Решение систем трансцендентных уравнений с помощью генетических алгоритмов
In this research we analyzed the problem of solving transcendental equations' systems. More detailed is analyzed the approach of solution based on genetic algorithms because it is less examined than the one based on numerical methods. The research will be useful for different kinds of physical and mathematical calculations containing transcendental equations of high complexity. The research is available for students and graduates who are familiar with the basics of numerical methods, mathematical analysis, discrete mathematics and combinatorial algorithms.
Abstract— In this paper a new multi-agent genetic algorithm for multi-objective optimization (MAGAMO) is presented. The algorithm based on the dynamical interaction of synchronized agents which are interdepended genetic algorithms (GAs) having own separate evolutions of their populations. This approach has some similarities with well known “island model” of GA. In both methods is used a migration of individuals from agents (“islands”) to the main process (“continent”). In contrast, the intelligent agents in MAGAMO are able to decompose the dimensions space to form evolutions of subpopulations (instead of distribution of initial population as in the standard “island model”). In the same time, the main (central) process is responsible for the coordination of agents only and their selection according Pareto rules (without evolution). Intelligent agents seek local suboptimal solutions for a global optimization, which will be completed in the result of the interaction of all agents. In the result of this, the amount of needed recalculating the fitness-functions can be significantly reduced. It is especially important for the multi-objective optimization related to a large-scale problem. Besides, the proposed approximating approach allows solving complex optimization problems for real big systems (like an oil company, plants, corporations, etc.).
In work the developed model of adaptive management by the vertically integrated companies based on the system approach supporting the mechanism of an operational management in a uniform cycle of strategic planning, within the limits of faster time is presented. Thus for a finding of optimum values of operating parameters special algorithms of a class of genetic algorithms are used, neural networks the example of the developed system of adaptive management for the vertically-integrated oil company is etc. presented.
The following topics were dealt with: human/computer interfaces; texture, depth and motor perception; neural nets; fuzzy systems; learning; product/process design; simulation; robotics; visual system cybernetics; batch processes; image compression and interpretation; AI applications; fuzzy adaptive control; decision modelling; agile manufacturing; service sector; inductive algorithms; complex systems; Petri nets; real time imaging; KBS; machine recognition; requirements engineering; inspection and shop floor control; environmental decision making; medicine; supervisory control; discrete event systems; power systems; software methods; heuristic search; vision systems; database systems; information modelling; facility design and material handling; conflict resolution; emergency management; genetic algorithms; decision making and path planning; IVHS; senses approximation; intelligent user interface; robust controllers for mechanical systems; cognitive and learning systems; command and control systems; pilot associate systems; neural net applications; real time systems; mobile robot visual processes; medical applications; utility energy systems; machine recognition; computing systems design; software engineering; military applications; data analysis; stochastic processes; guided vehicles; and stability and compensation.
This research work deals with the problem formulation of control of complex organizational structures. The mechanism of functioning of such systems is described by example of a vertically integrated company (VIC). The problems of strategic and operative control of VIC are considered. The methods for solving such problems based on genetic algorithms and neural networks are suggested. A new iterative procedure for coordination of strategic and operative control goals based on the estimation of imbalance between shareholder value and net profit distributed for payment of dividends to shareholders is suggested.
The considered system is a double criterion optimization problem with complex multiparameter restrictions.
This book constitutes the proceedings of the 16th Russian Conference on Artificial Intelligence, RCAI 2018, Moscow, Russia, in September 2018.
The 22 full papers presented along with 4 short papers in this volume were carefully reviewed and selected from 75 submissions. The conference deals with a wide range of topics, including data mining and knowledge discovery, text mining, reasoning, decision making, natural language processing, vision, intelligent robotics, multi-agent systems, machine learning, ontology engineering.
The paper is devoted to the description of an intellectual decision support system. We present the algorithms used and the results achieved in applying the system to credit screening tasks.
In this paper is presented a modern approach to designing the intelligent information system “Smarter Region” based on methods of system-dynamics and agent-based modeling. A novel CGE-model (computable general equilibrium model) of the region dynamics was developed for Krasnoyarsk Krai, Russia. Important direct and feedback relations were identified and implemented in the system. Agents in the model are branches of economics of the region and the government with its regulation policy. Using genetic optimization algorithm is solving complex optimization problem of the “Smarter Region”, which is related to maximization of the Gross Domestic Product (GDP).