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Wyświetlanie 1-2 z 2
Tytuł:
An efficient genetic algorithm for the uncapacitated multiple allocation p-hub median problem
Autorzy:
Stanimirovic, Z.
Powiązania:
https://bibliotekanauki.pl/articles/970612.pdf
Data publikacji:
2008
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
p-hub problem
genetic algorithms
discrete location and assignment
Opis:
In this paper the Uncapacitated Multiple Allocation p-hub Median Problem (the UMApHMP) is considered. A new heuristic method based on a genetic algorithm approach (GA) for solving UMApHMP is proposed. The described GA uses binary representation of the solutions. Genetic operators which keep the feasibility of individuals in the population are designed and implemented. The mutation operator with frozen bits is used to increase the diversibility of the genetic material. The running time of the GA is improved by caching technique. Proposed GA approach is bench-marked on the well known CAB and AP data sets and compared with the existing methods for solving the UMApHMP. Computational results show that the GA quickly reaches all previously known optimal solutions, and also gives results on large scale AP instances (up to n=200, p=20) that were not considered in the literature so far.
Źródło:
Control and Cybernetics; 2008, 37, 3; 669-692
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neural network-based allocation and self-improved firefly-based optimal sizing of fuel cells in distributed generation systems
Autorzy:
Subramanyam, T. C.
Tulasi Ram, S. S.
Subrahmanyam, J. B. V.
Powiązania:
https://bibliotekanauki.pl/articles/1839111.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
DG system
fuel cells
location
sizing
multiple objectives
firefly algorithm
self-improved firefly algorithm
Opis:
The notion of Distributed Generation (DG) refers to the production of power at the level of consumption. Production of energy on-site, instead of offering it centrally, reduces the cost, internal dependencies, difficulties, inefficiencies, and risks that are related to transmission and distribution systems. In case DG is realized with fuel cells, several issues exist in respect to allocating and sizing of these fuel cells in the system. For solving those issues, dual stage intelligent technique is employed in this paper. First, the Neural Networks (NN) technique is adopted for determining the required location to place the fuel cells. Secondly, an enhanced version of Self Improved Fire-Fly (SIFF) algorithm is adopted for finding the optimal size of the fuel cells. The implemented technique is simulated in four IEEE benchmark test bus systems, and the respective performance analysis along with statistical analysis serves for validation purposes. The here proposed technique is compared with six other known algorithms, namely Particle Swarm Optimization (PSO), Firefly (FF) algorithm, Artificial Bee colony (ABC) algorithm, Improved Artificial Bee colony algorithm (IABC), Genetic Algorithm (GA) and Global Search Optimizer (GSO). The results obtained from the comparative analysis show the enhanced performance of the proposed mechanism.
Źródło:
Control and Cybernetics; 2018, 47, 4; 357-381
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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