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27th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Sudeng, Sufian Wattanapongsakorn, Naruemon King Mongkuts Univ Technol Thonburi Dept Comp Engn 126 Pracha Uthit Rd Bangkok 10140 Thailand

ISBN: (纸本)9781509001637

Helping the human decision maker (DM) in finding the most preferred solution is the main purpose of Preference-based multi-objective Evolutionary Algorithm (P-MOEA). Interactive methods have been increasingly developed during the years because the preference information is less explicit than other methods. In addition, the DM can specify and correct his/her preference during the course of optimization. Our attention is to develop a new P-MOEA under the assumption that the DM must be available and willing to participate in solution process interactively. Our approach is hybridized between two well-known preference models, reference point and extended dominance approaches. Initially, pattern solution is approximated, Region of Interest (ROI) is identified by the means of user-supplied reference-point, and then a pruning mechanism called ASA approach is applied in order to filter out undesired solutions. The approach is evaluated using Component Redundancy Allocation Problem (RAP) in order to demonstrate the usefulness of our approach in practical context.

关键词： multi-objective optimization preference-based multi-objective evolutionary algorithm pareto-optimal solutions interactive approach

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Semi-analytical Modeling and optimization of a: SiGe Thin-film Solar Cell Including multi-trench Region 3

Semi-analytical Modeling and Optimization of a: SiGe Thin-fi...

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INTERNATIONAL CONFERENCE ON CONTROL ENGINEERING & INFORMATION TECHNOLOGY (CEIT)

作者： Ferhati, H. Djeffal, F. Bencherif, H. Meguellati, M. Univ Batna Dept Elect LEA Batna 05000 Algeria Univ Batna LEPCM Batna 05000 Algeria

ISBN: (纸本)9781479982134

In this paper, we present semi-analytical modeling and optimization approaches based on genetic algorithm computation to improve the electrical performance of amorphous SiGe thin-film solar cell including multi-trench region. The key idea behind the proposed investigation is to model the electrical efficiency using 2D numerical database and analytical fitting. Semi-analytical expression of the efficiency for the trench-dependent electrical models, have been used to estimate the performance of the investigated a: SiGe thin-film solar cell. Trench-dependent model includes the multi-trench region parameters, which are: the doping, thickness and width. The semi-analytical model has been used to formulate the objective function where multi-trench region, its geometry dependence and doping profile are considered simultaneously. The proposed approach is used to search for solar cell design parameters that optimize the electrical performance. The obtained results are validated with 2D numerical data, where a good agreement is recorded. The proposed semi-analytical analysis may provide a practical basis to investigate and optimize the a:SiGe-based solar cells for low cost and high performance applications.

关键词： genetic algorithm optimization solar cell SiGe thin-film

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Heuristic algorithms for a three-dimensional loading capacitated vehicle routing problem in a carrier

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COMPUTERS & INDUSTRIAL ENGINEERING 2015年 88卷 110-130页

作者： Junqueira, Leonardo Morabito, Reinaldo Univ Nove Julho Programa Posgrad Engn Prod BR-05001100 Sao Paulo SP Brazil Univ Fed Sao Carlos Dept Engn Prod BR-13565905 Sao Carlos SP Brazil

In this paper, we present heuristic algorithms for a three-dimensional loading capacitated vehicle routing problem arising in a real-world situation. In this problem, customers make requests of goods, which are packed in a sortment of boxes. The objective is to find minimum cost delivery routes for a set of identical vehicles that, departing from a depot, visit all customers only once and return to the depot. Apart of the usual 3D container loading constraints which ensure that the boxes are packed completely inside the vehicles and that the boxes do not overlap each other in each vehicle, the problem also takes into account constraints related to the vertical stability of the cargo and multi-drop situations. The algorithms are based on the combination of classical heuristics from both vehicle routing and container loading literatures, as well as two metaheuristic strategies, and their use in more elaborate procedures. Although these approaches cannot assure optimal solutions for the respective problems, they are relatively simple, fast enough to solve real instances, flexible enough to include other practical considerations, and normally assure relatively good solutions in acceptable computational times in practice. The approaches are also sufficiently generic to be embedded with algorithms other than those considered in this study, as well as they can be easily adapted to consider other practical constraints, such as the load bearing strength of the boxes, time windows and pickups and deliveries. Computational tests were performed with these methods considering instances based on the vehicle routing literature and actual customers' orders, as well as instances based on a real-world situation of a Brazilian carrier. The results show that the heuristics are able to produce relatively good solutions for real instances with hundreds of customers and thousands of boxes. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Capacitated vehicle routing problem Three-dimensional loading Combinatorial optimization Heuristics

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Comparing Robust Design optimization and Reliability Based optimization Formulations for practical Aspects of Industry Problems

Comparing Robust Design Optimization and Reliability Based O...

