0000000000122522

AUTHOR

Mohammad Tabatabaei

showing 3 related works from this author

ANOVA-MOP: ANOVA Decomposition for Multiobjective Optimization

2018

Real-world optimization problems may involve a number of computationally expensive functions with a large number of input variables. Metamodel-based optimization methods can reduce the computational costs of evaluating expensive functions, but this does not reduce the dimension of the search domain nor mitigate the curse of dimensionality effects. The dimension of the search domain can be reduced by functional anova decomposition involving Sobol' sensitivity indices. This approach allows one to rank decision variables according to their impact on the objective function values. On the basis of the sparsity of effects principle, typically only a small number of decision variables significantl…

Pareto optimality0209 industrial biotechnologyMathematical optimizationOptimization problempäätöksenteko0211 other engineering and technologies02 engineering and technologyMulti-objective optimizationdecision makingTheoretical Computer Science020901 industrial engineering & automationsensitivity analysisDecomposition (computer science)multiple criteria optimizationdimensionality reductionMathematicsta113021103 operations researchpareto-tehokkuusDimensionality reductionta111metamodelingmonitavoiteoptimointiMetamodelingOptimization methodsSoftwareSIAM Journal on Optimization
researchProduct

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
researchProduct

An interactive surrogate-based method for computationally expensive multiobjective optimisation

2019

Many disciplines involve computationally expensive multiobjective optimisation problems. Surrogate-based methods are commonly used in the literature to alleviate the computational cost. In this paper, we develop an interactive surrogate-based method called SURROGATE-ASF to solve computationally expensive multiobjective optimisation problems. This method employs preference information of a decision-maker. Numerical results demonstrate that SURROGATE-ASF efficiently provides preferred solutions for a decision-maker. It can handle different types of problems involving for example multimodal objective functions and nonconvex and/or disconnected Pareto frontiers. peerReviewed

black-box functionsMathematicsofComputing_NUMERICALANALYSISmetamodeling techniquesachievement scalarising functioninteractive methodsmatemaattinen optimointimultiple criteria decision-making (MCDM)computational costmonitavoiteoptimointi
researchProduct