EMOSOR: Evolutionary multiple objective optimization guided by interactive stochastic ordinal regression

Tomczyk, Michał K.; Kadziński, Miłosz

doi:10.1016/j.cor.2019.04.008

Cited by 21 publications

(2 citation statements)

References 39 publications

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“…• A BRSD: this is the average distance of the best solution in the final population from the optimal solution P b .The distance, denoted by BRSD(U), is computed only for the simulations in which the algorithm was not able to discover P b (in the case in which the algorithm is able to discover P b the distance is zero). Denoting by P Best the best solution in the final population, following [77], BRSD(U) is computed as…”

Section: Experimental Setup and Numerical Resultsmentioning

confidence: 99%

Multiobjective Combinatorial Optimization with Interactive Evolutionary Algorithms: the case of facility location problems

Barbati¹,

Corrente²,

Greco³

2022

Preprint

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We consider multiobjective combinatorial optimization problems handled by means of preference driven efficient heuristics. They look for the most preferred part of the Pareto front on the basis of some preferences expressed by the Decision Maker during the process. In general, what is searched for in this case is the Pareto set of efficient solutions. This is a problem much more difficult than optimizing a single objective function. Moreover, obtaining the Pareto set does not mean that the decision problem is solved since one or some of the solutions have to be chosen. Indeed, to make a decision, it is necessary to determine the most preferred solution in the Pareto set, so that it is also necessary to elicit the preferences of the user. In this perspective, what we are proposing can be seen as the first structured methodology in facility location problems to search optimal solutions taking into account preferences of the user. With this aim, we approach facility location problems using a recently proposed interactive evolutionary multiobjective optimization procedure called NEMO-II-Ch. NEMO-II-Ch is applied to a real world multiobjective location problem with many users and many facilities to be located. Several simulations considering different fictitious users have been performed. The results obtained by NEMO-II-Ch are compared with those got by three algorithms which know the user's true value function that is, instead, unknown to NEMO-II-Ch. They show that in many cases NEMO-II-Ch finds the best subset of locations more quickly than the methods knowing, exactly, the whole user's true preferences.

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Section: Experimental Setup and Numerical Resultsmentioning

confidence: 99%

Multiobjective Combinatorial Optimization with Interactive Evolutionary Algorithms: the case of facility location problems

Barbati¹,

Corrente²,

Greco³

2022

Preprint

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“…As developers are unwilling or unable to evaluate alternatives precisely, the target EC values of the considered alternatives are often expressed within large bounds of uncer-tainty. The corresponding multi-attribute decision-making models have been employed to cope with the uncertainty problem, such as stochastic ordinal regression method [53], stochastic dominance [54], multi-attribute decision-making approach based on stochastic dominance [55], and stochastic multi-attribute acceptability analysis [56]. These approaches address the distribution problem of the target EC values.…”

Section: Selecting Product Conceptsmentioning

confidence: 99%

An integrated quality-function-deployment and stochastic-dominance-based decision-making approach for prioritizing product concept alternatives

Wang

Chen

Garg

et al. 2022

Complex Intell. Syst.

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For continuous growth and sustaining the competitiveness of a company, product developers spend most of their time making crucial decisions to address a great variety of unpredictable and uncontrollable information. Several mathematical approaches have already been adopted to aid the developers in selecting the best product concept for meeting customers’ requirements and exceeding their expectations. However, those methods do not cope with fully revealed developers’ preferences and do not take into account the random distribution of the target values of engineering characteristics (ECs). In this paper, the application of a quality function deployment (QFD)-based model and a stochastic dominance-based method is presented for product concept development. The first phase in the approach is to construct a product planning house of quality (PPHoQ), which is the core and the engine of the entire QFD model. This model depicts the relationship between the customers’ requirements (CRs) and the ECs for a product. The proposed approach addresses both the relationships between CRs and ECs, in addition to the correlations among the ECs. In this study, developers are invited to express their preferences using different types of linguistic terms dependent on their diverse backgrounds and understanding levels of the product. Based on the outcomes of the PPHoQ process, a variety of alternative concepts can be created. The alternatives are then prioritized and ranked in the second phase. The proposed approach facilitates the random distribution with stochastic variables rather than fuzzy methods to obtain more realistic product concept alternatives. Several examples and comparative results further illustrate that unbalanced linguistic terms and stochastic dominance efficiently endow the product concept selection model with uncertain information and the random distribution in a realistic style.

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The Use of Decision Maker’s Preferences in Multiobjective Metaheuristics

Branke,

Jaszkiewicz,

Zielniewicz

2022

Intelligent Decision Support Systems

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EMOSOR: Evolutionary multiple objective optimization guided by interactive stochastic ordinal regression

Cited by 21 publications

References 39 publications

Multiobjective Combinatorial Optimization with Interactive Evolutionary Algorithms: the case of facility location problems

Multiobjective Combinatorial Optimization with Interactive Evolutionary Algorithms: the case of facility location problems

An integrated quality-function-deployment and stochastic-dominance-based decision-making approach for prioritizing product concept alternatives

The Use of Decision Maker’s Preferences in Multiobjective Metaheuristics

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