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1
Content available Localization in Wireless Sensor Networks Using Heuristic Optimization Techniques
Niewiadomska-Szynkiewicz E., Marks M., Kamola M.
Journal of Telecommunications and Information Technology
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2011
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nr 4
55-64
EN
Many applications of wireless sensor networks (WSN) require information about the geographic location of each sensor node. Devices that form WSN are expected to be remotely deployed in large numbers in a sensing field, and to self-organize to perform sensing and acting task. The goal of localization is to assign geographic coordinates to each device with unknown position in the deployment area. Recently, the popular strategy is to apply optimization algorithms to solve the localization problem. In this paper, we address issues associated with the application of heuristic techniques to accurate localization of nodes in a WSN system. We survey and discuss the location systems based on simulated annealing, genetic algorithms and evolutionary strategies. Finally, we describe and evaluate our methods that combine trilateration and heuristic optimization.
2
Content available A Software Platform for Global Optimization
Niewiadomska-Szynkiewicz E., Marks M.
Journal of Telecommunications and Information Technology
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2010
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nr 3
49-56
EN
This paper addresses issues associated with the global optimization algorithms, which are methods to find optimal solutions for given problems. It focuses on an integrated software environment - global optimization object-oriented library (GOOL), which provides the graphical user interface together with the library of solvers for convex and nonconvex, unconstrained and constrained problems. We describe the design, performance and possible applications of the GOOL system. The practical example - price management problem - is provided to illustrate the effectiveness and range of applications of our software tool.
3
Content available remote Random perturbation of the variable metric method for unconstrained nonsmooth nonconvex optimization
El Mouatasim A., Ellaia R., Souza de Cursi J. E.
International Journal of Applied Mathematics and Computer Science
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2006
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Vol. 16, no 4
463-474
EN
We consider the global optimization of a nonsmooth (nondifferentiable) nonconvex real function. We introduce a variable metric descent method adapted to nonsmooth situations, which is modified by the incorporation of suitable random perturbations. Convergence to a global minimum is established and a simple method for the generation of suitable perturbations is introduced. An algorithm is proposed and numerical results are presented, showing that the method is computationally effective and stable.
4
Content available remote Optimization problems with convex epigraphs. Application to optimal control
Kryazhimskii A. V.
International Journal of Applied Mathematics and Computer Science
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2001
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Vol. 11, no 4
773-801
EN
For a class of infinite-dimensional minimization problems with nonlinear equality constraints, an iterative algorithm for finding global solutions is suggested. A key assumption is the convexity of the "epigraph", a set in the product of the image spaces of the constraint and objective functions. A convexification method involving randomization is used. The algorithm is based on the extremal shift control principle due to N.N. Krasovskii. An application to a problem of optimal control for a bilinear control system is described.
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