Journal:Informatica
Volume 20, Issue 4 (2009), pp. 519–538
Abstract
The article addresses the issues of combinatorial evolution of standards in transmission of multimedia information including the following: (a) brief descriptions of basic combinatorial models as multicriteria ranking, knapsack-like problems, clustering, combinatorial synthesis, multistage design, (b) a description of standard series (MPEG) for video information processing and a structural (combinatorial) description of system changes for the standards, (c) a set of system change operations (including multi-attribute description of the operations and binary relations over the operations), (d) combinatorial models for the system changes, and (e) a multistage combinatorial scheme (heuristic) for the analysis of the system changes. Expert experience is used. Numerical examples illustrate the suggested problems, models, and procedures.
Journal:Informatica
Volume 20, Issue 2 (2009), pp. 255–272
Abstract
In this paper, an efficient hybrid genetic algorithm (HGA) and its variants for the well-known combinatorial optimization problem, the quadratic assignment problem (QAP) are discussed. In particular, we tested our algorithms on a special type of QAPs, the structured quadratic assignment problems. The results from the computational experiments on this class of problems demonstrate that HGAs allow to achieve near-optimal and (pseudo-)optimal solutions at very reasonable computation times. The obtained results also confirm that the hybrid genetic algorithms are among the most suitable heuristic approaches for this type of QAPs.
Journal:Informatica
Volume 13, Issue 3 (2002), pp. 311–332
Abstract
Real life scheduling problems are solved by heuristics with parameters defined by experts, as usual. In this paper a new approach is proposed where the parameters of various heuristics and their random mixtures are optimized to reduce the average deviations from the global optimum.
In many cases the average deviation is a stochastic and multi-modal function of heuristic parameters. Thus a stochastic global optimization is needed. The Bayesian heuristic approach is developed and applied for this optimization. That is main distinctive feature of this work. The approach is illustrated by flow-shop and school scheduling examples. Two versions of school scheduling models are developed for both traditional and profiled schools. The models are tested while designing schedules for some Lithuanian schools. Quality of traditional schedules is defined by the number of teacher “windows”. Schedules of profiled schools are evaluated by user defined penalty functions. That separates clearly subjective and objective data. This is the second specific feature of the proposed approach.
The software is developed for the Internet environment and is used as a tool for research collaboration and distance graduate studies. The software is available at web-sites and can be ran by standard net browsers supporting Java language. The care is taken that interested persons could easily test the results and apply the algorithms and software for their own problems.
Journal:Informatica
Volume 1, Issue 1 (1990), pp. 89–106
Abstract
This paper briefly reviews some of the recent results on the problems and algorithms for their solution in quadratic 0-1 optimization. First, the complexity of problems is discussed. Next, some exact algorithms and heuristics are mentioned. Finally, results in the analysis of the algorithms for 0-1 quadratic problems are summarized. The papers written in Russian are considered more thoroughly here.
Journal:Informatica
Volume 1, Issue 1 (1990), pp. 20–39
Abstract
In this paper we deal with the problem of extremal parameter grouping. The problem formulation, the algorithms of parameter grouping and the fields of implementation are presented. The deterministic algorithms of extremal parameter grouping often find the local maximum of the functional, characterizing the quality of a partition. The problem has been formulated as a problem of combinatorial optimization and attempted to be solved using the simulated annealing strategy. The algorithms, realizing such a strategy and devoted to the solving of the problem concerned, are proposed and investigated.