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Computational load balancing plays a prominent role on the success of domain decomposition based finite element computations on parallel computers. Computational load balancing is achieved through mesh partitioning. In this paper, a mesh-partitioning algorithm is proposed for generating sub-meshes with optimal shape using a steady state elite preserving evolutionary algorithm (EA). Numerical experiments have been conducted by solving several practical engineering problems. Comparisons have also been made with its multi-level counter part by solving several benchmark problems. Numerical studies indicate that performance of the proposed mesh-partitioning algorithm is either comparable or superior to the multi-level algorithm.

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The development of star cameras started in the 1970s. Their purpose was the determination of astronomic position. Using photogrammetric method, the fieldwork could be carried out quickly. The accuracy was reduced compared to the traditional methods, yet the overall time needed for measurement and processing decreased drastically. In the 2000s, some of these instruments were fitted with CCD sensors. Automatized data processing methods were developed, which provided superior speed and accuracy. Recently, the development of a simplified star camera system has been started at the HAS-BUTE PGG. In this paper some key steps of the processing of the measurements are outlined. Many of these steps can be regarded as optimization problems. For this purpose, the Differential Evolution was chosen as a fitting algorithm.

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Efficient algorithms are needed for optimization of objects and systems, because the user would like to be sure that the optimum is global. The paper shows a very well scalable discrete firefly algorithm, developed for solving a supplier selection problem. The built in general reduced gradient and evolutionary algorithms of the Excel solver are also compared solving this problem. The results show that the firefly algorithm solves the problem in the fragment of the running time of the evolutionary algorithm. In the second part of the article, a mathematical model was formulated to solve the fixed destination multidepot multiple tour multiple traveling salesmen problem (mdmMTSP).

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Abstract

Nowadays heuristic methods are one of the most used tools for the optimization of problems. The proof of that is the fact that they are widely used in chemistry, economics and energy. Among the most popular of heuristic methods belongs differential evolution, belonging to the so-called ‘evolutionary algorithms’. They can handle difficult, large-scale problems with many parameters, like the optimization of the hydro-thermal coordination of hydro and thermal power plants. As with any other method, differential evolution also has certain parameters. These parameters, among others, are the size of the population, the maximum number of generations, crossover parameter and mutation factor. The effect of these parameters on the results of an optimization using differential evolution is the focus of this paper. The hydro-thermal coordination of one hydro and one thermal power plant was used as an example to explain this issue.

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Horváth, A., Horváth Z. Optimal shape design of diesel intake ports with evolutionary algorithm, Proceedings of 5th European conference on numerical mathematics and advanced applications (ENUMATH 2003) Edited by

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Horváth A., Horváth Z. Optimal shape design of diesel intake ports with evolutionary algorithm, Proceedings of 5th European conference on numerical mathematics and advanced applications (ENUMATH 2003) Edited by

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] Ehsanolah A. , Mojtaba B. A ( 2015 ), Novel approach to capture the maximum power from variable speed wind turbines using, PIcontroller RBF neural network and GSA evolutionary algorithm . Renewable and Sustainable Energy

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study was to find the coefficients for the recently proposed equations [ 1 , 2 ] that minimizes the deviations between the predicted data and the experimental data. For this purpose, an evolutionary algorithm has been used. The statistical analysis was

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algorithm. The built in general reduced gradient and evolutionary algorithms of the Excel solver are also compared solving similar problems [ 7 ]. Hazim and Jarmai estimate the minimum structural dimensions of robot arms [ 8 ]. Many researchers have studied

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average values of flows observed. OF was calculated using the following equation: (3) O F = N S E + log N S E 2   . Parameter calibration was performed using the Differential Evolutionary algorithm (DEoptim) [ 16 ], which includes optimization functions

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