Genetic Algorithm Based Optimization Approach Research Articles

Genetic Algorithm-Based Optimization Approach for Solving a Class of Inverse Problems with Tikhonov Regularization

In this paper, we are interested in solving the data completion problem for the Laplace equation. It consists to determine the missing data on the inaccessible part of the boundary from overspecified conditions in the accessible part. Knowing that this problem is severely ill-posed, we consider its formulation as an optimization problem using Tikhonov regularization. Then, we consider an optimization approach based on adapted Real Coded Genetic Algorithm (RCGA) to minimize the cost function and recover the missing data. The performed numerical simulations, with different domains, illustrate the accuracy and efficiency of the proposed method with an adequate regularization parameter, in addition to the good agreement between the numerical solutions and different noise level of the given data.

Genetic algorithm optimization for parametrization, digital twinning, and now-casting of unknown small- and medium-scale PV systems based only on on-site measured data

Accurately predicting and balancing energy generation and consumption are crucial for grid operators and asset managers in a market where renewable energy is increasing. To speed up the process, these predictions should ideally be performed based only on on-site measured data and data available within the monitoring platforms, data which are scarce for small- and medium-scale PV systems. In this study, we propose an algorithm that can now-cast the power output of a photovoltaic (PV) system with high accuracy. Additionally, it offers physical information related to the configuration of such a PV system. We adapted a genetic algorithm-based optimization approach to parametrize a digital twin of unknown PV systems, using only on-site measured PV power and irradiance in the plane of array. We compared several training datasets under various sky conditions. A mean deviation of −1.14 W/kWp and a mean absolute percentage deviation of 1.81% were obtained when we analyzed the accuracy of the PV power now-casting for the year 2020 of the 16 unknown PV systems used for this analysis. This level of accuracy is significant for ensuring the efficient now-casting and operation of PV assets.

Computational design of custom therapeutic cells to correct failing human cardiomyocytes.

Myocardial delivery of non-excitable cells-namely human mesenchymal stem cells (hMSCs) and c-kit+ cardiac interstitial cells (hCICs)-remains a promising approach for treating the failing heart. Recent empirical studies attempt to improve such therapies by genetically engineering cells to express specific ion channels, or by creating hybrid cells with combined channel expression. This study uses a computational modeling approach to test the hypothesis that custom hypothetical cells can be rationally designed to restore a healthy phenotype when coupled to human heart failure (HF) cardiomyocytes. Candidate custom cells were simulated with a combination of ion channels from non-excitable cells and healthy human cardiomyocytes (hCMs). Using a genetic algorithm-based optimization approach, candidate cells were accepted if a root mean square error (RMSE) of less than 50% relative to healthy hCM was achieved for both action potential and calcium transient waveforms for the cell-treated HF cardiomyocyte, normalized to the untreated HF cardiomyocyte. Custom cells expressing only non-excitable ion channels were inadequate to restore a healthy cardiac phenotype when coupled to either fibrotic or non-fibrotic HF cardiomyocytes. In contrast, custom cells also expressing cardiac ion channels led to acceptable restoration of a healthy cardiomyocyte phenotype when coupled to fibrotic, but not non-fibrotic, HF cardiomyocytes. Incorporating the cardiomyocyte inward rectifier K+ channel was critical to accomplishing this phenotypic rescue while also improving single-cell action potential metrics associated with arrhythmias, namely resting membrane potential and action potential duration. The computational approach also provided insight into the rescue mechanisms, whereby heterocellular coupling enhanced cardiomyocyte L-type calcium current and promoted calcium-induced calcium release. Finally, as a therapeutically translatable strategy, we simulated delivery of hMSCs and hCICs genetically engineered to express the cardiomyocyte inward rectifier K+ channel, which decreased action potential and calcium transient RMSEs by at least 24% relative to control hMSCs and hCICs, with more favorable single-cell arrhythmia metrics. Computational modeling facilitates exploration of customizable engineered cell therapies. Optimized cells expressing cardiac ion channels restored healthy action potential and calcium handling phenotypes in fibrotic HF cardiomyocytes and improved single-cell arrhythmia metrics, warranting further experimental validation studies of the proposed custom therapeutic cells.