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SAE 2015 World Congress and Exhibition

作者： Gokhale, Apurva Parashar, Sumeet Kansara, Saket ESTECO North America United States ESTECO Software India India

Need for accounting Robustness and Reliability in engineering design is well understood and being researched. However, the actual practice of applying robustness and reliability methods to high fidelity CAE based simulations, especially during optimization is just starting to gain traction in last few years. Availability of computing power is helping the use of such methods, but, at the same time the demand for modeling stochastic behavior with high fidelity CAE simulations and considering large number of stochastic variables still makes it prohibitive. Typically, Robust Design optimization (RDO) formulations calculate mean and standard deviation of responses based on sampling. On the other hand Reliability Based Design optimization (RBDO) formulations have been using methods like First Order Reliability Method (FORM) or Second Order Reliability Method (SORM) which require nested optimization to evaluate joint probability distribution and reliability factor. It is difficult for an industry practitioner with limited expertise in optimization to decide which approach to use when they want to find a robust yet optimal design. This paper will try to evaluate these approaches based on following criteria;1. Ease of articulating the problem formulation;2. Effectiveness and flexibility of solution strategy;and 3. Ease of analyzing the data from optimization and picking a final solution. This Paper will also try to highlight the use of multi-objective formulations for both approaches. The paper will focus on industry examples where one has to use commercial tools for optimization and simulation. This paper covers the application and comparison of the above mentioned methods based on practical solution aspects for automotive problems and crash safety. One of the Automotive problem will be from safety application that will use models from MADYMO. All the optimization, uncertainty quantification and automation of CAE analysis tools is performed using commercial optimization sof

关键词： Reliability

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An Evolutionary Algorithm with Classifier Guided Constraint Evaluation Strategy for Computationally Expensive optimization Problems 1

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28th Australasian Joint Conference on Artificial Intelligence (AI)

作者： Bhattacharjee, Kalyan Shankar Ray, Tapabrata Univ New S Wales Sch Engn & Informat Technol Canberra ACT 2600 Australia

ISBN: (数字)9783319263502

ISBN: (纸本)9783319263502;9783319263496

practical optimization problems often involve objective and constraint functions evaluated using computationally expensive numerical simulations e.g. computational fluid dynamics (CFD), finite element methods (FEM) etc. In order to deal with such problems, existing methods based on surrogates/approximations typically use cheaper and less accurate models of objectives and constraint functions during the search. Promising solutions identified using approximations or surrogates are only evaluated using computationally expensive analysis. In the event the constraints and objectives are evaluated using independent computationally expensive analysis (e.g. multi-disciplinary optimization), there exists an opportunity to only evaluate relevant constraints and/or objectives that are necessary to ascertain the utility of such solutions. In this paper, we introduce an efficient evolutionary algorithm for the solution of computationally expensive single objective constrained optimization problems. The algorithm is embedded with selective evaluation strategies guided by Support Vector Machine (SVM) models. Identification of promising individuals and relevant constraints corresponding to each individual is based on SVM classifiers, while partially evaluated solutions are ranked using SVM ranking models. The performance of the approach has been evaluated using a number of constrained optimization benchmarks and engineering design optimization problems with limited computational budget. The results have been compared with a number of established approaches based on full and partial evaluation strategies. Hopefully this study will prompt further efforts in the direction of selective evaluation, which so far had attracted little attention.

关键词： Constraint handling Selective evaluation Approximation

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multi-input genetic algorithm for experimental optimization of the reattachment downstream of a backward-facing step with surface plasma actuator 46

Multi-input genetic algorithm for experimental optimization ...

