Hybrid firefly-Linde-Buzo-Gray algorithm for Channel-Optimized Vector Quantization codebook design

Ferreira, Felipe A. B. S.; Leitão, H. A. S.; Lopes, Waslon T. A.; Madeiro, Francisco

doi:10.3233/ica-170546

Cited by 9 publications

(4 citation statements)

References 52 publications

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“…The other category is the combination of algorithms; that is, the algorithms are used in parallel or in series, with no mutual penetration of structure. At present, many studies fuse bionic intelligence and data mining algorithms (Smolik & Skala, ) to improve the performance of traditional data mining algorithms such as the support vector machine (SVM) with PSO (Gilan, Jovein, & Ramezanianpour, ; Yang et al, ; Yang & Hsieh, ), SVM with the firefly algorithm (FA; Chou & Pham, ), the firefly Linde–Buzo–Gray algorithm (Ferreira, Leitão, Lopes, & Madeiro, ), and the multiple signal classification method (Jiang & Adeli, ) with Artificial neural network (ANN) (Osornio‐Rios, Amezquita‐Sanchez, Romero‐Troncoso, & Garcia‐Perez, ), especially for accuracy of prediction. At the same time, the interest in combination of multiple data mining algorithms (Padillo, Luna, Herrera, & Ventura, ) is evergrowing.…”

Section: Related Workmentioning

confidence: 99%

Smart bacteria‐foraging algorithm‐based customized kernel support vector regression and enhanced probabilistic neural network for compaction quality assessment and control of earth‐rock dam

et al. 2018

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Compaction quality assessment and control for an earth‐rock dam is the key measure to ensure dam safety. However, to date, the compaction quality assessment model has not been accurate enough, and no effective feedback control measures have been developed. Hybrid data mining algorithms have great potential for solving this problem. In this study, smart bacteria‐foraging algorithm‐based customized kernel support vector regression (SBFA‐CKSVR) is proposed for compaction quality assessment, whereas an enhanced probabilistic neural network (EPNN) is adopted for compaction quality control. SBFA integrates a bacteria‐foraging algorithm, chaos mapping, and adaptive and quantum computing to solve the high‐dimensional complex problem effectively. CKSVR is proposed to approximate a function in quadratic continuous integral space L2(R) where its hyperparameters are optimized by SBFA. Finally, SBFA‐CKSVR is used to establish a high‐precision compaction quality assessment model whereas the EPNN is adopted to realize the compaction quality feedback control. A three‐dimensional real‐time monitoring system for the earth‐rock dam is also developed based on SBFA‐CKSVR and EPNN. A large‐scale hydraulic engineering application proves the effectiveness and superiority of this research compared with the previous work.

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Section: Related Workmentioning

confidence: 99%

Smart bacteria‐foraging algorithm‐based customized kernel support vector regression and enhanced probabilistic neural network for compaction quality assessment and control of earth‐rock dam

et al. 2018

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“…The parameter Tol is set to 0.1 m because the error in this order of magnitude is acceptable in the shape generation of realistic structures. The parameters β 0 , γ, and α are usually set to 1, 1, and 0.2 in most studies of firefly algorithm (Wu et al, ; Ferreira et al., ). Many trial numerical examples show that they are also optimal values for the shape‐generation problem.…”

Section: Dynamic Weight Multiobjective Optimization Algorithmmentioning

confidence: 99%

Shape Generation of Grid Structures by Inverse Hanging Method Coupled with Multiobjective Optimization

et al. 2018

Computer aided Civil Eng

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Free‐form grid structure is a kind of structure whose shape cannot be expressed by analytical functions. Although this kind of structure has been widely used in practice, how to generate the structural shapes that not only satisfy architectural requirements but also have excellent mechanical properties is still a problem. This article focuses on the shape generation of complex grid structures and proposes a novel shape‐generation method for free‐form grid structures. The method integrates numerical inverse hanging method and multiobjective optimization. First, it uses numerical inverse hanging method to generate structural shapes in low bending moment level. Next, it uses dynamic weight multiobjective firefly optimization algorithm to adjust the parameters of the inverse hanging method adaptively so that the structural shape can satisfy architectural requirements. Comparisons with former shape‐generation methods show that the proposed method is more effective. Numerical examples based on real structures illustrate the applicability of the proposed method in practice.

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“…Os impactos provocados por canais ruidosos no desempenho de um sistema de QV podem ser amenizados utilizando a Quantizac ¸ão Vetorial Otimizada para Canal (QVOC) [7], [8] ou a Quantizac ¸ão Vetorial Robusta (QVR) [6], [9], [10]. Na QVOC, o projeto do dicionário de vetores-códigos é realizado levando em considerac ¸ão a distorc ¸ão de um canal específico.…”

Section: Introduc ¸ãOunclassified

Algoritmo FSS modificado Aplicado à Quantização Vetorial Robusta de Imagens

Santana¹,

Lopes²,

Madeiro³

2021

Anais Do 15. Congresso Brasileiro De Inteligência Computacional

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No presente trabalho, é apresentada uma versão modificada do algoritmo de Busca por Cardume (FSS – Fish School Search) aplicado ao problema de Quantização Vetorial Robusta (QVR), sendo consideradas distâncias Hamming igual a 1 e 2 no índice de desordem, o qual é figura de mérito para o problema de otimização considerado. Os resultados de simulação mostram que o algoritmo FSS apresentou melhor desempenho quando comparado ao Simulated Annealing (SA) para várias condições de transmissão de imagens por Canal Binário Simétrico.

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Hybrid firefly-Linde-Buzo-Gray algorithm for Channel-Optimized Vector Quantization codebook design

Cited by 9 publications

References 52 publications

Smart bacteria‐foraging algorithm‐based customized kernel support vector regression and enhanced probabilistic neural network for compaction quality assessment and control of earth‐rock dam

Smart bacteria‐foraging algorithm‐based customized kernel support vector regression and enhanced probabilistic neural network for compaction quality assessment and control of earth‐rock dam

Shape Generation of Grid Structures by Inverse Hanging Method Coupled with Multiobjective Optimization

Algoritmo FSS modificado Aplicado à Quantização Vetorial Robusta de Imagens

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