Search results for "Engineering optimization"

showing 8 items of 8 documents

Constraint handling in efficient global optimization

2017

Real-world optimization problems are often subject to several constraints which are expensive to evaluate in terms of cost or time. Although a lot of effort is devoted to make use of surrogate models for expensive optimization tasks, not many strong surrogate-assisted algorithms can address the challenging constrained problems. Efficient Global Optimization (EGO) is a Kriging-based surrogate-assisted algorithm. It was originally proposed to address unconstrained problems and later was modified to solve constrained problems. However, these type of algorithms still suffer from several issues, mainly: (1) early stagnation, (2) problems with multiple active constraints and (3) frequent crashes.…

Mathematical optimizationConstraint optimizationOptimization problemL-reduction0211 other engineering and technologiesGaussian processes02 engineering and technologyexpensive optimizationMulti-objective optimizationEngineering optimizationSurrogate modelsKriging0202 electrical engineering electronic engineering information engineeringMulti-swarm optimizationGlobal optimization/dk/atira/pure/subjectarea/asjc/1700/1712constraint optimizationMathematicsta113EGO/dk/atira/pure/subjectarea/asjc/1700/1706Expensive optimization021103 operations researchConstrained optimizationComputer Science Applicationssurrogate modelsKrigingComputational Theory and Mathematics020201 artificial intelligence & image processing/dk/atira/pure/subjectarea/asjc/1700/1703SoftwareProceedings of the Genetic and Evolutionary Computation Conference
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Surrogate-assisted multicriteria optimization: Complexities, prospective solutions, and business case

2017

Complexity in solving real-world multicriteria optimization problems often stems from the fact that complex, expensive, and/or time-consuming simulation tools or physical experiments are used to evaluate solutions to a problem. In such settings, it is common to use efficient computational models, often known as surrogates or metamodels, to approximate the outcome (objective or constraint function value) of a simulation or physical experiment. The presence of multiple objective functions poses an additional layer of complexity for surrogate-assisted optimization. For example, complexities may relate to the appropriate selection of metamodels for the individual objective functions, extensive …

optimization problemsMathematical optimizationComputer scienceStrategy and Managementmedia_common.quotation_subjectConstraint (computer-aided design)0211 other engineering and technologiesmultiple criteria decision makingGeneral Decision Sciences02 engineering and technologyMulti-objective optimizationOutcome (game theory)evolutionary multicriteria optimizationEngineering optimizationmulticriteria optimization0202 electrical engineering electronic engineering information engineeringPoint (geometry)Business caseFunction (engineering)media_commonta113Computational model021103 operations researchmetamodelsexpensive optimization problemssurrogatesexpensesmachine learning020201 artificial intelligence & image processing
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Black box scatter search for general classes of binary optimization problems

2010

The purpose of this paper is to apply the scatter search methodology to general classes of binary problems. We focus on optimization problems for which the solutions are represented as binary vectors and that may or may not include constraints. Binary problems arise in a variety of settings, including engineering design and statistical mechanics (e.g., the spin glass problem). A distinction is made between two sets of general constraint types that are handled directly by the solver and other constraints that are addressed via penalty functions. In both cases, however, the heuristic treats the objective function evaluation as a black box. We perform computational experiments with four well-k…

Continuous optimizationMathematical optimizationOptimization problemGeneral Computer ScienceL-reductionManagement Science and Operations ResearchMulti-objective optimizationEngineering optimizationVector optimizationModeling and SimulationPenalty methodAlgorithmMetaheuristicMathematicsComputers & Operations Research
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Simultaneous and multi-criteria optimization of TS requirements and maintenance at NPPs

2002

Abstract One of the main concerns of the nuclear industry is to improve the availability of safety-related systems at nuclear power plants (NPPs) to achieve high safety levels. The development of efficient testing and maintenance has been traditionally one of the different ways to guarantee high levels of systems availability, which are implemented at NPP through technical specification and maintenance requirements (TS&M). On the other hand, there is a widely recognized interest in using the probabilistic risk analysis (PRA) for risk-informed applications aimed to emphasize both effective risk control and effective resource expenditures at NPPs. TS&M-related parameters in a plant are associ…

