Search results for "Multi-Objective Optimization"

showing 10 items of 192 documents

Synchronous approach in interactive multiobjective optimization

2006

We introduce a new approach in the methodology development for interactive multiobjective optimization. The presentation is given in the context of the interactive NIMBUS method, where the solution process is based on the classification of objective functions. The idea is to formulate several scalarizing functions, all using the same preference information of the decision maker. Thus, opposed to fixing one scalarizing function (as is done in most methods), we utilize several scalarizing functions in a synchronous way. This means that we as method developers do not make the choice between different scalarizing functions but calculate the results of different scalarizing functions and leave t…

Mathematical optimizationInformation Systems and ManagementGeneral Computer ScienceComputer sciencebusiness.industrymedia_common.quotation_subjectContext (language use)Management Science and Operations ResearchMultiple-criteria decision analysisMulti-objective optimizationIndustrial and Manufacturing EngineeringNonlinear programmingNonlinear systemModeling and SimulationSoftware systemArtificial intelligenceFunction (engineering)businessmedia_commonEuropean Journal of Operational Research
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Experiments with classification-based scalarizing functions in interactive multiobjective optimization

2006

In multiobjective optimization methods, the multiple conflicting objectives are typically converted into a single objective optimization problem with the help of scalarizing functions and such functions may be constructed in many ways. We compare both theoretically and numerically the performance of three classification-based scalarizing functions and pay attention to how well they obey the classification information. In particular, we devote special interest to the differences the scalarizing functions have in the computational cost of guaranteeing Pareto optimality. It turns out that scalarizing functions with or without so-called augmentation terms have significant differences in this re…

Mathematical optimizationInformation Systems and ManagementGeneral Computer SciencePareto principleManagement Science and Operations ResearchMulti-objective optimizationMultiple objective programmingIndustrial and Manufacturing EngineeringSet (abstract data type)Nonlinear systemSingle objective optimization problemConflicting objectivesModeling and SimulationBenchmark (computing)MathematicsEuropean Journal of Operational Research
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Using box indices in supporting comparison in multiobjective optimization

2009

Because of the conflicting nature of criteria or objectives, solving a multiobjective optimization problem typically requires interaction with a decision maker who can specify preference information related to the objectives in the problem in question. Due to the difficulties of dealing with multiple objectives, the way information is presented plays a very important role. Questions posed to the decision maker must be simple enough and information shown must be easy to understand. For this purpose, visualization and graphical representations can be useful and constitute one of the main tools used in the literature. In this paper, we propose to use box indices to represent information relate…

Mathematical optimizationInformation Systems and ManagementGeneral Computer Sciencebusiness.industryScale (chemistry)Information and Computer ScienceManagement Science and Operations ResearchMachine learningcomputer.software_genreMultiple-criteria decision analysisMulti-objective optimizationIndustrial and Manufacturing EngineeringPreferenceVisualizationSimple (abstract algebra)Modeling and SimulationArtificial intelligenceGraphicsbusinesscomputerMathematicsEuropean Journal of Operational Research
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NAUTILUS method: An interactive technique in multiobjective optimization based on the nadir point

2010

Most interactive methods developed for solving multiobjective optimization problems sequentially generate Pareto optimal or nondominated vectors and the decision maker must always allow impairment in at least one objective function to get a new solution. The NAUTILUS method proposed is based on the assumptions that past experiences affect decision makers’ hopes and that people do not react symmetrically to gains and losses. Therefore, some decision makers may prefer to start from the worst possible objective values and to improve every objective step by step according to their preferences. In NAUTILUS, starting from the nadir point, a solution is obtained at each iteration which dominates t…

Mathematical optimizationInformation Systems and ManagementInteractive programmingGeneral Computer Sciencebiologymedia_common.quotation_subjectManagement Science and Operations Researchbiology.organism_classificationMulti-objective optimizationIndustrial and Manufacturing EngineeringSightNegotiationIterated functionModeling and SimulationMinificationNautilusOptimal decisionMathematicsmedia_commonEuropean Journal of Operational Research
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Interactive Nonconvex Pareto Navigator for Multiobjective Optimization

