Network Planning Optimization for Multimedia Networks

Author(s):  
Priscila Solis Barreto ◽  
Paulo H.P. de Carvalho
Author(s):  
Acep Hidayat ◽  
Deri Ferdina

Solok Regency irrigation network planning which has an area of irrigation land of 3738 ha. The main canals are spread in several areas, namely 43 Irrigation Channels, 17 Dams, 7 Reservoirs and 2 lakes which are still functioning in Solok Regency. The poverty rate in Solok Regency is still quite high, reaching 27,487%. The data includes secondary data on 10-year rainfall data from Kayu Aro, Bayur Maritime Bay Methodology, Padang Panjang Geophysics and 10-year climatology from Kayu Aro Climatology Station. The calculation method used is the intensity of the issen rainfall method, Evapotranspiration of the modified Penman method, the reliable discharge of the DR.FJ Mock method, the cropping pattern, and the need for irrigation water.  The most efficient and optimal planting pattern obtained is PADI-PADI-CORN with large irrigation water requirements in tertiary plots (NFR tertiary plots) ranging from 0 - 1,546 ltr / sec / ha with a maximum of 1,546 ltr / sec / ha in September II, whereas Irrigation water demand in the intake (DR intake) ranges from 0 to 2,378 ltr sec / ha with a maximum of 2,378 ltr / sec / ha in September II. The mainstay discharge available in the Pauh Tinggi Irrigation Network Planning is very abundant with the mainstay discharge (Q80) for irrigation, the maximum mainstay discharge (Q80) occurs in April I with 10.482 ltr / sec / ha and minimum in December II with 3,930 ltr / sec / ha. Based on the mainstay discharge results above it can be stated that the water balance / water balance between the mainstay discharge Q80 and the need for irrigation water experienced a large surplus.


2014 ◽  
Vol 2014 ◽  
pp. 1-21 ◽  
Author(s):  
Lianbo Ma ◽  
Hanning Chen ◽  
Kunyuan Hu ◽  
Yunlong Zhu

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness.


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