A mini-review on preference modeling and articulation in multi-objective optimization: current status and challenges

Wang, Handing; Olhofer, Markus; Jin, Yaochu

doi:10.1007/s40747-017-0053-9

Cited by 123 publications

(67 citation statements)

References 176 publications

(155 reference statements)

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“…As mentioned in Section 1, there have been a plethora of studies to approximate DM's preferred solutions a priori, posteriori or interactively. Since this paper mainly investigates the frequent involvements of a DM with an EMO algorithm, we do not intend to review a priori and posteriori approaches, except to encourage the interested readers to look at some recent survey papers [50][51][52]. Some recent studies periodically asked the DM to provide her/his preference information upon one or more pairs of alternative points found by an EMO algorithm.…”

Section: Past Studies On Progressively Interactive Methodsmentioning

confidence: 99%

Interactive Decomposition Multiobjective Optimization Via Progressively Learned Value Functions

Chen

Savić

et al. 2019

IEEE Trans. Fuzzy Syst.

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Decomposition has become an increasingly popular technique for evolutionary multi-objective optimization (EMO). A decomposition-based EMO algorithm is usually designed to approximate a whole Pareto-optimal front (PF). However, in practice, the decision maker (DM) might only be interested in her/his region of interest (ROI), i.e., a part of the PF. Solutions outside that might be useless or even noisy to the decision-making procedure. Furthermore, there is no guarantee to find the preferred solutions when tackling many-objective problems. This paper develops an interactive framework for the decomposition-based EMO algorithm to lead a DM to the preferred solutions of her/his choice. It consists of three modules, i.e., consultation, preference elicitation and optimization. Specifically, after every several generations, the DM is asked to score a few candidate solutions in a consultation session. Thereafter, an approximated value function, which models the DM's preference information, is progressively learned from the DM's behavior. In the preference elicitation session, the preference information learned in the consultation module is translated into the form that can be used in a decomposition-based EMO algorithm, i.e., a set of reference points that are biased toward to the ROI. The optimization module, which can be any decomposition-based EMO algorithm in principle, utilizes the biased reference points to direct its search process. Extensive experiments on benchmark problems with three to ten objectives fully demonstrate the effectiveness of our proposed method for finding the DM's preferred solutions.

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Section: Past Studies On Progressively Interactive Methodsmentioning

confidence: 99%

Interactive Decomposition Multiobjective Optimization Via Progressively Learned Value Functions

Chen

Savić

et al. 2019

IEEE Trans. Fuzzy Syst.

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“…Generally speaking, visualization becomes challenging when the number of objectives is larger than three. Wang et al (2017) suggest that existing techniques for visualization of the nondominated solutions in cases with more than three dimensions include parallel coordinates, mapping, and aggregation tree.…”

Section: Decision Aid Tools To Find the Preferred Solutionsmentioning

confidence: 99%

“…Therefore, derivation of the decision maker's preference and the incorporation of that preference in MOEAs are important both for solving MaOPs and for promoting better decision making. Preference‐based optimization methods have been proposed to derive and incorporate the preference in the optimization (Coello, ; Rachmawati & Srinivasan, ; Purshouse et al, ; Wang et al, ). Preference‐based optimization methods can be roughly classified into three categories, a priori, interactive, and a posteriori methods (Thiele et al, ), according to when the decision‐maker preference is incorporated.…”

Section: Introductionmentioning

confidence: 99%

“…A posteriori methods have gained significantly more popularity than either a prior or interactive methods. However, a posteriori methods become less effective when applied to MaOPs (Rachmawati & Srinivasan, ; Purshouse et al, ; Wang et al, ). When solving MaOPs, the computational cost incurred by supplying the decision maker with a set of alternatives is considerable.…”

Section: Introductionmentioning

confidence: 99%

“…Generally speaking, visualization becomes challenging when the number of objectives is larger than three. Wang et al () suggest that existing techniques for visualization of the nondominated solutions in cases with more than three dimensions include parallel coordinates, mapping, and aggregation tree. Parallel coordinates (Inselberg, ) utilizes M parallel axes for an M ‐dimensional objective space, where each axis corresponds to one objective.…”

Section: Introductionmentioning

confidence: 99%

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A many‐objective evolutionary algorithm incorporating decision maker's preference and its application to management of the electricity distribution network

Sekizaki

Nishizaki

Hayashida

2019

Multi Criteria Decision Anal

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This paper proposes a step‐by‐step decision making approach that incorporates preferences of a decision maker into a many‐objective evolutionary algorithm. The algorithm is applied to a challenging many‐objective optimization problem (MaOP) involving integer objective values, tight constraints, heavy computational burden, and limited knowledge about the true Pareto front. The proposed method consists of five steps. (a) The entire Pareto front with high dimensions is roughly approximated by a small set of nondominated solutions. (b) On the basis of the approximated Pareto front, the decision maker specifies a preference. (c) Reference points are distributed through part of the objective space using the preference information. (d) The approximation accuracy of the Pareto front in the region of interest is improved by local search using the reference points. (e) The most preferred solution from a set of the nondominated solutions is chosen. The evolutionary algorithms employed in Steps (a) and (d) have an improved normalization procedure and selection mechanism for the MaOP involving integer objective value(s) and the tight constraints. Steps (b) and (e) use a graphical‐user‐interface‐based interactive tool we developed to efficiently narrow down to the preferred solution(s) among the high‐dimensional, nondominated solutions. This alleviates the cognitive burden on the decision maker and therefore aids the decision maker in handling MaOPs. In the computational experiment, we choose the real‐world application of reconfiguring an electricity distribution network. We demonstrate that the proposed approach can provide satisfying solutions to a decision maker.

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Surrogate-Assisted Evolutionary Optimization of Large Problems

Chugh

Sun

Wang

et al. 2019

Studies in Computational Intelligence

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This chapter presents some recent advances in surrogate-assisted evolutionary optimization of large problems. By large problems, we mean either the number of decision variables is large, or the number of objectives is large, or both. These problems pose challenges to evolutionary algorithms themselves, constructing surrogates and surrogate management. To address these challenges, we proposed two algorithms, one called kriging-assisted reference vector guided evolutionary algorithm (K-RVEA) for many-objective optimization, and the other called cooperative swarm optimization algorithm (SA-COSO) for high-dimensional single-objective optimization. Empirical studies demonstrate that K-RVEA works well for manyobjective problems having up to ten objectives, while SA-COSA outperforms the state-of-the-art algorithms on 200-dimensional single-objective test problems.

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A mini-review on preference modeling and articulation in multi-objective optimization: current status and challenges

Cited by 123 publications

References 176 publications

Interactive Decomposition Multiobjective Optimization Via Progressively Learned Value Functions

Interactive Decomposition Multiobjective Optimization Via Progressively Learned Value Functions

A many‐objective evolutionary algorithm incorporating decision maker's preference and its application to management of the electricity distribution network

Surrogate-Assisted Evolutionary Optimization of Large Problems

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