Ernestas Filatovas

Investigation of human factors while solving multiple criteria optimization problems in computer network

Abstract The aim of this investigation is to analyze a class of multiple criteria optimization problems that are solved by human‐computer interaction, using a computer network. A multiple criteria problem is iterated by interactively selecting different weight coefficients of the criteria. Several parallel solution strategies for solving this optimization problem have been developed and analyzed. The experiments have shown the importance of human assistance in solving this multiple criteria problem. New experimental investigations have been carried out with a different number of computers and different strategies where the human factors are analyzed. We have investigated the time necessary for humans training to solve this multiple criteria optimization problem, the dependence of human factors on the strategy of parallel solution and on the number of computers in a computer network.

Visualization of Pareto Front Points when Solving Multi-objective Optimization Problems

In this paper, a new strategy of visualizing Pareto front points is proposed when solving multi-objective optimization problems. A problem of graphical representation of the Pareto front points arises when the number of objectives is larger than 2 or 3, because, in this case, the Pareto front points are multidimensional. We face the problem of multidimensional data visualization. The visualization strategy proposed is based on a combination of clustering and dimensionality reduction. Moreover, in the obtained projection of the Pareto front points onto a plane, the points are marked according to the Euclidean distance of multidimensional points, corresponding to the points visualized, from the ideal point. In the experimental investigation of the paper, neural gas is used for data clustering, and multidimensional scaling is applied to dimensionality reduction, as well as to visualizing multidimensional data. The strategy can be implemented in a decision support system and it would be useful for a decision maker, who needs to review and evaluate many points of the Pareto fronts, for example, obtained by genetic algorithms. DOI: http://dx.doi.org/10.5755/j01.itc.42.4.3209

A preference-based multi-objective evolutionary algorithm R-NSGA-II with stochastic local search

Incorporation of a decision maker’s preferences into multi-objective evolutionary algorithms has become a relevant trend during the last decade, and several preference-based evolutionary algorithms have been proposed in the literature. Our research is focused on improvement of a well-known preference-based evolutionary algorithm R-NSGA-II by incorporating a local search strategy based on a single agent stochastic approach. The proposed memetic algorithm has been experimentally evaluated by solving a set of well-known multi-objective optimization benchmark problems. It has been experimentally shown that incorporation of the local search strategy has a positive impact to the quality of the algorithm in the sense of the precision and distribution evenness of approximation.

Experimental Investigation of Local Searches for Optimization of Grillage-Type Foundations

In grillage-type foundations, beams are supported by piles. The main goal of engineering design is to achieve the optimal pile placement scheme in which the minimal number of piles is used and all the reactive forces do not exceed the allowed values. This can be achieved by searching for the positions of piles where the difference between the maximal reactive forces and the limit magnitudes of reactions for the piles is minimal. In this study, the values of the objective function are given by a separate modeling package. Various algorithms for local optimization have been applied and their performance has been investigated and compared. Parallel computations have been used to speed-up experimental investigation.

IMAGE CLASSIFICATION FOR JPEG COMPRESSION

We analyse storage problems of digital images in accordance with image quality and image compression efficiency. Storage problems are relevant for Cloud storage and file hosting services, online file storage providers, social networks, etc. In this paper, an approach is proposed to process a group of images with a JPEG algorithm that all the processed images satisfy the minimum threshold of quality with the automatic selection of the quality factor (QF). The experimental investigation reveals advantages of the compression efficiency of the proposed approach over the traditional JPEG algorithm. The proposed approach enables saving storage spaces while maintaining the desirable image quality.

Quality Prediction of Compressed Images via Classification

In this paper, we have investigated an image classification problem according to image quality after compression. A classification-based image compression approach has been proposed, where images are assigned to one of two classes before their compression by a JPEG algorithm. This classification allows to set the proper value of Quality Factor (QF) for each image, in such a way, to save storage space while maintaining sufficiently high image quality. The image quality has been evaluated by a Structural Similarity (SSIM) index metric and Peak Signal-to-Noise Ratio (PSNR). As image classification results depend on the selected features describing the images, the feature selection problem has to be solved before classification. The experimental investigation has shown that the proposed approach allows to save storage space compared to a conventional JPEG algorithm. It is especially useful when saving huge amount of images.