Using Choquet integral as preference model in interactive evolutionary multiobjective optimization

Branke, Juergen; Corrente, Salvatore; Greco, Salvatore; Słowiński, Roman; Zielniewicz, Piotr

doi:10.1016/j.ejor.2015.10.027

Cited by 93 publications

(43 citation statements)

References 34 publications

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“…They use Electre Tri as a Multi-Criteria Analysis approach. Recently, the authors focus on combining by Multi-Criteria Decision Analysis and multiobjective evolutionary algorithms to select the most preferred solution from the generated set [5,6]. In [3], the authors introduce the hash algorithm to push speediness and efficiency of ARM process with the aim of providing a faster mining.…”

Section: Literature Reviewmentioning

confidence: 99%

Towards an enhanced user’s preferences integration into ranking process using dominance approach

Mohammed

Balouki

Gadi

2017

Vietnam J Comput Sci

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User preference is very important in orienting data miner, and this is the reason why these user preferences are integrated in the mining process, where they are coupled with Association Rules Mining "ARM" Algorithms to select only Association Rules "ARs" that satisfy the user's wishes and expectations. Within this framework, several approaches were proposed to overcome some problems which persist with the traditional ARM algorithms mainly dimensionality phenomenon engendered by thresholding and the subjective choice of measures. "MDP REF Algorithm" is one of these approaches; it prunes, filters to select the relevant ARs, while "Rank-Sort-MDP REF

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Section: Literature Reviewmentioning

confidence: 99%

Towards an enhanced user’s preferences integration into ranking process using dominance approach

Mohammed

Balouki

Gadi

2017

Vietnam J Comput Sci

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“…In what follows, we focus on NEMO-0 and NEMO-II (see Branke et al 2015Branke et al , 2016) that will be extended in our proposal. We neglect NEMO-I (Branke et al 2015), because it needs to solve a prohibitively large number of Linear Programming (LP) problems, which makes it infeasible for dealing with real-world optimization problems.…”

Section: Nemomentioning

confidence: 99%

“…NEMO-I (Branke et al 2015) implements the same idea though with more general additive value functions. Recently, to reduce the computational complexity of NEMO-I, in NEMO-II (see Branke et al 2015Branke et al , 2016) the necessary relation has been replaced with the preference fronts obtained by iterative identification of potentially optimal solutions, i.e., individuals which are ranked first by at least one compatible value function. Moreover, NEMO-II adjusts the complexity of an assumed preference model to the pairwise comparisons provided by the DM, thus, starting the iterative process with a simple linear value function and switching to the Choquet integral (Choquet 1954) when the linear model is not expressive enough.…”

Section: Review Of Existing Value-based Multiple Objective Optimizatimentioning

confidence: 99%

“…-considering other preference models than an additive value function (e.g., an achievement scalarizing function); -implementing the idea of adaptive preference modeling that adjusts the complexity of the model to the provided preference information in the course of evolutionary search in the spirit of Branke et al (2016); -preserving elitism in the evolutionary search using measures derived from Stochastic Ordinal Regression (see Tervonen 2013 andMichalski 2016) which is based on Monte Carlo simulation rather than LP techniques; -adapting the proposed approaches to discrete (combinatorial) MOO. The 'W' prefix indicates the variants of REP-UT and AVG-UT which incorporate the DMs' weights already during the evolutionary search SD standard deviation …”

Section: Conclusion and Future Researchmentioning

confidence: 99%

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Interactive Evolutionary Multiple Objective Optimization for Group Decision Incorporating Value-based Preference Disaggregation Methods

Kadziński

Tomczyk

2016

Group Decis Negot

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We present a set of interactive evolutionary multiple objective optimization (MOO) methods, called NEMO-GROUP. All proposed approaches incorporate pairwise comparisons of several decision makers (DMs) into the evolutionary search, though evaluating the suitability of solutions for inclusion in the next population in different ways. The performance of algorithms is quantified with various convergence factors derived from the extensive computational tests on a set of benchmark problems. The best individuals and complete populations of solutions constructed by the proposed approaches are evaluated in terms of both utilitarian and egalitarian group value functions for different numbers of DMs. Our results indicate that more promising directions for optimization can be discovered when exploiting the set of value functions compatible with the DMs' preferences rather than selecting a single representative value function for each DM or all DMs considered jointly. We demonstrate that NEMO-GROUP is flexible enough to account for the weights assigned to the DMs. We also show that by appropriately adjusting the elicitation interval and starting generation of the elicitation, one could significantly decrease the number of pairwise comparisons the DMs need to perform to construct a satisfactory solution.

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“…However, many current interactive methods still depend on the preference model, which is used to identify the region of interest (Chaudhuri and Deb, 2010;Sinha et al, 2014) or refine the approximation of the Pareto front (Klamroth and Miettinen, 2008). Other studies built designer preference interactively by query to the designer (Pedro and Takahashi, 2013) or comparison of pairwise solutions by the designer (Branke et al, 2015;2016); in these schemes the designer is provided with only fractional information, instead of a big picture of the optimization potential in the current situation. The visualization of Pareto-optimal solutions is also often studied in specialized topics so that the designer can make decisions based on that visual information (Kollat and Reed, 2007;Blasco et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

A novel multi-objective optimization method for the pressurized reservoir in hydraulic robotics

Ouyang

Fan

Yang

et al. 2016

J. Zhejiang Univ. Sci. A

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Abstract:The pressurized reservoir is a closed hydraulic tank which plays a significant role in enhancing the capabilities of hydraulic driven robotics. The spring pressurized reservoir adopted in this paper requires comprehensive performance, such as weight, size, fluid volume, and pressure, which is hard to balance. A novel interactive multi-objective optimization approach, the feasible space tightening method, is proposed, which is efficient in solving complicated engineering design problems where multiple objectives are determined by multiple design variables. This method provides sufficient information to the designer by visualizing the performance trends within the feasible space as well as its relationship with the design variables. A step towards the final solution could be made by raising the threshold on performance indicators interactively, so that the feasible space is reduced and the remaining solutions are more preferred by the designer. With the help of this new method, the preferred solution of a spring pressurized reservoir is found. Practicability and efficiency are demonstrated in the optimal design process, where the solution is determined within four rounds of interaction between the designer and the optimization program. Tests on the designed prototype show good results.

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Using Choquet integral as preference model in interactive evolutionary multiobjective optimization

Cited by 93 publications

References 34 publications

Towards an enhanced user’s preferences integration into ranking process using dominance approach

Towards an enhanced user’s preferences integration into ranking process using dominance approach

Interactive Evolutionary Multiple Objective Optimization for Group Decision Incorporating Value-based Preference Disaggregation Methods

A novel multi-objective optimization method for the pressurized reservoir in hydraulic robotics

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