Разработка адаптивного генетического оптимизационного алгоритма с использованием методов агентного моделирования
This article presents a new approach to developing an adaptive genetic optimization algorithm (MAGAMO/A) using agent modeling techniques. The peculiarity of this approach is the support of the mechanism of adaptive control of key characteristics of GA, in particular, the values of the probabilities of crossover operators and mutations, their types and other important characteristics that affect the population diversity and the rate of convergence of GA. Support for adaptive control is provided by using the mechanism of agent state charts and the specified rules of transition between the corresponding states that determine the values of the control parameters of the GA at the individual level of each agent-process. The review of the most popular GAs used for multicriteria optimization, including SPEA2, NSGA, MOEA, etc., is reviewed. The main metrics for evaluating the effectiveness of such GAs (Hypervolume, Generational Distance, distance between solutions on the Pareto boundary, etc.) are considered. The efficiency of the developed approach in the solution of optimization problems of large dimension on several test examples and in comparison with other known GA is demonstrated. The main directions of further research in the field of development of agent-oriented genetic algorithms are formulated.
This article presents an integrated dynamic model of eco-economic system of the Republic of Armenia (RA). This model is constructed using system dynamics methods, which allow to consider the major feedback related to key characteristics of eco-economic system. Such model is a two-objective optimization problem where as target functions the level of air pollution and gross profit of national economy are considered. The air pollution is minimized due to modernization of stationary and mobile sources of pollution at simultaneous maximization of gross profit of national economy. At the same time considered eco-economic system is characterized by the presence of internal constraints that must be accounted at acceptance of strategic decisions. As a result, we proposed a systematic approach that allows forming sustainable solutions for the development of the production sector of RA while minimizing the impact on the environment. With the proposed approach, in particular, we can form a plan for optimal enterprise modernization and predict long-term dynamics of harmful emissions into the atmosphere.
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.
Issues of interfunctional conflicts, connected with the management of material flows, are topical for many companies that operate in the Russian market. In order to finish the conflict, align the standpoints of its participants, it is required firstly to identify their goals and interests. The article is devoted to the ways of revelation of the choice criteria for the company’s functional departments – parties of the conflict, as well as to the means of their preferences formalization.
In this work, in order of development of the previously proposed decision support system to counteract the development of infectious diseases (DSS «CDID») it is proposed evolutionary model (EM), that extends the capabilities of forecast – analytical studies on the spread of infectious disease processes for individual cities and areas of the country as a whole, as well as early assessment of ways solutions to the problems of prophylaxis and therapy in the study territories.
Recent results on non-convex multi-objective optimization problems and methods are presented in this book, with particular attention to expensive black-box objective functions. Multi-objective optimization methods facilitate designers, engineers, and researchers to make decisions on appropriate trade-offs between various conflicting goals. A variety of deterministic and stochastic multi-objective optimization methods are developed in this book. Beginning with basic concepts and a review of non-convex single-objective optimization problems; this book moves on to cover multi-objective branch and bound algorithms, worst-case optimal algorithms (for Lipschitz functions and bi-objective problems), statistical models based algorithms, and probabilistic branch and bound approach. Detailed descriptions of new algorithms for non-convex multi-objective optimization, their theoretical substantiation, and examples for practical applications to the cell formation problem in manufacturing engineering, the process design in chemical engineering, and business process management are included to aide researchers and graduate students in mathematics, computer science, engineering, economics, and business management.
The earliest approaches to the cell formation problem in group technology, dealing with a binary machine-part incidence matrix, were aimed only at minimizing the number of intercell moves (exceptional elements in the block-diagonalized matrix). Later on this goal was extended to simultaneous minimization of the numbers of exceptions and voids, and minimization of intercell moves and within-cell load variation, respectively. In this paper we design the first exact branch-and-bound algorithm to create a Pareto-optimal front for the bi-criterion cell formation problem.
This book constitutes the refereed proceedings of the 4th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2016, held in Mexico City, Mexico, in November 2016.
The 18 full papers presented were carefully reviewed and selected from 56 submissions.
Accepted papers were grouped into various subtopics including information retrieval, machine learning, pattern recognition, knowledge discovery, classification, clustering, image processing, network security, speech processing, natural language processing, language, cognition and computation, fuzzy sets, and business intelligence.
This proceedings publication is a compilation of selected contributions from the “Third International Conference on the Dynamics of Information Systems” which took place at the University of Florida, Gainesville, February 16–18, 2011. The purpose of this conference was to bring together scientists and engineers from industry, government, and academia in order to exchange new discoveries and results in a broad range of topics relevant to the theory and practice of dynamics of information systems. Dynamics of Information Systems: Mathematical Foundation presents state-of-the art research and is intended for graduate students and researchers interested in some of the most recent discoveries in information theory and dynamical systems. Scientists in other disciplines may also benefit from the applications of new developments to their own area of study.