In this paper we introduce the draft of a new graph-based algorithm for optimization of scheduling problems. Our algorithm is based on the Generalized Lifelong Planning A* algorithm, which is usually used for path planning for mobile robots. It was tested on the Job Shop Scheduling Problem against a genetic algorithm’s classic implementation. The acquired results of these experiments were compared by each algorithm’s required time (to find the best solution) as well as makespan. The comparison of these results showed that the proposed algorithm exhibited a promising convergence rate toward an optimal solution. Job shop scheduling (or the job shop problem) is an optimization problem in informatics and operations research in which jobs are assigned to resources at particular times. The makespan is the total length of the schedule (when all jobs have finished processing). In most of the tested cases, our proposed algorithm managed to find a solution faster than the genetic algorithm; in five cases, the graph-based algorithm found a solution at the same time as the genetic algorithm. Our results also showed that the manner of priority calculation had a non-negligible impact on solutions, and that an appropriately chosen priority calculation could improve them.

The paper describes the application of algorithms for object classification by using artificial neural networks. The MLP (multi layer perceptron) and RBF (radial basis function) neural networks were used. We compared results obtained by a using of learning algorithms back-propagation (BP) and K-means. The real technological scene for object classification was simulated with digitization of two-dimensional pictures. The principles and algorithms given below have been used in an application that was developed at Brno University of Technology

This paper presents an implementation of the parallelization of genetic algorithms. Three models of parallelized genetic algorithms are presented, namely the Master–Slave genetic algorithm, the Coarse-Grained genetic algorithm, and the Fine-Grained genetic algorithm. Furthermore, these models are compared with the basic serial genetic algorithm model. Four modules, Multiprocessing, Celery, PyCSP, and Scalable Concurrent Operation in Python, were investigated among the many parallelization options in Python. The Scalable Concurrent Operation in Python was selected as the most favorable option, so the models were implemented using the Python programming language, RabbitMQ, and SCOOP. Based on the implementation results and testing performed, a comparison of the hardware utilization of each deployed model is provided. The results’ implementation using SCOOP was investigated from three aspects. The first aspect was the parallelization and integration of the SCOOP module into the resulting Python module. The second was the communication within the genetic algorithm topology. The third aspect was the performance of the parallel genetic algorithm model depending on the hardware.

In this research paper, we introduce a federated learning communication protocol tailored for emergency management applications. Our primary objective is to tackle the communication challenges that arise in such critical scenarios. In order to overcome the limitations associated with centralized server architectures, we present an innovative communication protocol. This protocol empowers the framework to effectively cooperate with multiple centralized servers, fostering efficient knowledge sharing and model training while ensuring the utmost data privacy and security. By harnessing this protocol, our framework elevates the performance and resilience of vital infrastructure systems operating on the Android platform, thereby facilitating real-time operational scenarios. This research makes a substantial contribution to the field of emergency management applications, as we offer a comprehensive solution that optimizes communication and enables seamless collaboration with numerous centralized servers.