2019

Abstract We introduce a new interactive multiobjective optimization method operating in the objective space called Nonconvex Pareto Navigator . It extends the Pareto Navigator method for nonconvex problems. An approximation of the Pareto optimal front in the objective space is first generated with the PAINT method using a relatively small set of Pareto optimal outcomes that is assumed to be given or computed prior to the interaction with the decision maker. The decision maker can then navigate on the approximation and direct the search for interesting regions in the objective space. In this way, the decision maker can conveniently learn about the interdependencies between the conflicting ob…

Mathematical optimizationInformation Systems and Managementinteractive multiobjective optimizationGeneral Computer ScienceComputer science0211 other engineering and technologies02 engineering and technologyManagement Science and Operations ResearchSpace (commercial competition)Multi-objective optimizationIndustrial and Manufacturing Engineering0502 economics and businessnonconvex problemsnavigationta113050210 logistics & transportation021103 operations researchpareto-tehokkuuspareto optimality05 social sciencesPareto principlemonitavoiteoptimointinavigointiModeling and Simulationmultiple objective programmingEuropean Journal of Operational Research
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Modelling energy storage systems using Fourier analysis: An application for smart grids optimal management

2014

In this paper, a new and efficient model for variables representation, named F-coding, in optimal power dispatch problems for smart electrical distribution grids is proposed. In particular, an application devoted to optimal energy dispatch of Distributed Energy Resources including ideal storage devices is here considered. Electrical energy storage systems, such as any other component that must meet an integral capacity constraint in optimal dispatch problems, have to show the same energy level at the beginning and at the end of the considered timeframe for operation. The use of zero-integral functions, such as sinusoidal functions, for the synthesis of the charge and discharge course of bat…

Mathematical optimizationIntegral constraintMulti-objective evolutionary algorithmbusiness.industryComputer scienceFourier analysiEconomic dispatchSmart gridsMulti-objective optimizationEnergy storageElectrical energy storage systemSettore ING-IND/33 - Sistemi Elettrici Per L'EnergiaSettore ING-IND/31 - ElettrotecnicaSmart gridDistributed generationComponent (UML)Optimal dispatch of resourcebusinessRepresentation (mathematics)SoftwareEnergy (signal processing)Applied Soft Computing
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Improving Computing Systems Automatic Multiobjective Optimization Through Meta-Optimization

2016

This paper presents the extension of framework for automatic design space exploration (FADSE) tool using a meta-optimization approach, which is used to improve the performance of design space exploration algorithms, by driving two different multiobjective meta-heuristics concurrently. More precisely, we selected two genetic multiobjective algorithms: 1) non-dominated sorting genetic algorithm-II and 2) strength Pareto evolutionary algorithm 2, that work together in order to improve both the solutions’ quality and the convergence speed. With the proposed improvements, we ran FADSE in order to optimize the hardware parameters’ values of the grid ALU processor (GAP) micro-architecture from a b…

Mathematical optimizationMeta-optimizationComputer scienceCycles per instructionDesign space explorationPareto principleSortingEvolutionary algorithm02 engineering and technologyComputer Graphics and Computer-Aided DesignMulti-objective optimization020202 computer hardware & architecture0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingAlgorithm designElectrical and Electronic EngineeringSoftwareIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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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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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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Developing Domain-Knowledge Evolutionary Algorithms for Network-on-Chip Application Mapping

2013

This paper addresses the Network-on-Chip (NoC) application mapping problem. This is an NP-hard problem that deals with the optimal topological placement of Intellectual Property cores onto the NoC tiles. Network-on-Chip application mapping Evolutionary Algorithms are developed, evaluated and optimized for minimizing the NoC communication energy. Two crossover and one mutation operators are proposed. It is analyzed how each optimization algorithm performs with every genetic operator, in terms of solution quality and convergence speed. Our proposed operators are compared with state-of-the-art genetic operators for permutation problems. Finally, the problem is approached in a multi-objective w…

Mathematical optimizationMutation operatorTheoretical computer scienceComputer Networks and CommunicationsComputer scienceQuality control and genetic algorithmsCrossoverEvolutionary algorithmGenetic operatorMulti-objective optimizationNetwork on a chipArtificial IntelligenceHardware and ArchitectureSimulated annealingGenetic algorithmGenetic representationSoftwareMicroprocessors and Microsystems
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