Harmony Search-Based Approaches for Fine-Tuning Deep Belief Networks

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Data

2023-01-01

Autores

Rodrigues, Douglas [UNESP]
Roder, Mateus [UNESP]
Passos, Leandro Aparecido
Rosa, Gustavo Henrique de [UNESP]
Papa, João Paulo [UNESP]
Geem, Zong Woo

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Resumo

Harmony Search (HS) is a metaheuristic algorithm inspired by the musical composition process, precisely the composition of harmonies, i.e., the chain of different musical notes. The algorithm’s simplicity allows several points to improve to explore the entire search space efficiently. This work aims to compare different HS variants in image restoration using Deep Belief Networks (DBN). We compared standard HS against five variants: Improved Harmony Search (IHS), Self-adaptive Global Best Harmony Search (SGHS), Global-best Harmony Search (GHS), Novel Global Harmony Search (NGHS), and Global Harmony Search with Generalized Opposition-based learning (GOGHS). Experiments in public datasets for binary image reconstruction highlighted that HS and its variants obtained superior results than a random search used as a baseline. Also, it was found that the GHS variant is inferior to the others for some cases.

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Deep Belief Networks, Harmony Search, Metaheuristic optimization

Como citar

Intelligent Systems Reference Library, v. 236, p. 105-118.