Design of the optimal groundwater quality monitoring network using a genetic algorithm based optimization approach

سابقه و هدف: توزیع مکانی و دقت داده­های کیفی در مدیریت منابع آب زیرزمینی ضروری است. معمولا این داده­ها از چاه­های پایش جمع­آوری می­شود که به صورت مکانی در حوضه آبریز یا آبخوان مورد مطالعه توزیع شده است. در طراحی شبکه پایش، حداقل تعداد چاه­های پایش با توزیع مکانی بهینه و مناسب از نظر اقتصادی مورد نیاز است. بنابراین نحوه توزیع چاه­ها و تعداد آن­ها در طراحی شبکه پایش کیفی یکی از موارد مهم در بهینه­سازی مسائل مربوط به آب زیرزمینی است. هدف از این مطالعه، یافتن شبکه پایش بهینه با حداقل تعداد چاه­ها در آبخوان دشت گیلان است، به­طوری­که، توزیع مکانی مناسبی از نظر پارامترهای کیفیت آب زیرزمینی داشته باشد. با توجه به اینکه یکی از مهمترین معیارهای کیفیت آب زیرزمینی شوری است که با پارامترهایی مانند کل جامدات محلول (TDS)، یون کلر (Cl) و هدایت الکتریکی (EC) اندازه­گیری می­شود، در این مطالعه پارامتر EC به عنوان پارامتر کیفی در طراحی شبکه پایش انتخاب گردید. مواد و روش­ها: برای جستجوی بهینه شبکه پایش کیفی از الگوریتم بهینه­سازی ژنتیک (GA) استفاده شد. در این روش، یک شبکه ممکن از چاه­های پایشی که در منطقه واقع شده است به عنوان یک کروموزوم در نظر گرفته می­شود و هر چاه پایش، ژن­های کروموزوم هستند که با ساختار دودویی (صفر و یک) کدگذاری می­شوند. در صورتی­که انتخاب شود یک و در غیر این صورت صفر خواهد بود. در این مطالعه همزمان دو تابع هدف متضاد حل می­شود. هدف اول حداکثر کردن تطابق (برازش) بین توزیع­های EC محاسبه شده در شبکه پایش موجود و شبکه جدید است. که این تطابق با استفاده از کارایی مدل نش ساتکلیف ارزیابی می­شود. هدف دوم نیز با در نظر گرفتن محدودیت مربوط به هزینه، حداقل کردن تعداد چاه­های پایش در شبکه جدید ایجاد شده می­باشد. این دو هدف در یک تابع هدف تعریف می­شود که در آن ترکیب­های مختلفی از دو هدف با اعمال ضریب وزنی w مورد بررسی قرار می­گیرد. نتایج و بحث: نتایج ارزیابی­ها نشان داد که انتخاب جواب بهینه تا حد زیادی به تعیین ضریب وزنی w بستگی دارد. بنابراین باید مقدار w را با توجه به متعادل­ترین جواب انتخاب شود. متعادل­ترین جواب به این معناست که بین هزینه و پراکنش مکانی چاه­ها در منطقه یک رابطه قابل قبولی برقرار باشد. در ادامه، برای انتخاب بهترین جواب بهتر است علاوه بر استفاده از ضریب کارایی نش ساتکلیف از شاخص­های عملکرد دیگری مانند PBIAS، RMSE، ضریب رگرسیون و انحراف معیار نیز استفاده شود. به طور کلی، نتایج بدست آمده در این مطالعه با توجه به این شاخص­ها قابل قبول بوده است. همچنین، بررسی توزیع مکانی و مقایسه میانگین مقادیر EC مشاهده شده در منطقه و EC محاسبه شده در شبکه بهینه با توجه به همه چاه­های پایش انتخاب شده نشان می­دهد که مقادیر بهینه­شده بزرگتر از میانگین مقادیر مشاهده شده در منطقه است. بنابراین به وضوح نتیجه­گیری می­شود که شبکه بهینه­شده داده­های کیفی آب زیرزمینی مناطق آلوده­تر را فراهم می­کند. نتیجه­ گیری: نتایج نشان داد که کاربرد روش بهینه­سازی به طور قابل ملاحظه­ای تعداد چاه­های پایش را با توجه به توزیع مکانی مقادیر EC کاهش می­دهد. علاوه بر این، شبکه پایش طوری طراحی شد که تعداد نقاط نمونه­برداری در مناطق با آلودگی کمتر، حذف شد و در مناطقی با آلودگی بیشتر، اضافه شد. در طراحی شبکه پایش کیفی آب زیرزمینی بهتر است به صورت دوره­ای بهینه­سازی انجام شود، زیرا انتظار می­رود کارایی شبکه پایش با اضافه شدن چاه­های جدید به شبکه تغییر کند. ارزیابی­های پی در پی شبکه پایش به صورت هر چند سال یک بار، در تعیین ارزیابی درازمدت کیفیت آب زیرزمینی و عوامل موثر در آن کمک می­کند که می­تواند در برنامه­ریزی و اعمال روش­هایی برای بهبود کیفیت آب زیرزمینی موثر باشد.