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46th AIAA Plasmadynamics and Lasers Conference, 2015

作者： Benard, Nicolas Pons-Prats, Jordi Periaux, Jacques Bugeda, Gabriel Bonnet, Jean-Paul Moreau, Eric Institut PPRIME - UPR 3346 – CNRS Université de Poitiers - ISAE/ENSMA SP2MI Téléport2 Bd Marie and Pierre Curie BP 30179 Futuroscope Chasseneuil Cedex86962 France c/ Esteve Terrades 5 Castelldefels08860 Spain c/ Gran Capità s/n Barcelona08034 Spain

ISBN: (数字)9781624103605

ISBN: (纸本)9781624103605

The practical interest of flow control approaches is no more debated as flow control provides an effective mean for considerably increasing the performances of ground or air transport systems, among many others applications. Here a fundamental configuration is investigated by using non-thermal surface plasma discharge. A dielectric barrier discharge is installed at the step corner of a backward-facing step (Reh=30000, Reθ=1650). Wall pressure sensors are used to estimate the reattaching location downstream of the step. The primary objective of this paper is the coupling of a numerical optimizer with an experiment. More specifically, optimization by genetic algorithm is implemented experimentally in order to minimize the reattachment point downstream of the step model. Validation through inverse problem is firstly demonstrated. When coupled with the plasma actuator and the wall pressure sensors, the genetic algorithm finds the optimum forcing conditions with a good convergence rate, the best control design variables being in agreement with the literature that uses other types of control devices than plasma. Indeed, the minimum reattaching position is achieved by forcing the flow at the shear layer mode where a large spreading rate is obtained by increasing the periodicity of the vortex street and by enhancing the vortex pairing phenomena. At the best forcing conditions, the mean flow reattachment is reduced by 20%. This article, with its experiment-based approach, demonstrates the robustness of a single-objective multi-design optimization method, and its feasibility for wind tunnel experiments. © 2015, American Institute of Aeronautics and Astronautics Inc, AIAA. All Rights Reserved.

关键词： Flow control

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An Approach to Identify Six Sigma Robust Solutions of multi/Many-objective Engineering Design optimization Problems

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JOURNAL OF MECHANICAL DESIGN 2015年第5期137卷 051404-051404页

作者： Ray, Tapabrata Asafuddoula, Md Singh, Hemant Kumar Alam, Khairul Univ New S Wales Sch Engn & Informat Technol Canberra ACT 2600 Australia

In order to be practical, solutions of engineering design optimization problems must be robust, i.e., competent and reliable in the face of uncertainties. While such uncertainties can emerge from a number of sources (imprecise variable values, errors in performance estimates, varying environmental conditions, etc.), this study focuses on problems where uncertainties emanate from the design variables. While approaches to identify robust optimal solutions of single and multi-objective optimization problems have been proposed in the past, we introduce a practical approach that is capable of solving robust optimization problems involving many objectives building on authors' previous work. Two formulations of robustness have been considered in this paper, (a) feasibility robustness (FR), i.e., robustness against design failure and (b) feasibility and performance robustness (FPR), i.e., robustness against design failure and variation in performance. In order to solve such formulations, a decomposition based evolutionary algorithm (DBEA) relying on a generational model is proposed in this study. The algorithm is capable of identifying a set of uniformly distributed nondominated solutions with different sigma levels (feasibility and performance) simultaneously in a single run. Computational benefits offered by using polynomial chaos (PC) in conjunction with Latin hypercube sampling (LHS) for estimating expected mean and variance of the objective/constraint functions has also been studied in this paper. Last, the idea of redesign for robustness has been explored, wherein selective component(s) of an existing design are altered to improve its robustness. The performance of the strategies have been illustrated using two practical design optimization problems, namely, vehicle crash-worthiness optimization problem (VCOP) and a general aviation aircraft (GAA) product family design problem.

