Tabu search to solve the Synchronized and Integrated Two-Level Lot Sizing and Scheduling Problem

This paper proposes a tabu search approach to solve the Synchronized and Integrated Two-Level Lot Sizing and Scheduling Problem (SITLSP). It is a real-world problem, often found in soft drink companies, where the production process has two integrated levels with decisions concerning raw material storage and soft drink bottling. Lot sizing and scheduling of raw materials in tanks and products in bottling lines must be simultaneously determined. Real data provided by a soft drink company is used to make comparisons with a previous genetic algorithm. Computational results have demonstrated that tabu search outperformed genetic algorithm in all instances. Copyright 2011 ACM.

Optimum-Path Forest pruning parameter estimation through Harmony Search

Pattern recognition in large amount of data has been paramount in the last decade, since that is not straightforward to design interactive and real time classification systems. Very recently, the Optimum-Path Forest classifier was proposed to overcome such limitations, together with its training set pruning algorithm, which requires a parameter that has been empirically set up to date. In this paper, we propose a Harmony Search-based algorithm that can find near optimal values for that. The experimental results have showed that our algorithm is able to find proper values for the OPF pruning algorithm parameter. © 2011 IEEE.

Boosting optimum-path forest clustering through harmony Search and its applications for intrusion detection in computer networks

In this paper we propose a nature-inspired approach that can boost the Optimum-Path Forest (OPF) clustering algorithm by optimizing its parameters in a discrete lattice. The experiments in two public datasets have shown that the proposed algorithm can achieve similar parameters' values compared to the exhaustive search. Although, the proposed technique is faster than the traditional one, being interesting for intrusion detection in large scale traffic networks. © 2012 IEEE.

Search for methylation-sensitive amplification polymorphisms in mutant figs

Fig (Ficus carica) breeding programs that use conventional approaches to develop new cultivars are rare, owing to limited genetic variability and the difficulty in obtaining plants via gamete fusion. Cytosine methylation in plants leads to gene repression, thereby affecting transcription without changing the DNA sequence. Previous studies using random amplification of polymorphic DNA and amplified fragment length polymorphism markers revealed no polymorphisms among select fig mutants that originated from gamma-irradiated buds. Therefore, we conducted methylation-sensitive amplified polymorphism analysis to verify the existence of variability due to epigenetic DNA methylation among these mutant selections compared to the main cultivar 'Roxo-de-Valinhos'. Samples of genomic DNA were double-digested with either HpaII (methylation sensitive) or MspI (methylation insensitive) and with EcoRI. Fourteen primer combinations were tested, and on an average, non-methylated CCGG, symmetrically methylated CmCGG, and hemimethylated hmCCGG sites accounted for 87.9, 10.1, and 2.0%, respectively. MSAP analysis was effective in detecting differentially methylated sites in the genomic DNA of fig mutants, and methylation may be responsible for the phenotypic variation between treatments. Further analyses such as polymorphic DNA sequencing are necessary to validate these differences, standardize the regions of methylation, and analyze reads using bioinformatic tools. © FUNPEC-RP.

A hybrid image restoration algorithm based on Projections onto Convex Sets and Harmony Search

Image restoration is a research field that attempts to recover a blurred and noisy image. Since it can be modeled as a linear system, we propose in this paper to use the meta-heuristics optimization algorithm Harmony Search (HS) to find out near-optimal solutions in a Projections Onto Convex Sets-based formulation to solve this problem. The experiments using HS and four of its variants have shown that we can obtain near-optimal and faster restored images than other evolutionary optimization approach. © 2013 IEEE.

A New Approach to Reducing Search Space and Increasing Efficiency in Simulation Optimization Problems via the Fuzzy-DEA-BCC

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Discovering promising regions to help global numerical optimization algorithms

We have developed an algorithm using a Design of Experiments technique for reduction of search-space in global optimization problems. Our approach is called Domain Optimization Algorithm. This approach can efficiently eliminate search-space regions with low probability of containing a global optimum. The Domain Optimization Algorithm approach is based on eliminating non-promising search-space regions, which are identifyed using simple models (linear) fitted to the data. Then, we run a global optimization algorithm starting its population inside the promising region. The proposed approach with this heuristic criterion of population initialization has shown relevant results for tests using hard benchmark functions.

Implementation of an F-statistic all-sky search for continuous gravitational waves in Virgo VSR1 data

We present an implementation of the F-statistic to carry out the first search in data from the Virgo laser interferometric gravitational wave detector for periodic gravitational waves from a priori unknown, isolated rotating neutron stars. We searched a frequency f(0) range from 100 Hz to 1 kHz and the frequency dependent spindown f(1) range from -1.6(f(0)/100 Hz) x 10(-9) Hz s(-1) to zero. A large part of this frequency-spindown space was unexplored by any of the all-sky searches published so far. Our method consisted of a coherent search over two-day periods using the F-statistic, followed by a search for coincidences among the candidates from the two-day segments. We have introduced a number of novel techniques and algorithms that allow the use of the fast Fourier transform (FFT) algorithm in the coherent part of the search resulting in a fifty-fold speed-up in computation of the F-statistic with respect to the algorithm used in the other pipelines. No significant gravitational wave signal was found. The sensitivity of the search was estimated by injecting signals into the data. In the most sensitive parts of the detector band more than 90% of signals would have been detected with dimensionless gravitational-wave amplitude greater than 5 x 10(-24).

Explorando abordagens de aprendizado sequencial para floresta de caminhos ótimos

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A formalized optimum code search for q-ary trellis codes

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A comparison about evolutionary algorithms for optimum-path forest clustering optimization

In this paper we deal with the problem of boosting the Optimum-Path Forest (OPF) clustering approach using evolutionary-based optimization techniques. As the OPF classifier performs an exhaustive search to find out the size of sample's neighborhood that allows it to reach the minimum graph cut as a quality measure, we compared several optimization techniques that can obtain close graph cut values to the ones obtained by brute force. Experiments in two public datasets in the context of unsupervised network intrusion detection have showed the evolutionary optimization techniques can find suitable values for the neighborhood faster than the exhaustive search. Additionally, we have showed that it is not necessary to employ many agents for such task, since the neighborhood size is defined by discrete values, with constrain the set of possible solution to a few ones.

Reconfiguração de sistemas de distribuição de energia elétrica utilizando uma metodologia multipartida

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Theoretical approaches to liana management: a search for a less harmful method

Lianas can change forest dynamics, slowing down forest regeneration after a perturbation. In these cases, it may be necessary to manage these woody climbers. Our aim was to simulate two management strategies: (1) focusing on abundant liana species and (2) focusing on the largest lianas, and contrast them with the random removal of lianas. We applied mathematical simulations for liana removal in three different vegetation types in southeastern Brazil: a Rainforest, a Seasonal Tropical Forest, and a Woodland Savanna. Using these samples, we performed simulations based on two liana removal procedures and compared them with random removal. We also used regression analysis with quasi-Poisson distribution to test whether larger lianas were aggressive, i.e., if they climbed into many trees. The procedure of cutting larger lianas was as effective as cutting them randomly and proved not to be a good method for liana management. Moreover, most of the lianas climbed into one or two trees, i.e., were not aggressive. Cutting the most abundant lianas proved to be a more effective method than cutting lianas randomly. This method could maintain liana richness and presumably should accelerate forest regeneration.