Weight and Material Optimization of Scissor-hinge Linkages According to Given Span Length

Scissor-hinge linkages are one type of deployable structures which are mostly used in architectural and engineering applications due to their transformation capabilities and the advantages of ease of erection and dismantling. For hundreds of years, these linkages have been used in wide range of applications such as deployable roof structures, bridges, shells, pavilions, emergency shelters, furniture design and satellite equipment. Scissor-hinge linkages have different primary units depending on the geometry of the bars and the location of the pivot point, which are called as polar, translational, and angulated. Geometry and dimensions of these primary elements directly affect the geometry and the weight of the whole structure. This study aims to investigate the relationship between the span length, geometry of the primary units, cross section of the bars and the material of the scissor-hinge linkages by means of genetic algorithms. In detail, a structural and geometric optimization is made in order to obtain the lightest geometric configuration for the given span lengths while keeping the structural strength and stability by altering typology, dimensions, number of the primary units and material parameters. To achieve that a multi-objective genetic algorithm based optimization approach is utilized. Generative model is created in Grasshopper® parametric design software. The structural performances of the generated solutions are evaluated with the help of Karamba3D that is a parametric structural tool for Grasshopper. Optimization of the problem is performed via multi objective genetic optimization plug-in named Octopus.

Statistical modelling and optimization of clad characteristics in laser metal deposition of austenitic stainless steel

The study aims to design and develop an integrated system to model and optimize the direct metal deposition process. Direct metal deposition is an emerging metal additive manufacturing technology, in which powder particles are melted using a high-energy laser source to consolidate a solid layer. With the aim to work with a variety of materials, process economy is an important consideration. Keeping this in mind, the present work shows application of statistical methods and evolutionary algorithms in laser-based manufacturing. A regression model using response surface method has been developed considering laser power, scan speed and powder flow rate as the process parameters, while capture efficiency and clad layer height are considered as the clad characteristics response. The developed regression model provides useful information to control responses and ensure building a desired clad layer height as per prototyping requirements. A robust correlation between the experimentally obtained values and model-predicted values for responses is achieved, and the error percentage is 5% and 10% for capture efficiency and clad layer height, respectively. Genetic algorithm-based approach for optimization yields improvement of 30.93% and 71.02% for capture efficiency and clad layer height, respectively.

Optimization of inventory policies of food grain distribution stage in public distribution system

Purpose The purpose of this paper is to model the distribution stage of the public distribution system (PDS) and optimize the inventory policy during this stage of the PDS to address some of the inefficiencies present in the system. This study models this supply chain as a multistage supply chain consisting of storage depots, issue centers, fair price shops and card holders. Design/methodology/approach A two-stage modeling approach is used to model the distribution stage in the PDS. In the first stage, the authors developed a simulation model for periodic review-based stock policy with appropriate assumptions. This helped minimize the total supply chain cost (TSCC). The TSCC consists of three cost elements, namely, ordering cost, holding cost and shortage cost. These three cost elements, in turn, depend on inventory policy parameters, such as review periods and base stock levels, at various echelons. In the second stage, a Genetic Algorithm based optimization approach was used. Findings A set of optimal policy parameters was identified. It is observed that base stock levels at issue centers are higher as compared to those in the FPS and the TSCC is less in scenario, when backorder cost is equal to the holding cost. Practical implications Present study will be useful to policy makers in improving PDS performance. This optimization of inventory policies helps actors in the PDS supply chain to choose appropriate policy parameters in the present inventory policy so as to reduce the overall distribution cost. Originality/value Unlike the previous researchers who examined the PDS from the social security perspective and tried to address specific problems to improve functioning of the PDS, this study looked at the problem as a supply chain-related problem and optimized the inventory parameters in one of the subsets of the PDS supply chain.

