Finding representations for an unconstrained bi-objective combinatorial optimization problem

Jesus, Alexandre D.; Paquete, Luís; Figueira, José Rui

doi:10.1007/s11590-017-1129-6

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…The problem formulated in Sect. 2 is a bi-objective combinatorial optimization problem [4][5][6][7][8][9][10], which is generally difficult to solve when the number of the decision variable becomes large. Instead of using complex algorithms to solve the bi-objective combinatorial optimization problem, an overall fitness function is proposed and a solver based on evolutionary computation is used to efficiently obtain good solutions.…”

Section: The Proposed Approachmentioning

confidence: 99%

Final Report Assignment Based on Students’ Preferences to Topics and Partners

Hsu¹,

Chen²,

Kuo³

et al. 2022

Proceedings of the 2022 International Conference on Diversified Education and Social Development (DESD 2022)

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To evaluate the learning performance of students, a final report is usually assigned. Students are asked to form a group which includes several students and they are assigned a specific topic to prepare a report. The way of assignment greatly influences on the results. The purpose of this study is to present a novel optimization approach to final report assignment which considers the preferences of students to topics as well as their partners. Initially, students are asked to indicate their preferences to some specific topics and their preferred co-operative partners. After aggregating preference data, a scoring system is used to evaluate the assignment. Mathematical Programming (MP) is employed and an evolutionary method is used to find solutions. Results from this study show that the proposed approach presented in this study can get good solutions efficiently.

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Section: The Proposed Approachmentioning

confidence: 99%

Final Report Assignment Based on Students’ Preferences to Topics and Partners

Hsu¹,

Chen²,

Kuo³

et al. 2022

Proceedings of the 2022 International Conference on Diversified Education and Social Development (DESD 2022)

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“…-Coverage [25], is a measure of how distant the points in the representation are from those in the non-dominated set, and is defined by the maximum distance between a non-dominated point and its closest point in the representation set. ε-indicator (multiplicative) [27,13], is defined by the smallest multiplicative factor that can be applied to the all points in the representation, such that each point of the non-dominated set is dominated by at least one point in the representation set. -Hypervolume [15], corresponds to the measure of the multi-dimensional region dominated by the points in the representation set with respect to a reference point.…”

Section: Representation Qualitymentioning

confidence: 99%

A model of anytime algorithm performance for bi-objective optimization

2020

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