Evolutionary Multi-Agent Systems in Non-Stationary Environments

In the article the performance of an evolutionary multi-agent system in  dynamic optimization is evaluated in comparison to classical evolutionary  algorithms.  The starting point is a general introduction describing the  background, structure and behaviour of EMAS against classical  evolutionary techniques.  Then the properties of energy-based selection  are investigated to show how it may influence the diversity of the  population in EMAS.  The considerations are illustrated by experimental  results based on the dynamic version of the well-known, high-dimensional Rastrigin function  benchmark

GPGPU for Difficult Black-box Problems

AbstractDifficult black-box problems arise in many scientific and industrial areas. In this paper, efficient use of a hardware accelerator to implement dedicated solvers for such problems is discussed and studied based on an example of Golomb Ruler problem. The actual solution of the problem is shown based on evolutionary and memetic algorithms accelerated on GPGPU. The presented results prove that GPGPU outperforms CPU in some memetic algorithms which can be used as a part of hybrid algorithm of finding near optimal solutions of Golomb Ruler problem. The presented research is a part of building heterogenous parallel algorithm for difficult black-box Golomb Ruler problem

Classic and Agent-Based Evolutionary Heuristics for Shape Optimization of Rotating Discs

The article presents a metaheuristic solution for the problem of shape optimization of a rotating annular disc. Such discs are important structural components of e.g. jet engines, steam turbines or disc brakes. The design goal is to find the disc shape that would ensure its maximal carrying capacity (corresponding to the speed of rotation), which is a variational problem with the objective functional defined by L-infinity norm. Such a definition makes the problem impossible to solve using analytical methods so utilization of metaheuristics is necessary. We present different algorithms to solve the problem starting with a classic evolutionary one, followed by agent-based and hybrid agent-based memetic algorithms, which are the main focus of this paper. The reason for this is that agent-based computing systems proved to be versatile as an optimization technique being especially efficient for the problems with complex fitness functions. The obtained experimental results encourage further application of such an approach to similar engineering problems

A Crisis Management Approach To Mission Survivability In Computational Multi-Agent Systems

In this paper we present a biologically-inspired approach for mission survivability (consideredas the capability of fulfilling a task such as computation) that allows the system to be aware ofthe possible threats or crises that may arise. This approach uses the notion of resources usedby living organisms to control their populations.We present the concept of energetic selectionin agent-based evolutionary systems as well as the means to manipulate the configuration ofthe computation according to the crises or user’s specific demands

Adapting a Constituency Parser to User-Generated Content in Polish Opinion Mining

The paper focuses on the adjustment of NLP tools for Polish; e.g., morphological analyzers and parsers, to user-generated content (UGC). The authors discuss two rule-based techniques applied to improve their efficiency: pre-processing (text normalization) and parser adaptation (modified segmentation and parsing rules). A new solution to handle OOVs based on inflectional translation is also offered

A CRISIS MANAGEMENT APPROACH TO MISSION SURVIVABILITY IN COMPUTATIONAL MULTI-AGENT SYSTEMS

In this paper we present a biologically-inspired approach for mission survivability (consideredas the capability of fulfilling a task such as computation) that allows the system to be aware ofthe possible threats or crises that may arise. This approach uses the notion of resources usedby living organisms to control their populations.We present the concept of energetic selectionin agent-based evolutionary systems as well as the means to manipulate the configuration ofthe computation according to the crises or user’s specific demands

Model for Dynamic and Hierarchical Data Repository in Relational Database

The aim of this research is to build an open schema model for digital sources repository in the relational database. It required us to develop a few advanced techniques. One of them was to keep and maintain a hierarchical data structure pushed into the repository. The second was to create constraints on any hierarchical level that allow enforcing data integrity and consistency. The created solution is mainly based on a JSON as a native column type, which was designed for holding open schema documents. In this paper, we present the model for any repository that uses hierarchical dynamic data. Additionally, we include a structure for normalizing input and description for data to keep all the model assumptions. We compared our solution with well-known open schema model -- Entity-Attribute-Value -- in the scope of saving data and querying about relationship and content from structure. Results have shown that we achieved improvement in both performance and disk space usage, although we extended our model with a few new features that the previous model does not include. The techniques developed in this research can be applied in every domain where hierarchical dynamic data is required, as demonstrated by the digital book repository that we have presented

Implementation of the Concept of a Repository for Automated Processing of Semi-Structural Data, Journal of Telecommunications and Information Technology, 2020, nr 1

Semi-structural data tend to be problematic due to the sparsity of their attributes and due to the fact that, regardless of their type, they are immensely diverse. This means that data storage is a challenge, especially when the data contained within a relational database – often a strict requirement defined in advance. In this paper, we present a thoroughly described concept of a repository that is capable of storing and processing semi-structural data. Based on this concept, we establish a database model comprising the architecture and the tools needed to search the data and build relevant processors. The processor described may assign roles and dispatch tasks between the users. We demonstrate how the capacities of this repository are capable of overcoming current limitations by creating a system for facilitated digitization of scientific resources. In addition, we show that the repository in question is suitable for general use, and, as such, may be adapted to any domains in which semi-structural data are processed, without any additional work require

Emergence of population structure in socio-cognitively inspired ant colony optimization

A metaheuristic proposed by us recently, Ant Colony  Optimization (ACO) hybridized with socio-cognitive inspirations, turned out to generate interesting results compared to classic ACO. Even though it does not always find better solutions to the considered problems, it usually finds sub-optimal solutions  usually. Moreover, instead of a trial-and-error approach to configure the parameters of the ant species in the population, in our approach, the actual structure of the population emerges from predefined species-to-species ant migration strategies. Experimental results of our approach are compared against classic ACO and selected socio-cognitive versions of this algorithm