A convergence indicator for multi-objective optimisation algorithms.
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Data
2018
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Resumo
The algorithms of multi-objective optimisation had a relative growth in the last years.
Thereby, it requires some way of comparing the results of these. In this sense, performance measures play
a key role. In general, it’s considered some properties of these algorithms such as capacity, convergence,
diversity or convergence-diversity. There are some known measures such as generational distance (GD),
inverted generational distance (IGD), hypervolume (HV), Spread(∆), Averaged Hausdorff distance (∆p),
R2-indicator, among others. In this paper, we focuses on proposing a new indicator to measure convergence based on the traditional formula for Shannon entropy. The main features about this measure are: 1)
It does not require to know the true Pareto set and 2) Medium computational cost when compared with
Hypervolume.
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Shannon entropy, Performance measure
Citação
SANTOS, T. F.; XAVIER, S. M. A convergence indicator for multi-objective optimisation algorithms. TEMA. Tendências em Matemática Aplicada e Computacional, v. 19, n. 3, p. 437-448, 2018. Disponível em: <http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2179-84512018000300437>. Acesso em: 19 mar. 2019.