ACO&lt;inf&gt;&amp;#x211D;&lt;/inf&gt;-V - An algorithm that incorporates the visibility heuristic to the ACO in continuous domain

Conti, Cassio Rodrigo; Roisenberg, Mauro; Neto, Guenther Schwedersky

doi:10.1109/cec.2012.6252921

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…By incorporating a variety of heuristics, our extension can achieve a high performance when applied to seismic inversion problems. More implementation details can be seen in [21].…”

Section: Revisionmentioning

confidence: 99%

Fast Seismic Inversion Methods Using Ant Colony Optimization Algorithm

Conti

Roisenberg

Neto

et al. 2013

IEEE Geosci. Remote Sensing Lett.

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This letter presents ACO R -V , a new computationally efficient ant-colony-optimization-based algorithm, tailored for continuous-domain problems. The ACO R -V algorithm is well suited for application in seismic inversion problems, owing to its intrinsic features, such as heuristics in generating the initial solution population and its facility to deal with multiobjective optimization problems. Here, we show how the ACO R -V algorithm can be applied in two methodologies to obtain 3-D impedance maps from poststack seismic amplitude data. The first methodology pertains to the traditional method of forward convolution of a reflectivity model with the estimated wavelet, where ACO R -V is used to guess the appropriate wavelet as the reflectivity model. In the second methodology, we propose an even faster inversion algorithm based on inverse filter optimization, where ACO R -V optimizes the inverse filter that is deconvolved with the seismic traces and results in a reflectivity model similar to that found in well logs. This modeled inverse filter is then deconvolved with the entire 3-D seismic volume. In experiments, both the methodologies are applied to a synthetic 3-D seismic volume. The results validate their feasibility and the suitability of ACO R -V as an optimization algorithm. The results also show that the second methodology has the advantages of a much higher convergence speed and effectiveness as a seismic inversion tool.

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“…By incorporating a variety of heuristics, our extension can achieve a high performance when applied to seismic inversion problems. More implementation details can be seen in [21].…”

Section: Revisionmentioning

confidence: 99%

Fast Seismic Inversion Methods Using Ant Colony Optimization Algorithm

Conti

Roisenberg

Neto

et al. 2013

IEEE Geosci. Remote Sensing Lett.

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“…The regularization of RBF neurons is accomplished by a clustering algorithm. The other parameters of the system are determined by an improved Ant Colony Optimization Algorithm (ACO) designed for continuous domain variables called ACO R V, proposed in [10]. Just like other Evolutionary Methods, the ACO R V uses a fitness function to measure the quality of the represented solution.…”

Section: Introductionmentioning

confidence: 99%

Generating interpretable Mamdani-type fuzzy rules using a neuro-fuzzy system based on radial basis functions

Rodrigues

Moura

Jacinto

et al. 2014

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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This paper presents a novel neuro-fuzzy inference system, called RBFuzzy, capable of knowledge extraction and generation of highly interpretable Mamdani-type fuzzy rules. RBFuzzy is a four layer neuro-fuzzy inference system that takes advantage of the functional behavior of Radial Basis Function (RBF) neurons and their relationship with fuzzy inference systems. Inputs are combined in the RBF neurons to compound the antecedents of fuzzy rules. The fuzzy rules consequents are determined by the third layer neurons where each neuron represents a Mamdani-type fuzzy output variable in the form of a linguistic term. The last layer weights each fuzzy rule and generates the crisp output. An extension of the ant-colony optimization (ACO) algorithm is used to adjust the weights of each rule in order to generate an accurate and interpretable fuzzy rule set. For benchmarking purposes some experiments with classic datasets were carried out to compare our proposal with the EFuNN neuro-fuzzy model. The RBFuzzy was also applied in a real world oil well-log database to model and forecast the Rate of Penetration (ROP) of a drill bit for a given offshore well drilling section. The obtained results show that our model can reach the same level of accuracy with fewer rules when compared to the EFuNN, which facilitates understanding the operation of the system by a human expert.

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ACO<inf>ℝ</inf>-V - An algorithm that incorporates the visibility heuristic to the ACO in continuous domain

Cited by 2 publications

References 21 publications

Fast Seismic Inversion Methods Using Ant Colony Optimization Algorithm

Fast Seismic Inversion Methods Using Ant Colony Optimization Algorithm

Generating interpretable Mamdani-type fuzzy rules using a neuro-fuzzy system based on radial basis functions

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