Identification of the optimum groundwater quality monitoring network using a genetic algorithm based optimization approach

Management of groundwater requires a sufficient coverage of accurate groundwater quality data. These data are usually collected from monitoring wells which are spatially distributed in the river basin or the groundwater body that is studied. A minimum number of monitoring wells with an optimum spatial distribution is desired to ensure a cost-effective observation of the groundwater body. Therefore, the configuration of groundwater monitoring networks and the number of required wells becomes an important engineering optimization problem. The goal of this study is to find an optimum monitoring network with the fewest wells that provides sufficient spatial coverage on groundwater quality. With the presented method redundant wells in an already existing network are identified. Here, a genetic algorithm (GA) based optimization approach is used in which each monitoring well in the watershed is represented with a binary GA bit to evaluate if the corresponding monitoring well will be selected for the network. The proposed approach can solve the problem by simultaneously optimizing two conflicting objectives. The first objective is the maximization of the match between the interpolated groundwater quality concentration distributions obtained using data from all available monitoring wells and the wells from the newly generated network. The match is primarily evaluated using the Nash-Sutcliffe (NS) model efficiency. Groundwater quality is represented by the water quality index WQI that aggregates several quality parameters. The second objective deals with the minimization of the number of monitoring wells in the newly generated network by considering cost-related constraints. These two objectives are integrated in a single objective function where different combinations of both objectives are investigated by considering two cases. The applicability of the proposed approach is evaluated for the groundwater monitoring network of the Gediz River Basin (GRB) which is one of the most important river basins in Turkey. Findings indicate that the proposed approach significantly reduces the number of monitoring wells with a relatively small deviation of the spatial distribution of the WQI values. Also, the monitoring network is optimized such that sampling points are removed from less polluted areas and selected in areas with higher pollutant concentrations.

Robust Virtual Welding Process Optimization

The finished geometry of a welded structure is greatly affected by heat-induced distortion from the welding process. This often requires correction by straightening and top-level machining to ensure manufacturing quality. Distortion is sensitive to weld sequencing and manufacturing environment. Optimal weld sequencing can significantly reduce distortion and manufacturing cost. However, an optimal weld sequence is not always intuitively obvious, especially for large structures with many welds. Virtual Fabrication Technology (VFT) is Caterpillar’s proprietary software for the simulation of welding physics via finite element analysis. Powered by the highly successful VFT platform, a combinatorial genetic algorithm based optimization approach combined with an uncertainty quantification (UQ) module for evaluating robustness has been developed at Caterpillar for weld sequence optimization (WSO). WSO has been successfully applied to more than 50 structures, resulting in the minimization of straightening operations. The combined VFT and robust WSO approach results in an accelerated manufacturing process development schedule. We will present a summary of our WSO approach and some results that demonstrate how WSO has proven to be a transformative manufacturing planning technology at Caterpillar.

On Production and Green Transportation Coordination in a Sustainable Global Supply Chain

This paper addresses a coordination problem of production and green transportation and the effects of production and transportation coordination on supply chain sustainability in a global supply chain environment with the consideration of important realistic characteristics, including parallel machines, different order processing complexities, fixed delivery departure times, green transportation and multiple transportation modes. We formulate the measurements for carbon emissions of different transportation modes, including air, sea and land transportation. A hybrid genetic algorithm-based optimization approach is developed to handle this problem, in which a hybrid genetic algorithm and heuristic procedures are combined. The effectiveness of the proposed approach is validated by means of various problem instances. We observe that the coordination of production and green transportation has a large effect on the overall supply chain sustainability, which can reduce the total supply chain cost by 9.60% to 21.90%.

