Optimization of Garbage Removal Within A Territorial Community

This paper proposes an algorithm for optimizing the garbage collection route in a local community (or a separate settlement). The study was conducted for one garbage truck. To achieve the maximum efficiency of the algorithm, it has been assumed that the points of discharge of collected waste by a garbage truck could be arranged along the way between the proposed clusters of garbage collection points. The optimization of the built routes has been proven, taking into consideration the above assumptions. The study's results could be used to reduce the budget expenditures by territorial community authorities for the collection and disposal of waste. The reported solutions could significantly shorten the garbage collection time, which would improve the environmental and aesthetic situation within the study area. The use of a new algorithm makes it possible to display the results both in quantitative and qualitative forms. An improved k-means algorithm with a maximum cluster size was selected for clustering. Each cluster was built on the basis of the maximal value of garbage truck tonnage. That means that the size of the cluster would be determined by the value of the maximum amount of waste that can be removed by a garbage truck in one run. A task of the traveling salesman was applied to find the shortest path between representatives of one cluster (garbage collection points) calling at all its points and to establish the optimal path between all the clusters formed for a territorial community. The issue related to efficient waste disposal in local communities tends to aggravate rapidly while the task to optimize garbage collection and removal is becoming increasingly acute. This is because at present, along with the increase in the global population, all types of production are increasing their volumes, which, in turn, leads to an increase in the amount of waste, in particular, household

Modeling of the Process of Territorial Communities Formation Using Swarm Intelligence Algorithms

The process of TC formation is considered, using algorithms of swarm intelligence. The main aim of TC formation is reducing the budget and saving the public funds. The approved methodology and the process of formation of capable communities are studied when in the human settlements that form the society is the administrative building, the health care institution, the general education school of the third degree, the kindergarten, the institutions of social protection, housing and communal services, taking into account the financial security and daily migration of residents in the zone of accessibility of the administrative center. The minimum distance from the center of the community to other settlements is taken for the purpose of forming territorial communities. A mathematical model of such problem is developed, using specific limitations that arise from the formulation of the problem itself. To build effective algorithms for formation of territorial communities, the concept of independence of communities, as well as the contiguity of individual councils is introduced. Stochastic algorithms of ant colony and migrating birds have been adapted to solve the established multicriteria optimization problem. The proposed approach is investigated

Development of a Method for Calculating the Safe Position of Military Units by Using Artificial Neural Networks Based on Swarm Algorithms

The object of research is development of a method for finding a safe position for military units in combat conditions, using swarm algorithms and neural networks. One of the most problematic places is the complexity of testing the developed method. The difficulty lies in the fact that to check the method in real time, financial costs and military weapons are necessary.The data are obtained due to a multicriteria problem, which allowed to calculate the errors of subjects and objects of research.The obtained results show that the hybrid method allowed to calculate the safe position with greater accuracy, namely by 25–50 % more accurately than using the classical approach. This is due to the fact that the proposed method calculates all possible errors.This makes it possible to obtain the flexibility of the method for finding a safe position. In comparison with the analogous methods known in the formulation of the classical problem of calculating the trajectory and the damage region, only one mathematical value (region, trajectory) is taken into account, and using a hybrid approach one can take into account a number of errors simultaneously. This approach ensures the flexibility of the system and the possibility of expanding a number of mathematical calculations and improving the accuracy of the result

Big Data Analytics Ontology

The object of this research is the Big Data (BD) analysis processes. One of the most problematic places is the lack of a clear classification of BD analysis methods, the presence of which will greatly facilitate the selection of an optimal and efficient algorithm for analyzing these data depending on their structure.In the course of the study, Data Mining methods, Technologies Tech Mining, MapReduce technology, data visualization, other technologies and analysis techniques were used. This allows to determine their main characteristics and features for constructing a formal analysis model for Big Data. The rules for analyzing Big Data in the form of an ontological knowledge base are developed with the aim of using it to process and analyze any data.A classifier for forming a set of Big Data analysis rules has been obtained. Each BD has a set of parameters and criteria that determine the methods and technologies of analysis. The very purpose of BD, its structure and content determine the techniques and technologies for further analysis. Thanks to the developed ontology of the knowledge base of BD analysis with Protégé 3.4.7 and the set of RABD rules built in them, the process of selecting the methodologies and technologies for further analysis is shortened and the analysis of the selected BD is automated. This is due to the fact that the proposed approach to the analysis of Big Data has a number of features, in particular ontological knowledge base based on modern methods of artificial intelligence.Thanks to this, it is possible to obtain a complete set of Big Data analysis rules. This is possible only if the parameters and criteria of a specific Big Data are analyzed clearly

Method of Functioning of Intelligent Agents, Designed to Solve Action Planning Problems Based on Ontological Approach

The problem of operation of intelligent agents of action planning with the use of ontological approach was studied. Operation of intelligent agents is possible based on the knowledge of the subject-area, in other words, the knowledge base is used. Ontologies became the standard of knowledge base. Therefore, there arises the problem of development of methods and means of operation of intelligent systems based on ontologies, in particular intelligent agents of action planning.The method of functioning of intelligent agents of action planning based on ontologies was developed. For this purpose, weights of importance of concepts and relationships were introduced to the structure of ontology. These weights are used for finding a path in the space of states. The space of states itself is built by using the language of requests to ontology. Optimization problem, which assigns the rational behavior of an intelligent agent, is two-criterial. To solve it, we chose the method of the main component, if objective functions may be evaluated, or the method of complex criterion, if these functions are impossible to evaluate.Dimensionality of the space of states depends on the completeness of the ontology, and behavior effectiveness of an intelligent agent depends on the relevance of ontology. With this aim, in the course of automated development of ontology, we developed a method for evaluation of reliability of information sources that are used for developing ontologies. As a result of the studies, it was found that this approach allows us to increase operational efficiency of intelligent agents, if the process of ontology development is relevant to the needs of a subject domain.The developed approach may serve as a base for constructing a unified methodology for development of intelligent agents of action planning if ontology of a subject domain is the central component of this software comple