نتایج جستجو برای: dominant sorting genetic algorithm
تعداد نتایج: 1441000 فیلتر نتایج به سال:
This paper presents an evolutionary algorithm for solving multi-objective optimization problems-based artificial neural network (ANN). The multi-objective evolutionary algorithm used in this study is genetic algorithm while ANN used is radial basis function network (RBFN). The proposed algorithm named memetic elitist Pareto non-dominated sorting genetic algorithm-based RBFN (MEPGAN). The propos...
In multi-criteria sorting methods, it is often difficult for decision makers to precisely define their preferences. It is even harder to express them into parameters values. The idea of this work is to automatically find the parameters of a sorting model using classification examples. The sorting method we are working with is FlowSort, which is based on the PROMETHEE methodology. Starting with ...
In this paper we present eecient algorithms for sorting, selection and packet routing on the AROB (Array with Reconngurable Optical Buses) model.
The Batcher`s bitonic sorting algorithm is one of the best parallel sorting algorithms, for sorting random numbers in modern parallel machines. Load balancing property of bitonic sorting algorithm makes it unique among other parallel sorting algorithms. Contribution of bitonic sorting algorithm can be seen in various scientific and engineering applications. Research on a bitonic sorting algorit...
evolutionary algorithms are some of the most crucial random approaches tosolve the problems, but sometimes generate low quality solutions. on the otherhand, learning automata are adaptive decision-making devices, operating onunknown random environments, so it seems that if evolutionary and learningautomaton based algorithms are operated simultaneously, the quality of results willincrease sharpl...
determining the optimal location of wells with the aid of an automated search algorithm is a significant and difficult step in the reservoir development process. it is a computationally intensive task due to the large number of simulation runs required. therefore,the key issue to such automatic optimization is development of algorithms that can find acceptable solutions with a minimum number of...
Combining classical technologies with modern intelligent algorithms, this paper introduces a new approach for the optimisation and modelling of EAF-based steel-making process based on multi-objective using evolutionary computing machine learning. Using large amount real-world historical data containing 6423 consecutive EAF heats collected from melt shop in an established steel plant work not on...
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