Search results for "Multiobjective Optimization"

showing 10 items of 71 documents

Interactive decision support and trade-off analysis for sustainable forest landscape planning under deep uncertainty

2022

Sustainable environmental management often involves long-term time horizons and multiple conflicting objectives and, by nature, is affected by different sources of uncertainty. Many sources of uncertainty, such as climate change or government policies, cannot be addressed using probabilistic models, and, therefore, they can be seen to contain deep uncertainty. In this setting, the variety of possible future states is represented as a set of scenarios lacking any information about the likelihood of occurring. Integrating deep uncertainty into multiobjective decision support increases complexity, calling for the elaboration of appropriate methods and tools. This paper proposes a novel intera…

Global and Planetary Changescenario planningpartially known preferencestodennäköisyyslaskentaEcologykestävä kehitysympäristömetsäalapäätöksentekoforest managementForestryilmastonmuutoksetskenaariotmonitavoiteoptimointimetsätympäristön tilaclimate changestrateginen suunnittelumultiobjective optimizationtulevaisuusmetsänhoitotodennäköisyys
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Constrained Robust MultiObjective Optimization for Reactive Design in Distribution Systems

2006

This paper presents a new formulation including robustness of solution of constrained multiobjective design or reactive power compensation. The algorithm used for optimization is the NSGA-II (Non dominated Sorting Genetic Algorithm II) with a special crowded comparison operator for constraints handling. The need for including the issue of robustness of solutions derives from the simple observation that loads are uncertain in distribution systems and their estimation is often affected by errors. In design problems it is desirable to consider the loads with a certain range of variation. In this paper the NSGA-II algorithm is applied to efficiently solve the issue and the solutions attained co…

Settore ING-IND/33 - Sistemi Elettrici Per L'EnergiaDistribution systemMathematical optimizationDistribution networksRobustness (computer science)Stochastic processControl theoryGenetic algorithmOptimal reactive power design Multiobjective optimization robust optimization distribution systemsRobust optimizationAC powerMulti-objective optimizationMathematics2006 International Conference on Probabilistic Methods Applied to Power Systems
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Towards resilient cities: Advancements allowed by a multi-criteria optimization tool to face the new challenges of European Union’s climate and energ…

2019

Abstract The United Nations as well as the European Union are strongly committed in promoting a transition towards more sustainable and resilient cities. Indeed, they are increasingly affected by different types of threats, among which the natural ones such as earthquakes, fires, and floods (shocks) and climate variability (stresses). Cities are quite often unable to cope with the adverse effects of such natural hazards. This circumstance leads to the need of introducing resilience-related criteria (besides commonly used sustainability indicators) in decision-making processes. This paper investigates at which extent the inclusion of such new indicators, within multi-criteria assessment tool…

green roofEnergy (esotericism)cool roofGreen roofFace (sociological concept)Indoor air pollutionHistoric preservationEnergy conservationmulticriteria assessmentEnvironmental protectionMulti criteriaSustainable developmentmedia_common.cataloged_instanceEuropean unionResilience (network)Concrete pavementsEnvironmental planningmedia_commonMultiobjective optimizationResiliencePublic administrationAir quality; Concrete pavements; Decision making; Energy conservation; Environmental protection; Historic preservation; Indoor air pollution; Multiobjective optimization; Public administration; Sustainable developmentAHP methodAir qualityBusinesspriority-setting process.Decision making
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Optimistic NAUTILUS navigator for multiobjective optimization with costly function evaluations

2022

AbstractWe introduce novel concepts to solve multiobjective optimization problems involving (computationally) expensive function evaluations and propose a new interactive method called O-NAUTILUS. It combines ideas of trade-off free search and navigation (where a decision maker sees changes in objective function values in real time) and extends the NAUTILUS Navigator method to surrogate-assisted optimization. Importantly, it utilizes uncertainty quantification from surrogate models like Kriging or properties like Lipschitz continuity to approximate a so-called optimistic Pareto optimal set. This enables the decision maker to search in unexplored parts of the Pareto optimal set and requires …

Control and Optimizationdecision makersApplied Mathematicspäätöksentekopreference informationManagement Science and Operations Researchinteractive methodsmonitavoiteoptimointiComputer Science ApplicationsoptimointiBusiness Management and Accounting (miscellaneous)multiobjective optimization problemskrigingmallit (mallintaminen)kriging-menetelmäcomputational cost
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A Multiple Surrogate Assisted Decomposition Based Evolutionary Algorithm for Expensive Multi/Many-Objective Optimization

2019

Many-objective optimization problems (MaOPs) contain four or more conflicting objectives to be optimized. A number of efficient decomposition-based evolutionary algorithms have been developed in the recent years to solve them. However, computationally expensive MaOPs have been scarcely investigated. Typically, surrogate-assisted methods have been used in the literature to tackle computationally expensive problems, but such studies have largely focused on problems with 1–3 objectives. In this paper, we present an approach called hybrid surrogate-assisted many-objective evolutionary algorithm to solve computationally expensive MaOPs. The key features of the approach include: 1) the use of mul…

metamodelsmultiprotocol label switchingmultiobjective optimizationevoluutiolaskentareference vectorscomputational costmonitavoiteoptimointi
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Decision making in multiobjective optimization problems under uncertainty: balancing between robustness and quality

