0000000000583232

AUTHOR

Sauli Ruuska

showing 3 related works from this author

A New Hybrid Mutation Operator for Multiobjective Optimization with Differential Evolution

2011

Differential evolution has become one of the most widely used evolution- ary algorithms in multiobjective optimization. Its linear mutation operator is a sim- ple and powerful mechanism to generate trial vectors. However, the performance of the mutation operator can be improved by including a nonlinear part. In this pa- per, we propose a new hybrid mutation operator consisting of a polynomial based operator with nonlinear curve tracking capabilities and the differential evolution’s original mutation operator, to be efficiently able to handle various interdependencies between decision variables. The resulting hybrid operator is straightforward to implement and can be used within most evoluti…

Pareto optimalityMathematical optimizationEvolutionary algorithmComputational intelligenceMOEA/DNonlinearGenetic operatorEvolutionary algorithmsMulti-objective optimizationPolynomialTheoretical Computer ScienceDEOperator (computer programming)Evolutionary algorithms; DE; Nonlinear; Multi-criteria optimization; Polynomial; Pareto optimality; MOEA/DPareto-optimaalisuusMathematicsMatematikMulti-criteria optimizationState (functional analysis)monitavoiteoptimointiNonlinear systemDifferential evolutionGeometry and TopologyAlgorithmSoftwareMathematics
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A solution process for simulation-based multiobjective design optimization with an application in the paper industry

2014

In this paper, we address some computational challenges arising in complex simulation-based design optimization problems. High computational cost, black-box formulation and stochasticity are some of the challenges related to optimization of design problems involving the simulation of complex mathematical models. Solving becomes even more challenging in case of multiple conflicting objectives that must be optimized simultaneously. In such cases, application of multiobjective optimization methods is necessary in order to gain an understanding of which design offers the best possible trade-off. We apply a three-stage solution process to meet the challenges mentioned above. As our case study, w…

Pareto optimalityEngineeringMathematical optimizationIntegrated designOptimization problemMathematical modelbusiness.industrymedia_common.quotation_subjectControl (management)ta111Computer Graphics and Computer-Aided DesignMulti-objective optimizationIndustrial and Manufacturing EngineeringPAINT methodComputer Science ApplicationsSet (abstract data type)Multicriteria decision makingQuality (business)multiobjective optimizationNIMBUS methodbusinessSimulation basedcomputational costmedia_commonComputer-Aided Design
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Connections Between Single-Level and Bilevel Multiobjective Optimization

2011

The relationship between bilevel optimization and multiobjective optimization has been studied by several authors and there have been repeated attempts to establish a link between the two. We unify the results from the literature and generalize them for bilevel multiobjective optimization. We formulate sufficient conditions for an arbitrary binary relation to guarantee equality between the efficient set produced by the relation and the set of optimal solutions to a bilevel problem. In addition, we present specially structured bilevel multiobjective optimization problems motivated by real-life applications and an accompanying binary relation permitting their reduction to single-level multiob…

bilevel optimizationMathematical optimizationMatematikControl and OptimizationRelation (database)Multiobjective programmingBinary relationTwo-level optimizationApplied MathematicsMulticriteriaManagement Science and Operations ResearchSingle levelmonitavoiteoptimointiMulti-objective optimizationBilevel optimizationSet (abstract data type)Reduction (complexity)Theory of computationmultiobjective optimizationMathematicsMathematics
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