Preference Incorporation in Multi-objective Evolutionary Algorithms: A Survey

Rachmawati, Lily; Srinivasan, Dipti

doi:10.1109/cec.2006.1688414

Cited by 83 publications

(59 citation statements)

References 23 publications

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“…There are various studies that focus on the issue of preference articulation in EMO, in particular integrating additional preferences such as priorities, goals, and reference points [16], [4], [6], [2], [11], [25]. However, these studies mainly cover preferences on solutions and not preferences on sets.…”

Section: Introductionmentioning

confidence: 99%

On Set-Based Multiobjective Optimization

Zitzler

Thiele

Bader

2010

IEEE Trans. Evol. Computat.

156

104

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Abstract-Assuming that evolutionary multiobjective optimization (EMO) mainly deals with set problems, one can identify three core questions in this area of research: (i) how to formalize what type of Pareto set approximation is sought, (ii) how to use this information within an algorithm to efficiently search for a good Pareto set approximation, and (iii) how to compare the Pareto set approximations generated by different optimizers with respect to the formalized optimization goal. There is a vast amount of studies addressing these issues from different angles, but so far only few studies can be found that consider all questions under one roof. This paper is an attempt to summarize recent developments in the EMO field within a unifying theory of set-based multiobjective search. It discusses how preference relations on sets can be formally defined, gives examples for selected user preferences, and proposes a general, preference-independent hill climber for multiobjective optimization with theoretical convergence properties. Furthermore, it shows how to use set preference relations for statistical performance assessment and provides corresponding experimental results. The proposed methodology brings together preference articulation, algorithm design, and performance assessment under one framework and thereby opens up a new perspective on EMO.

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Section: Introductionmentioning

confidence: 99%

On Set-Based Multiobjective Optimization

Zitzler

Thiele

Bader

2010

IEEE Trans. Evol. Computat.

156

104

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“…The technique of progressively incorporating preferences into the multi-objective optimisation process is referred to as progressive preference articulation, see [14] and [49]. A comparative analysis of the various approaches of preference articulation is reported in [2] and in the talk [25].…”

Section: Progressive Preference Articulation: a Brief Reviewmentioning

confidence: 99%

Progressive preference articulation for decision making in multi-objective optimisation problems

Rostami

Neri

Epitropakis

2017

ICA

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Abstract.This paper proposes a novel algorithm for addressing multi-objective optimisation problems, by employing a progressive preference articulation approach to decision making. This enables the interactive incorporation of problem knowledge and decision maker preferences during the optimisation process. A novel progressive preference articulation mechanism, derived from a statistical technique, is herein proposed and implemented within a multi-objective framework based on evolution strategy search and hypervolume indicator selection. The proposed algorithm is named the Weighted Z-score Covariance Matrix Adaptation Pareto Archived Evolution Strategy with Hypervolume-sorted Adaptive Grid Algorithm (WZ-HAGA).WZ-HAGA is based on a framework that makes use of evolution strategy logic with covariance matrix adaptation to perturb the solutions, and a hypervolume indicator driven algorithm to select successful solutions for the subsequent generation. In order to guide the search towards interesting regions, a preference articulation procedure composed of four phases and based on the weighted z-score approach is employed. The latter procedure cascades into the hypervolume driven algorithm to perform the selection of the solutions at each generation.Numerical results against five modern algorithms representing the state-of-the-art in multi-objective optimisation demonstrate that the proposed WZ-HAGA outperforms its competitors in terms of both the hypervolume indicator and pertinence to the regions of interest.

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“…When it comes to preference information asked from the DM, it may be related to the objectives, constraints, or solutions (see Coello 2000;Rachmawati and Srinivasan 2006). Indeed, many MOO methods ask the DM to specify weights of the objectives, admissible trade-offs, reference or aspiration points (see Fonseca and Fleming 1993;Said et al 2010), or desirability functions (Wagner and Trautmann 2010).…”

Section: Introductionmentioning

confidence: 99%

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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Preference Incorporation in Multi-objective Evolutionary Algorithms: A Survey

Cited by 83 publications

References 23 publications

On Set-Based Multiobjective Optimization

On Set-Based Multiobjective Optimization

Progressive preference articulation for decision making in multi-objective optimisation problems

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

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