes, foundation rocking behavior, and structural asymmetries. A relation between foundation rocking and structural natural frequencies was also found. Natural frequencies and structural acceleration from the field measurements were compared with those predicted by the SLMM which was updated by inverse solving based on advanced multiobjective optimization methods using the measured structural responses and found to have good agreement.

Solving Large Instances of the RSA Problem in Flexgrid Elastic Optical Networks

We present an optimization procedure that mixes advanced large-scale optimization methods and heuristics to solve large instances (with over 1.7 million integer variables) of the routing and spectrum allocation (RSA) problem — a basic optimization problem in flexgrid elastic optical networks. We formulate the problem as a mixed-integer program for which we develop a branch-and-price algorithm enhanced with such techniques as problem relaxations and cuts for improving lower bounds (LBs) for the optimal objective value, and an RSA heuristic for improving the upper bounds. All these elements are combined into an effective optimization procedure. The results of numerical experiments run on network topologies of different dimensions and with large demand sets show that the algorithm performs well and can be applied to the problem instances that are difficult to solve using commercial solvers such as CPLEX.

Advanced optimization methods for whole pelvic and local prostate external beam therapy

PurposeRadiation treatment planning inherently involves multiple conflicting planning goals, which makes it a suitable application for multicriteria optimization (MCO). This study investigates a MCO algorithm for VMAT planning (VMAT–MCO) for prostate cancer treatments including pelvic lymph nodes and uses standard inverse VMAT optimization (sVMAT) and Tomotherapy planning as benchmarks. MethodsFor each of ten prostate cancer patients, a two stage plan was generated, consisting of a stage 1 plan delivering 22Gy to the prostate, and a stage 2 plan delivering 50.4Gy to the lymph nodes and 56Gy to the prostate with a simultaneous integrated boost. The single plans were generated by three planning techniques (VMAT–MCO, sVMAT, Tomotherapy) and subsequently compared with respect to plan quality and planning time efficiency. ResultsPlan quality was similar for all techniques, but sVMAT showed slightly better rectum (on average Dmean −7%) and bowel sparing (Dmean −17%) compared to VMAT–MCO in the whole pelvic treatments. Tomotherapy plans exhibited higher bladder dose (Dmean +42%) in stage 1 and lower rectum dose (Dmean −6%) in stage 2 than VMAT–MCO. Compared to manual planning, the planning time with MCO was reduced up to 12 and 38min for stage 1 and 2 plans, respectively. ConclusionMCO can generate highly conformal prostate VMAT plans with minimal workload in the settings of prostate-only treatments and prostate plus lymph nodes irradiation. In the whole pelvic plan manual VMAT optimization led to slightly improved OAR sparing over VMAT–MCO, whereas for the primary prostate treatment plan quality was equal.

An Experimental Investigation into the Amalgamated Al<sub>2</sub>O<sub>3</sub>-40% TiO<sub>2</sub> Atmospheric Plasma Spray Coating Process on EN24 Substrate and Parameter Optimization Using TLBO

Surface coating is a critical procedure in the case of maintenance engineering. Ceramic coating of the wear areas is of the best practice which substantially enhances the Mean Time between Failure (MTBF). EN24 is a commercial grade alloy which is used for various industrial applications like sleeves, nuts, bolts, shafts, etc. EN24 is having comparatively low corrosion resistance, and ceramic coating of the wear and corroding areas of such parts is a best followed practice which highly improves the frequent failures. The coating quality mainly depends on the coating thickness, surface roughness and coating hardness which finally decides the operability. This paper describes an experimental investigation to effectively optimize the Atmospheric Plasma Spray process input parameters of Al2O3-40% TiO2 coatings to get the best quality of coating on EN24 alloy steel substrate. The experiments are conducted with an Orthogonal Array (OA) design of experiments (DoE). In the current experiment, critical input parameters are considered and some of the vital output parameters are monitored accordingly and separate mathematical models are generated using regression analysis. The Analytic Hierarchy Process (AHP) method is used to generate weights for the individual objective functions and based on that, a combined objective function is made. An advanced optimization method, Teaching-Learning-Based Optimization algorithm (TLBO), is practically utilized to the combined objective function to optimize the values of input parameters to get the best output parameters. Confirmation tests are also conducted and their output results are compared with predicted values obtained through mathematical models. The dominating effects of Al2O3-40% TiO2 spray parameters on output parameters: surface roughness, coating thickness and coating hardness are discussed in detail. It is concluded that the input parameters variation directly affects the characteristics of output parameters and any number of input as well as output parameters can be easily optimized using the current approach.

Parameter Optimization of Amalgamated Al<sub>2</sub>O<sub>3</sub>-40% TiO<sub>2</sub> Atmospheric Plasma Spray Coating on SS304 Substrate Using TLBO Algorithm

SS304 is a commercial grade stainless steel which is used for various engineering applications like shafts, guides, jigs, fixtures, etc. Ceramic coating of the wear areas of such parts is a regular practice which significantly enhances the Mean Time Between Failure (MTBF). The final coating quality depends mainly on the coating thickness, surface roughness and hardness which ultimately decides the life. This paper presents an experimental study to effectively optimize the Atmospheric Plasma Spray (APS) process input parameters of Al2O3-40% TiO2 ceramic coatings to get the best quality of coating on commercial SS304 substrate. The experiments are conducted with a three-level L18 Orthogonal Array (OA) Design of Experiments (DoE). Critical input parameters considered are: spray nozzle distance, substrate rotating speed, current of the arc, carrier gas flow and coating powder flow rate. The surface roughness, coating thickness and hardness are considered as the output parameters. Mathematical models are generated using regression analysis for individual output parameters. The Analytic Hierarchy Process (AHP) method is applied to generate weights for the individual objective functions and a combined objective function is generated. An advanced optimization method, Teaching-Learning-Based Optimization algorithm (TLBO), is applied to the combined objective function to optimize the values of input parameters to get the best output parameters and confirmation tests are conducted based on that. The significant effects of spray parameters on surface roughness, coating thickness and coating hardness are studied in detail.

Inverse analysis of cyclic constitutive models for unsaturated soil under consideration of oscillating functions

In order to assess the probability of foundation failure resulting from cyclic action on structures and to minimize the prediction error, various existing constitutive models considering cyclic loaded dry soils were extended to unsaturated soil conditions by the authors, thus requiring further calibration during application on existing slightly variable soil condition as well as the soil heterogeneities. The efficiency and effectiveness of these models is majorly influenced by the cyclic constitutive parameters and the soil suction. Little or no details exist in literature about the model based identification and the calibration of the constitutive parameters under cyclic loaded soils. This could be attributed to the difficulties and complexities of the inverse modeling of such complex phenomena. A wide variety of optimization strategies for the solution of the sum of least-squares problems as usually done in the field of model calibration exists, however the inverse analysis of the unsaturated soil response under oscillatory load functions has not been solved up to now. This paper gives insight into the model calibration challenges and also puts forward advanced optimization methods for the inverse modeling of cyclic loaded foundation response on unsaturated soils.