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evolution-based scheduling of multiple variant and multiple processor programs

FUTURE GENERATION COMPUTER SYSTEMS 2001年第4期17卷 405-414页

作者： Jedrzejowicz, P Czarnowski, I Skakowski, A Szreder, H Gdynia Maritime Acad Chair Comp Sci PL-81225 Gdynia Poland

The paper proposes to manage complexity and cost issues of the fault-tolerant programs not at a single program level, but rather from the point of view of the whole set of such programs, which are to be run under time constraints. The paper introduces a family of scheduling problems called fault-tolerant programs scheduling. Since, the discussed problems are, in general, computationally difficult, a challenge is to find effective scheduling procedures. Several evolution-based algorithms solving three basic kinds of fault-tolerant programs scheduling problems have been proposed. The problems involve scheduling multiple variant or multiple processor tasks on multiple, identical processors, under time constraints. To validate the algorithms computational experiment has been conducted. Experimental results show that evolution based algorithms produce satisfactory to good solutions in a reasonable time. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： scheduling fault-tolerance evolution-based algorithms multiple processors

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The Large-Scale Optimization Problem of Product Distribution in Orders

The Large-Scale Optimization Problem of Product Distribution...

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International Conference on Applied Mathematics and Computational Science (ICAMCS.NET)

作者： Semenkina, Olga Ryzhikov, Ivan Semenkin, Eugene Joint Stock Co Nonferrous Met Plant Krastsvetmet Numer Modeling Off Krasnoyarsk Russia Reshetnev Siberian State Univ Sci & Technol Syst Anal & Control Dept Krasnoyarsk Russia

ISBN: (纸本)9781538694688

In this paper, we consider a large-scale global optimization problem, which takes place in distribution of large number of products into individual orders. In general, the amount of each product is limited and the packaging is constrained, which means that the initial problems can be reduced to constrained nonlinear optimization problem. The high dimensionality and complexity of this problem leads to developing of the specific problem-oriented optimization tool. Two different approaches of solving this problem were considered. The first one is based on the reduction of the initial problem to the discrete optimization problem with dimension is equal to the product of orders number and number of products. The second approach is based on the decomposition of the initial problem into two related problems: combinatorial problem of ranking orders and combinatorial/discrete optimization problem of optimal distributing. As the main extremum seeking approaches, the evolution based and nature-inspired algorithms were used and modified for efficiently solving the considered problem. We also propose the set of particular problems: different combinations of orders and products, which were used for the algorithms' parameters tuning. In addition, the proposed reduction approaches and related algorithms were compared in its performance on these particular problems.

关键词： constrained knapsack problem combinatorial optimization problem hierarchical optimization evolution-based algorithms decision-making support system

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Multi-objective Order Reduction Problem Solving with Restart Meta-heuristic Implementation 14

Multi-objective Order Reduction Problem Solving with Restart...

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14th International Conference on Informatics in Control, Automation and Robotics (ICINCO)

作者： Ryzhikov, Ivan Brester, Christina Semenkin, Eugene Siberian State Aerosp Univ Inst Comp Sci & Telecommun Krasnoyarsk Russia

ISBN: (纸本)9789897583049

An order reduction problem for linear time invariant models brought to the multi-objective optimization problem is considered. Each criterion is multi-extremum and complex, requires an efficient tool for estimating the parameters of the lower order system and characterizes the model adequacy for the unit-step and Dirac function inputs. A common problem definition is to estimate the lower order model coefficients by minimizing the distance between the output of this model and the initial one. We propose an evolution-based multiobjective stochastic optimization algorithm with a restart operator implemented. The algorithm performance was estimated on two order reduction problems for a single input-single output system and a multiple input-multiple output one. The effectiveness of the algorithm increased sufficiently after implementing a metaheuristic restart operator. It is shown that the proposed approach is comparable to other approaches, but allows a Pareto-front approximation to be found and not just a single solution.

关键词： Linear Time Invariant Systems System Identification Order Reduction Multi-objective Optimization evolution-based algorithms Meta-heuristic Restart Operator

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Inverse Mathematical Modelling of Hexadecane Disintegration Reaction with Cooperative Multi-objective Genetic algorithms 14th

Inverse Mathematical Modelling of Hexadecane Disintegration ...

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14th International Conference on Informatics in Control, Automation and Robotics (ICINCO)

作者： Ryzhikov, Ivan Brester, Christina Semenkin, Eugene Reshetnev Siberian State Univ Sci & Technol Krasnoyarskiy Rabochyi Ave 31 Krasnoyarsk 660037 Russia

ISBN: (纸本)9783030112929;9783030112912

In this paper, we consider the inverse mathematical modelling problem for linear dynamic systems with multiple inputs and multiple outputs. The problem of this kind appears in chemical disintegration reactions and determines product concentration changing. In general case of dynamical system modelling, one needs to identify its parameters and initial values. The reason for this is the fact that a dynamical system output is a reaction on some input function and it depends on the initial state of the system. This means that changing initial values would cause parameter changing and vice versa. At the same time, statistical approximation of initial values does not give us a reliable result because in most of the cases data is noisy and flat. To provide simultaneous estimation of parameters and initial values we propose an approach based on the reduction of the inverse modelling problem to a two-criterion extremum problem and then approximating the Pareto front with specific evolution-based algorithms. Different algorithms, such as SPEA-II, PICEA-g and NSGA-2, were applied to solve the reduced multi-objective black-box optimization problem as well as their heterogeneous and homogeneous cooperations. We compared performance of these algorithms on solving inverse modelling problems for concentrations of hexadecane disintegration reaction products in case of diffusion and static reactions. On the base of numerical experiments, we provided the analysis of algorithm performances.

关键词： Inverse mathematical modelling Linear dynamical system Identification Multi-objective optimization evolution-based algorithms

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The Large-Scale Optimization Problem of Product Distribution in Orders

The Large-Scale Optimization Problem of Product Distribution...

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International Conference on Applied Mathematics & Computational Science

作者： Olga Semenkina Ivan Ryzhikov Eugene Semenkin Numerical modeling office Joint-Stock Company Non-Ferrous Metal Plant "Krastsvetmet" System Analysis and Control Department Reshetnev Siberian State University of Science and Technology

ISBN: (数字)9781538694688

ISBN: (纸本)9781538694695

关键词： Constrained knapsack problem Combinatorial optimization problem Hierarchical optimization evolution-based algorithms Decision-making support system decision support systems algorithms

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