Szymon Łukasik

Firefly Algorithm for Continuous Constrained Optimization Tasks

The paper provides an insight into the improved novel metaheuristics of the Firefly Algorithm for constrained continuous optimization tasks. The presented technique is inspired by social behavior of fireflies and the phenomenon of bioluminescent communication. The first part of the paper is devoted to the detailed description of the existing algorithm. Then some suggestions for extending the simple scheme of the technique under consideration are presented. Subsequent sections concentrate on the performed experimental parameter studies and a comparison with existing Particle Swarm Optimization strategy based on existing benchmark instances. Finally some concluding remarks on possible algorithm extensions are given, as well as some properties of the presented approach and comments on its performance in the constrained continuous optimization tasks.

Complete Gradient Clustering Algorithm for Features Analysis of X-Ray Images

Methods based on kernel density estimation have been successfully applied for various data mining tasks. Their natural interpretation together with suitable properties make them an attractive tool among others in clustering problems. In this paper, the Complete Gradient Clustering Algorithm has been used to investigate a real data set of grains. The wheat varieties, Kama, Rosa and Canadian, characterized by measurements of main grain geometric features obtained by X-ray technique, have been analyzed. The proposed algorithm is expected to be an effective tool for recognizing wheat varieties. A comparison between the clustering results obtained from this method and the classical k-means clustering algorithm shows positive practical features of the Complete Gradient Clustering Algorithm.

Study of Flower Pollination Algorithm for Continuous Optimization

Modern optimization has in its disposal an immense variety of heuristic algorithms which can effectively deal with both continuous and combinatorial optimization problems. Recent years brought in this area fast development of unconventional methods inspired by phenomena found in nature. Flower Pollination Algorithm based on pollination mechanisms of flowering plants constitutes an example of such technique. The paper presents first a detailed description of this algorithm. Then results of experimental study of its properties for selected benchmark continuous optimization problems are given. Finally, the performance the algorithm is discussed, predominantly in comparison with the well-known Particle Swarm Optimization Algorithm.

An algorithm for reducing the dimension and size of a sample for data exploration procedures

Abstract The paper deals with the issue of reducing the dimension and size of a data set (random sample) for exploratory data analysis procedures. The concept of the algorithm investigated here is based on linear transformation to a space of a smaller dimension, while retaining as much as possible the same distances between particular elements. Elements of the transformation matrix are computed using the metaheuristics of parallel fast simulated annealing. Moreover, elimination of or a decrease in importance is performed on those data set elements which have undergone a significant change in location in relation to the others. The presented method can have universal application in a wide range of data exploration problems, offering flexible customization, possibility of use in a dynamic data environment, and comparable or better performance with regards to the principal component analysis. Its positive features were verified in detail for the domain’s fundamental tasks of clustering, classification and detection of atypical elements (outliers).

Experimental Study of Selected Parameters of the Krill Herd Algorithm

The Krill Herd Algorithm is the latest heuristic technique to be applied in deriving best solution within various optimization tasks. While there has been a few scientific papers written about this algorithm, none of these have described how its numerous basic parameters impact upon the quality of selected solutions. This paper is intended to contribute towards improving the aforementioned situation, by examining empirically the influence of two parameters of the Krill Herd Algorithm, notably, maximum induced speed and inertia weight. These parameters are related to the effect of the herd movement as induced by individual members. In this paper, the results of a study – based on certain examples obtained from the CEC13 competition – are being presented. They appear to show a relation between these selected two parameters and the convergence of the algorithm for particular benchmark problems. Finally, some concluding remarks, based on the performed numerical studies, are provided.

Parallel Computing of Kernel Density Estimates with MPI

Kernel density estimation is nowadays a very popular tool for nonparametric probabilistic density estimation. One of its most important disadvantages is computational complexity of calculations needed, especially for data-based bandwidth selection and adaptation of bandwidth coefficient. The article presents parallel methods which can significantly improve calculation time. Results of using reference implementation based on Message Passing Interface standard in multicomputer environment are included as well as a discussion on effectiveness of parallelization.Kernel density estimation is nowadays a very popular tool for nonparametric probabilistic density estimation. One of its most important disadvantages is computational complexity of calculations needed, especially for data-based bandwidth selection and adaptation of bandwidth coefficient. The article presents parallel methods which can significantly improve calculation time. Results of using reference implementation based on Message Passing Interface standard in multicomputer environment are included as well as a discussion on effectiveness of parallelization.

Parallel simulated annealing algorithm for graph coloring problem

The paper describes an application of Parallel Simulated Annealing (PSA) for solving one of the most studied NP-hard optimization problems: Graph Coloring Problem (GCP). Synchronous master-slave model with periodic solution update is being used. The paper contains description of the method, recommendations for optimal parameters settings and summary of results obtained during algorithms evaluation. A comparison of our novel approach to a PGA metaheuristic proposed in the literature is given. Finally, directions for further work in the subject are suggested.

An algorithm for sample and data dimensionality reduction using fast simulated annealing

This paper deals with dimensionality and sample length reduction applied to the tasks of exploratory data analysis. Proposed technique relies on distance preserving linear transformation of given dataset to the lower dimensionality feature space. Coefficients of feature transformation matrix are found using Fast Simulated Annealing - an algorithm inspired by physical annealing of solids. Furthermore the elimination or weighting of data elements which, as an effect of above mentioned transformation, were moved significantly from the rest of the dataset can be performed. Presented method was positively verified in routines of clustering, classification and outlier detection. It ensures proper efficiency of those procedures in compact feature space and with reduced data sample length at the same time.

Using topology preservation measures for high-dimensional data analysis in a reduced feature space

Przedmiotem niniejszego artykulu jest wielowymiarowa analiza danych, ktora realizowana jest poprzez uzupelnienie standardowych procedur ekstrakcji cech odpowiednimi miarami zachowania struktury topologicznej zbioru. Podejście to motywuje obserwacja, ze nie wszystkie elementy zbioru pierwotnego w toku redukcji są wlaściwie zachowane w ramach reprezentacji w przestrzeni o zmniejszonej wymiarowości. W artykule przedstawiono najpierw istniejące miary zachowania topologii zbioru, a nastepnie omowiono mozliwości ich wlączenia w klasyczne procedury eksploracyjnej analizy danych. Zalączono rowniez ilustracyjne przyklady uzycia omawianego podejścia w zadaniach analizy skupien i klasyfikacji.

Discrimination of Wheat Grain Varieties Using X-Ray Images

A study was conducted so as to develop a methodology for wheat variety discrimination and identification by way of image analysis techniques. The main purpose of this work was to determine a crucial set of parameters with respect to wheat grain morphology which best differentiate wheat varieties. To achieve better performance, the study was done by means of multivariate discriminant analysis. This utilized both forward and backward stepwise procedures based on various sets of geometric features. These parameters were extracted from the digitized X-ray images of wheat kernels obtained for three wheat varieties: Canadian, Kama, and Rosa. In our study, we revealed that selected combinations of geometric features permitted discriminant analysis to achieve a recognition rate of 89–96 %. We then compared the correctness of classification with results obtained by way of employing the nonparametric approach. The discriminant analysis proved effective in differentiating wheat varieties.