In multirate multicasting, different users (receivers) in the same multicast group can receive service at different rates, depending on the user requirements and the network congestion level. Compared with unirate mul...
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In multirate multicasting, different users (receivers) in the same multicast group can receive service at different rates, depending on the user requirements and the network congestion level. Compared with unirate multicasting, this provides more flexibility to the users and allows more efficient usage of the network resources. In this paper, we simultaneously address the route selection and rate allocation problem in multirate multicast networks;that is, the problem of constructing multiple multicast trees and simultaneously allocating the rate of receivers for maximizing the sum of utilities over all receivers, subject to link capacity and delay constraints for high-bandwidth delay-sensitive applications in point-to-point communication networks. We propose a genetic algorithm for this problem and elaborate on many of the elements in order to improve solution quality and computational efficiency in applying the proposed methods to the problem. These include the genetic representation, evaluation function, genetic operators, and procedure. Additionally, a new method using an artificial intelligent search technique, called the coevolutionary algorithm, is proposed to achieve better solutions, and methods of selecting environmental individuals and evaluating fitness are developed. The results of extensive computational simulations show that the proposed algorithms provide high-quality solutions and outperform existing approach.
Launch vehicles in general are slender and flexible. The flexibility of the launch vehicle easily induces instability to the total launch vehicle system. There is also a high rate of change in mass and aerodynamic con...
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Launch vehicles in general are slender and flexible. The flexibility of the launch vehicle easily induces instability to the total launch vehicle system. There is also a high rate of change in mass and aerodynamic conditions. These conditions require an attitude controller which is robust to various system parameter changes and disturbances. In this paper, the launch vehicle's attitude controller is optimized by the coevolutionary algorithm. The attitude controller structure must be determined and the performance index is set according to the user needs. The coevolutionary algorithm can find the gains of the controller which gives the best performance. The controller is robust over a wide range of the system uncertainties. Also these uncertainties are controlled by the adaptive control using neural networks.
A mixed model assembly line is a production line where a variety of product models are produced. Line balancing and model sequencing problems are important for an efficient use of such lines. Although the two problems...
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A mixed model assembly line is a production line where a variety of product models are produced. Line balancing and model sequencing problems are important for an efficient use of such lines. Although the two problems are tightly interrelated with each other, prior researches have considered them separately or sequentially. This paper presents a new method using a coevolutionary algorithm that can solve the two problems at the same time. In the algorithm, it is important to promote population diversity and search efficiency. We adopt a localized interaction within and between populations, and develop methods of selecting symbiotic partners and evaluating fitness. Efficient genetic representations and operator schemes are also provided. When designing the schemes, we take into account the features specific to the problems. Also presented are the experimental results that demonstrate the proposed algorithm is superior to existing approaches.
This paper proposes a new symbiotic evolutionary algorithm to solve complex optimization problems. This algorithm imitates the natural evolution process of endosymbionts, which is called endosymbiotic evolutionary alg...
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This paper proposes a new symbiotic evolutionary algorithm to solve complex optimization problems. This algorithm imitates the natural evolution process of endosymbionts, which is called endosymbiotic evolutionary algorithm. Existing symbiotic algorithms take the strategy that the evolution of symbionts is separated from the host. In the natural world, prokaryotic cells that are originally independent organisms are combined into an eukaryotic cell. The basic idea of the proposed algorithm is the incorporation of the evolution of the eukaryotic cells into the existing symbiotic algorithms. In the proposed algorithm, the formation and evolution of the endosymbionts is based on fitness, as it can increase the adaptability of the individuals and the search efficiency. In addition, a localized coevolutionary strategy is employed to maintain the population diversity. Experimental results demonstrate that the proposed algorithm is a promising approach to solving complex problems that are composed of multiple sub- problems interrelated with each other.
Táto práce se zabývá vytvořením obrazových filtrů pomocí koevolučních algoritmů. Práce obsahuje popis evolučních algoritmů, zaměřený hlavně na genetické pr...
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Táto práce se zabývá vytvořením obrazových filtrů pomocí koevolučních algoritmů. Práce obsahuje popis evolučních algoritmů, zaměřený hlavně na genetické programování, kartézské genetické programování a koevoluci. Čtenář se dále seznámí s různými typy obrazových filtrů. V dalších částech práce je popsán návrh programu pro tvorbu obrazových filtrů kombinovaných s detektory šumu pomocí kartézského genetického programování a s využitím kooperativní koevoluce, implementace a testování navrženého programu. V poslední části práce jsou filtry vytvořené pomocí koevoluce s detektory šumu porovnány s filtry s detektory šumu vytvořenými bez použití koevoluce a filtry, které nepoužívají detektory.
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