Mathematical optimizationMeta-optimizationOptimization problemNuclear Energy and EngineeringComputer scienceProbabilistic-based design optimizationMulti-swarm optimizationMulti-objective optimizationBilevel optimizationMetaheuristicEngineering optimizationAnnals of Nuclear Energy
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Coupling dynamic simulation and interactive multiobjective optimization for complex problems: An APROS-NIMBUS case study

2014

Dynamic process simulators for plant-wide process simulation and multiobjective optimization tools can be used by industries as a means to cut costs and enhance profitability. Specifically, dynamic process simulators are useful in the process plant design phase, as they provide several benefits such as savings in time and costs. On the other hand, multiobjective optimization tools are useful in obtaining the best possible process designs when multiple conflicting objectives are to be optimized simultaneously. Here we concentrate on interactive multiobjective optimization. When multiobjective optimization methods are used in process design, they need an access to dynamic process simulators, …

implementation challengesMathematical optimizationOptimization problemProcess (engineering)Computer scienceta111General Engineeringaugmented interactive multiobjective optimization algorithminteractive methodMulti-objective optimizationComputer Science ApplicationsEngineering optimizationSeparation processDynamic simulationSimulation-based optimizationIND-NIMBUSArtificial Intelligencedynamic process simulationApache ThriftPareto optimal solutionsProcess simulationsimulation based optimizationExpert Systems with Applications
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Pareto-optimal Glowworm Swarms Optimization for Smart Grids Management

2013

This paper presents a novel nature-inspired multi-objective optimization algorithm. The method extends the glowworm swarm particles optimization algorithm with algorithmical enhancements which allow to identify optimal pareto front in the objectives space. In addition, the system allows to specify constraining functions which are needed in practical applications. The framework has been applied to the power dispatch problem of distribution systems including Distributed Energy Resources (DER). Results for the test cases are reported and discussed elucidating both numerical and complexity analysis.

Mathematical optimizationMeta-optimizationComputer scienceDerivative-free optimizationTest functions for optimizationSwarm behaviourMulti-swarm optimizationevolutionary optimization swarm-optimization pareto optimization micro-gridsMulti-objective optimizationMetaheuristicEngineering optimization
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Integrated Computer-Aided Innovation: The PROSIT approach

2009

Abstract The paper presents a methodology aimed at the improvement of the product development cycle through the integration of Computer-Aided Innovation (CAI) with Optimization and PLM systems. The interoperability of these tools is obtained through the adoption of Optimization systems as a bridging element between CAI and PLM systems. This methodology was developed within the PROSIT project ( http://www.kaemart.it/prosit ). The paper describes the main issues related to the integration of these complementary instruments and the solutions proposed by the authors. More specifically, the main idea of the PROSIT project to link CAI and Optimization systems is the adoption of the latter tools n…

Decision support systemGeneral Computer Sciencebusiness.industryComputer scienceKnowledge-based engineeringInteroperabilityKnowledge engineeringGeneral Engineeringlaw.inventionKnowledge baselawNew product developmentComputer-Aided Innovation Knowledge-Based Engineering Optimization systems Product Lifecycle ManagementSystems engineeringTRIZElectronic design automationbusinessSettore ING-IND/15 - Disegno E Metodi Dell'Ingegneria Industriale
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Optimal design algorithm for seismic retrofitting of RC columns with steel jacketing technique

2020

Abstract Steel jacketing (SJ) of beams and columns is widely employed as retrofitting technique to provide additional deformation and strength capacity to existing reinforced concrete (RC) frame structures. The latter are many times designed without considering seismic loads, or present inadequate seismic detailing. The use of SJ is generally associated with non-negligible costs depending on the amount of structural work and non-structural manufacturing and materials. Moreover, this kind of intervention results in noticeable downtime for the building. This paper presents a new optimization framework which is aimed at obtaining minimization of retrofitting costs by optimizing the position an…

Optimal design0209 industrial biotechnologyDowntimeComputer scienceOpenSEESSeismic loadingsteel jacketing02 engineering and technologyIndustrial and Manufacturing EngineeringEngineering optimizationSettore ICAR/09 - Tecnica Delle Costruzioni020303 mechanical engineering & transports020901 industrial engineering & automationOpenSees0203 mechanical engineeringArtificial IntelligenceconfinementGenetic algorithmRetrofittingSeismic retrofitconfinement; OpenSEES; optimization; steel jacketing; stress-strain curvestress-strain curveAlgorithmoptimization
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