Frequency Regulation of Micro-grid Connected Hybrid Power System with SMES

This paper presents the frequency regulation analysis of a micro-grid connected hybrid power system based on solar Photovoltaic (PV), Wind and Diesel-Engine Generator (DEG) with Superconducting Magnetic Energy Storage system (SMES) unit. Abrupt change in load demand and power fluctuations from PV and wind power source causes frequency variability in the system. In order to mitigate this issue, the hybrid power system incorporates along with the energy storage devices. A case study of the impact of SMES operations on the performance of frequency stability of hybrid system has been carried out. Simulation of a Hybrid Power System (HPS) model has been carried out in two different scenarios with the two phases. First is the steady state investigation of HPS using PI controller with step load change response. Second is the dynamic performance study throughout a typical day with arbitrary variation in load demands. Controller parameters are tuned with Genetic Algorithm based optimization approach. The limits on the inductor (coil) current due to the possibilities of discontinuous conduction in the presence of large disturbances and limited storage capacity have also been incorporated. HPS has analyzed in the Matlab Simulink environment. Simulation results indicate that SMES system can contribute towards the frequency stability in both cases for variable power generations from renewable sources and irregular demand loads.

A Comparison of Algorithms for Solving Multicomponent Optimization Problems

Real-world problems are often composed of multiple interdependent components. In this case, benchmark problems that do not represent that interdependency are not a good choice to assess algorithm performance. In recently literature, a benchmark problem called Travelling Thief Problem (TTP) was proposed to better represent real-world multi-component problems. TTP is a combination of two well-known problems: 0-1 Knapsack Problem and the Travelling Salesman Problem. This paper presents a genetic algorithm-based optimization approach for solving TTP. It aims to solve the overall problem instead of each sub-component separately. The proposed algorithm was tested on 60 representative instances of TTP available in the literature. The comparisons show that the genetic algorithm obtains competitive solutions for small TTP instances.

Coarse graining of force fields for metal–organic frameworks

We have adapted our genetic algorithm based optimization approach, originally developed to generate force field parameters from quantum mechanic reference data, to derive a first coarse grained force field for a MOF, taking the atomistic MOF-FF as a reference. On the example of the copper paddle-wheel based HKUST-1, a maximally coarse grained model, using a single bead for each three and four coordinated vertex, was developed as a proof of concept. By adding non-bonded interactions with a modified Buckingham potential, the resulting MOF-FF-CGNB is able to predict local deformation energies of the building blocks as well as bulk properties like the tbovs.pto energy difference or elastic constants in a semi-quantitative way. As expected, the negative thermal expansion of HKUST-1 is not reproduced by the maximally coarse grained model. At the expense of atomic resolution, substantially larger systems (up to tens of nanometers in size) can be simulated with respect to structural and mechanical properties, bridging the gap to the mesoscale. As an example the deformation of the [111] surface of HKUST-1 by a "tip" could be computed without artifacts from periodic images.

Integration of Distributed Energy Resources

This Study proposes the feasibility of proposed restructured power system for the integration of distributed energy resources. Once the proposed power system network takes place, it will be able to provide opportunities for sustained economic growth. Main objectives of the proposed network are maximization of Distributed Generation (DGs) penetration and decarbonization of electric utility. IEEE-14 bus test system with proposed configuration has been undertaken for MATLAB® simulations are compared with genetic algorithm based optimization approach.

Feasibility of Interconnected Distribution Network for the Integration of Distributed Energy Resources Using GA Approach

This paper envisages the feasibility of proposed power system configuration, which aspires at maximizing the electricity generation from green energy sources and furthermore to initiate the involvement of the consumers. Once the proposed distribution network configuration implemented, a power system based on distributed energy generation technologies is likely to be able to provide opportunities for sustainable development. Main objectives of the proposed network configuration are increased penetration of distributed generation (DGs) and decarbonization of electric utility. The IEEE-14 bus with proposed configuration has been simulated using MATLAB Simulink, which shows that interconnection of distribution networks allow the operation of centralized electricity generation and distributed energy generation efficiently. The outcomes of the proposed power system configuration have been compared with genetic algorithm based optimization approach

Robust <i>H∞</i> Output-Feedback Synthesis Optimal Control to Uncertain Time-Varying Delay System with Genetic Algorithm

This paper explores the robust stability and H∞ control performance index of uncertain system with time-varying delay. The output feedback H∞ controller is constituted at the finite uncertain limit. The condition of delay-independence for the system with asymptotic stable and perturbation sustaining is derived as LMI formulation. The optimum control problem of uncertain time-delay system with H-infinite output feedback is presented. The stability and exogenous disturbance constraint synthesis is performed by chaos and genetic algorithm. The genetic algorithm based optimization approach is developed for extracting the upper bound of the performance index. Experiment results suggest that the H∞ output feedback control law is less conservative and possesses much high stabilization and optimization performance.