关键词： Design optimization Robustness Algorithms Engineering design Uncertainty

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MOMM: multi-objective model merging

MOMM: Multi-objective model merging

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作者： Mansoor, Usman Kessentini, Marouane Langer, Philip Wimmer, Manuel Bechikh, Slim Deb, Kalyanmoy University of Michigan DearbornMI United States Vienna University of Technology Vienna Austria University of Michigan East Lansing DearbornMI United States

Nowadays, software systems are complex and large. To cope with this situation, teams of developers have to cooperate and work in parallel on software models. Thus, techniques to support the collaborative development of models are a must. To this end, several approaches exist to identify the change operations applied in parallel, to detect conflicts among them, as well as to construct a merged model by incorporating all non-conflicting operations. Conflicts often denote situations where the application of one operation disables the applicability of another one. Consequently, one operation has to be omitted to construct a valid merged model in such scenarios. When having to decide which operation to omit, the importance of its application has to be taken into account depending on the operation type and the application context. However, existing works treat the operations to merge with equal importance. We introduce in this paper, for the first time, a multi-objective formulation of the problem of model merging, based on NSGA-II, that aims to find the best trade-off between minimizing the number of omitted operations and maximizing the number of successfully applied important operations. We evaluated our approach using seven open source systems and compared it with different existing model merging approaches. The merging solutions obtained with our approach were found in all of the scenarios of our experiments to be comparable in terms of minimizing the number of conflicts to those suggested by existing approaches and to carry a high importance score of merged operations. Our results also revealed an interesting feature concerning the trade-off between the two conflicting objectives that demonstrates the practical value of taking the importance of operations into account in model merging tasks. In fact, the shape of the Pareto front represents an interesting guidance for developers to select best solutions based on their preferences. © 2014 Elsevier Inc. All rights res

关键词： multiobjective optimization

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Review: Coastal groundwater optimization-advances, challenges, and practical solutions

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HYDROGEOLOGY JOURNAL 2015年第6期23卷 1129-1154页

作者： Ketabchi, Hamed Ataie-Ashtiani, Behzad Sharif Univ Technol Dept Civil Engn Tehran Iran

Decision models are essential tools for coastal groundwater management (CGM). A combined simulation-optimization framework is employed to develop these models. One of the main barriers in the widespread application of these models for real-world cases is their large computational burden. Recent advances in efficient computational approaches and robust optimization methods can crack this barrier. This study surveys the scientific basis of CGM to provide an overview on this subject and reviews the-state-of-the-art to clarify recent developments and to outline ideas for improving the computational performance. Key details are presented on the performance and choice of possible robust tools such as efficient evolutionary algorithms (EAs), surrogate models, and parallel processing techniques. Then, the potential challenges remaining in this context are scrutinized, demonstrating open fields for further research, which include issues related to advances in simulating and optimizing phases such as introducing new robust algorithms and considering multi-objective purposes, implementing novel and high-performance tools, considering global concerns (e.g. climate change impacts), enhancing the existing models to fit the real world, and taking into account the complexities of real-world applications (e.g. uncertainties in the modeling parameters, and data acquisition). Finally, the outcomes of the systematic review are applied to solve a real-world CGM problem in Iran, to quantitatively examine the performance of combined implementation of some of the suggested tools. It is revealed that the required computational time is considerably reduced by as much as three orders of magnitude when correct combinations of robust EAs, surrogate model, and parallelization technique are utilized.

关键词： Coastal groundwater optimization Evolutionary algorithms Iran Parallel processing

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Time and energy based assignment strategy for unit-load AS/RS warehouses 23

Time and energy based assignment strategy for unit-load AS/R...

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23rd International Conference for Production Research, ICPR 2015

作者： Accorsi, Riccardo Bortolini, Marco Faccio, Maurizio Gamberi, Mauro Manzini, Riccardo Pilati, Francesco Department of Industrial Engineering University of Bologna Viale del Risorgimento 2 Bologna40136 Italy Department of Management and Engineering Univeristy of Padova Stradella San Nicola 3 Vicenza36100 Italy

Effectiveness in warehouse design and operations is crucial for the industrial companies to be competitive in the market arena by reducing the response time and inbound costs, increasing their global service level. Storage assignment deals with the definition of effective strategies to organise items into industrial warehouses to achieve high performances. This paper enhances the conventional approaches on storage assignment proposing a time and energy based strategy, for single-deep rack automatic warehouses served by aisle captive AS/RSs, based on the joint minimisation of the travel time and the energy required by the cranes to S/R the unit-loads. The models to compute the expected single-command cycle time and energy are integrated into a multi-objective model, optimising the load assignment. An application, taken from the beverage industry, is, finally, discussed. The different perspectives of adopting time and energy to drive the load assignment are stressed proposing a practical best trade-off rule.

关键词： multiobjective optimization

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