2018

As an emerging research field, multiobjective robust optimization employs minmax robustness as the most commonly used concept. Light robustness is a concept in which a parameter, tolerable degradations, can be used to control the loss in the objective function values in the most typical scenario for gaining in robustness. In this paper, we develop a lightly robust interactive multiobjective optimization method, LiRoMo, to support a decision maker to find a most preferred lightly robust efficient solution with a good balance between robustness and the objective function values in the most typical scenario. In LiRoMo, we formulate a lightly robust subproblem utilizing an achievement scalarizi…

Mathematical optimizationdecision supportOptimization problemmultiobjective robust optimizationComputer sciencepäätöksenteko0211 other engineering and technologies02 engineering and technologyManagement Science and Operations ResearchMulti-objective optimizationoptimointiRobustness (computer science)0502 economics and business050210 logistics & transportation021103 operations research05 social scienceslight robust efficiencyRobust optimizationinteractive methodshandling uncertaintyDecision makerMinimaxmonitavoiteoptimointiepävarmuusVisualizationMultiobjective optimization problemtrade-off between robustness and qualityBusiness Management and Accounting (miscellaneous)OR Spectrum
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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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Interactive Nonlinear Multiobjective Optimization Methods

2016

An overview of interactive methods for solving nonlinear multiobjective optimization problems is given. In interactive methods, the decision maker progressively provides preference information so that the most satisfactory Pareto optimal solution can be found for her or his. The basic features of several methods are introduced and some theoretical results are provided. In addition, references to modifications and applications as well as to other methods are indicated. As the role of the decision maker is very important in interactive methods, methods presented are classified according to the type of preference information that the decision maker is assumed to provide. peerReviewed

Pareto optimalityMathematical optimization021103 operations researchComputer sciencemultiple criteria decision making0211 other engineering and technologies02 engineering and technologyinteractive methodsDecision makernonlinear optimizationMulti-objective optimizationPreferenceNonlinear programmingPareto optimalNonlinear systemMultiobjective optimization problemmultiple objectives0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processing
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A Surrogate-assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-objective Optimization

2018

We propose a surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive optimization problems with more than three objectives. The proposed algorithm is based on a recently developed evolutionary algorithm for many-objective optimization that relies on a set of adaptive reference vectors for selection. The proposed surrogateassisted evolutionary algorithm uses Kriging to approximate each objective function to reduce the computational cost. In managing the Kriging models, the algorithm focuses on the balance of diversity and convergence by making use of the uncertainty information in the approximated objective values given by the Kriging models, the distr…

Pareto optimalityPareto-tehokkuus0209 industrial biotechnologyMathematical optimizationOptimization problemComputer sciencemodel managementpäätöksentekoEvolutionary algorithmInteractive evolutionary computation02 engineering and technologyEvolutionary computationTheoretical Computer Science020901 industrial engineering & automationKrigingalgoritmit0202 electrical engineering electronic engineering information engineeringvektorit (matematiikka)multiobjective optimizationcomputational costsurrogate-assisted evolutionary algorithmsBayesian optimizationta113Cultural algorithmpareto-tehokkuusbayesilainen menetelmäta111Approximation algorithmImperialist competitive algorithmmonitavoiteoptimointiKrigingkoneoppiminenComputational Theory and Mathematics020201 artificial intelligence & image processingreference vectorsSoftwareIEEE Transactions on Evolutionary Computation
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A survey on handling computationally expensive multiobjective optimization problems using surrogates: non-nature inspired methods

2015

Computationally expensive multiobjective optimization problems arise, e.g. in many engineering applications, where several conflicting objectives are to be optimized simultaneously while satisfying constraints. In many cases, the lack of explicit mathematical formulas of the objectives and constraints may necessitate conducting computationally expensive and time-consuming experiments and/or simulations. As another challenge, these problems may have either convex or nonconvex or even disconnected Pareto frontier consisting of Pareto optimal solutions. Because of the existence of many such solutions, typically, a decision maker is required to select the most preferred one. In order to deal wi…

Mathematical optimizationEngineeringControl and Optimizationbusiness.industryPareto principlePareto frontierDecision makerSampling techniqueComputer Graphics and Computer-Aided DesignMulti-objective optimizationComputer Science ApplicationsMultiobjective optimization problemPareto optimalConflicting objectivesBlack-box functionControl and Systems EngineeringMulticriteria Decision Making (MCDM)Computational costNature inspiredMetamodeling techniquebusinessEngineering design processSoftwareStructural and Multidisciplinary Optimization
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