Towards Automatic Testing of Reference Point Based Interactive Methods

Ojalehto, Vesa; Podkopaev, Dmitry; Miettinen, Kaisa

doi:10.1007/978-3-319-45823-6_45

Cited by 16 publications

(29 citation statements)

References 10 publications

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“…For ADM-PSO, parameters were set by following the previous work and standard settings of PSO 2007 [10]. It comprised of a maximum number of iterations I max = 11, an objective consideration probability p = 0.5, a weight w = 1/k and a tolerance θ = 10 −3 (see [7] for a further explanation of ADM parameters). For PSO, we set ϕ 1 = ϕ 2 = 1/(2 + log(2)) and inertia ω = 1/(2 · log(2)).…”

Section: Resultsmentioning

confidence: 99%

“…In addition, with the aim of keeping track of the original ADM [7], it was also applied by following the same procedure. The results of ADM and ADM-PSO are arranged in Tables 2 and 3, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…In what follows, we refer to the ADM proposed in [7] as the original ADM. It consists of three main components: steady part, current context and preference information.…”

Section: Artificial Decision Makermentioning

confidence: 99%

“…The first phase corresponds to initialization of parameters, populations and initial Pareto set approximations (from line 1 to 11). In this phase, an initial reference point is also generated (line 12) as done in the original ADM (see Section 3 in [7]). After this, the iterative solution process (line 13) starts with multiple rounds of the interactive multiobjective optimization method (line 14) and the corresponding generation of new reference points, by means of PSO (line 16).…”

Section: Algorithm Of Adm-psomentioning

confidence: 99%

“…Recently, in [7] a new ADM has been developed for testing reference point based interactive methods. It is able to adjust reference points based on information about solutions derived so far.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Artificial Decision Maker Driven by PSO: An Approach for Testing Reference Point Based Interactive Methods

Barba-González

Ojalehto

García-Nieto

et al. 2018

Parallel Problem Solving From Nature – PPSN XV

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Over the years, many interactive multiobjective optimization methods based on a reference point have been proposed. With a reference point, the decision maker indicates desirable objective function values to iteratively direct the solution process. However, when analyzing the performance of these methods, a critical issue is how to systematically involve decision makers. A recent approach to this problem is to replace a decision maker with an artificial one to be able to systematically evaluate and compare reference point based interactive methods in controlled experiments. In this study, a new artificial decision maker is proposed, which reuses the dynamics of particle swarm optimization for guiding the generation of consecutive reference points, hence, replacing the decision maker in preference articulation. We use the artificial decision maker to compare interactive methods. We demonstrate the artificial decision maker using the DTLZ benchmark problems with 3, 5 and 7 objectives to compare R-NSGA-II and WASF-GA as interactive methods. The experimental results show that the proposed artificial decision maker is useful and efficient. It offers an intuitive and flexible mechanism to capture the current context when testing interactive methods for decision making.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…In what follows, we refer to the ADM proposed in [7] as the original ADM. It consists of three main components: steady part, current context and preference information.…”

Section: Artificial Decision Makermentioning

confidence: 99%

Section: Algorithm Of Adm-psomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Artificial Decision Maker Driven by PSO: An Approach for Testing Reference Point Based Interactive Methods

Barba-González

Ojalehto

García-Nieto

et al. 2018

Parallel Problem Solving From Nature – PPSN XV

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An Artificial Decision Maker for Comparing Reference Point Based Interactive Evolutionary Multiobjective Optimization Methods

Afşar

Miettinen

Ruíz

2021

Lecture Notes in Computer Science

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Comparing interactive evolutionary multiobjective optimization methods is controversial. The main difficulties come from features inherent to interactive solution processes involving real decision makers. The human can be replaced by an artificial decision maker (ADM) to evaluate methods quantitatively. We propose a new ADM to compare reference point based interactive evolutionary methods, where reference points are generated in different ways for the different phases of the solution process. In the learning phase, the ADM explores different parts of the objective space to gain insight about the problem and to identify a region of interest, which is studied more closely in the decision phase. We demonstrate the ADM by comparing interactive versions of RVEA and NSGA-III on benchmark problems with up to 9 objectives. The experiments show that our ADM is efficient and allows repetitive testing to compare interactive evolutionary methods in a meaningful way.

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