Genetic Algorithm based Optimization approach for MR Dampers Fuzzy Modeling

Magnetorheological (MR) fluid damper is a semi� active control device that has recently received more attention by the vibration control community. But inherent hysteretic and highly nonlinear dynamics of MR fluid damper is one of the challenging aspects to employ its unique characteristics. The combination of artificial neural network (ANN) and fuzzy logic system (FLS) have been used to imitate more precisely the behavior of this device. However, the derivativebased nature of adaptive ne tworks causes some deficiencies. Therefore, in this paper, a novel approach that employ genetic algorithm, as a freederivative algo rithm, to enhance the capability of fuzzy systems, is proposed. The proposed method used to model MR damper. The results will be compared with adaptive neurofuzzy inference system (ANFIS) model , which is one of the wellknown approaches in soft computing fram ework, and two best parametric models of MR damper. Data are generated based on benchmark program by applying a number of famous earthquake records.

Sensitivity of Optimal Tradeoffs between Cost and Greenhouse Gas Emissions for Water Distribution Systems to Electricity Tariff and Generation

Increased awareness of climate change has shifted the focus of water distribution system (WDS) optimization research from cost minimization only to the incorporation of energy or associated greenhouse gas (GHG) minimization. In this study, a sensitivity analysis is conducted to investigate the effect of electricity tariff and generation (emission factors) on the results of multiobjective WDS optimization accounting for both total economic cost (both capital and operating costs) and GHGs. A multiobjective genetic algorithm-based optimization approach is used to conduct the analysis. The results show that electricity tariff has a significant effect on the total economic cost of WDSs and the selection of optimal solutions. In contrast, the changes of emission factors in the future have a significant effect on the total GHGs from WDSs. However, it does not alter the final solutions on the Pareto-optimal front. DOI: 10.1061/(ASCE)WR.1943-5452.0000169. © 2012 American Society of Civil Engineers. CE Database subject headings: Water distribution systems; Optimization; Emissions; Climate change; Sensitivity analysis; Algorithms. Author keywords: Water distribution systems; Multiobjective optimization; Greenhouse gas emissions; Climate change; Sensitivity analysis; Genetic algorithms.

Genetic Algorithm-Based Optimization Approach and Generic Tool for Calibrating Traffic Microscopic Simulation Parameters

GENOSIM is a generic traffic microsimulation parameter optimization tool that uses genetic algorithms and was implemented in the Port Area network in downtown Toronto, Canada. GENOSIM was developed as a pilot software as part of the pursuit of a fast, systematic, and robust calibration process. It employs the state of the art in combinatorial parametric optimization to automate the tedious task of hand calibrating traffic microsimulation models. The employed global search technique, genetic algorithms, can be integrated with any dynamic traffic microscopic simulation tool. In this research, Paramics, the microscopic traffic simulation platform currently adopted at the University of Toronto Intelligent Transportation Systems Centre, was used. Paramics consists of high-performance, cross-linked traffic models that have multiple user adjustable parameters. Genetic algorithms in GENOSIM manipulate the values of those control parameters and search for an optimal set of values that minimize the discrepancy between simulation output and real field data. Results obtained by replicating observed vehicle counts are promising.

Facial modeling from an uncalibrated face image using a coarse-to-fine genetic algorithm

This paper presents a genetic algorithm-based optimization approach for facial modeling from an uncalibrated face image using a flexible generic parameterized facial model (FGPFM). The FGPFM can be easily modified using the facial features as parameters of FGPFM to construct an accurate specific 3D facial model from only a photograph of an individual with a yawed face based on the projection transformation. The facial modeling problem is formulated as a parameter optimization problem and the objective function is also given. Moreover, a coarse-to-fine approach based on our intelligent genetic algorithm which can efficiently solve the large parameter optimization problems is used to accelerate the search for an optimal solution. Furthermore, sensitivity analysis and experimental results with texture mapping demonstrate the effectiveness of the proposed method.