A convergence indicator for multi-objective optimisation algorithms.
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,...
| Authors: | , |
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| Format: | article |
| Status: | Published version |
| Publication Date: | 2018 |
| Country: | Brasil |
| Institution: | Universidade Federal de Ouro Preto (UFOP) |
| Repository: | Repositório Institucional da UFOP |
| Language: | English |
| OAI Identifier: | oai:repositorio.ufop.br:123456789/11525 |
| Online Access: | http://www.repositorio.ufop.br/handle/123456789/11525 http://dx.doi.org/10.5540/tema.2018.019.03.0437 |
| Access Level: | Open access |
| Keyword: | Shannon entropy Performance measure |
| Summary: | 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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