Combinatorial Evolutionary Methods in Wireless Mobile Computing

Vidyarthi, Geetali Dutta.; Ngom, Alioune

doi:10.1007/0-387-29026-5_3

Cited by 3 publications

(4 citation statements)

References 62 publications

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“…Holland described a simple genetic algorithm (SGA) in 1975, with an architecture that makes it very easy for readers to understand how it is implemented, and this process is shown in Fig. 3, taken from Vidyarthi et al 37 In this example of the implementation of SGA, genes are initially selected randomly before calculating the fitness function, which then identifies the elite genomes, and this is followed by evolution operations. Evolution operations usually use three methods: selection, crossover, and mutation.…”

Section: Genetic Algorithmmentioning

confidence: 99%

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A Decision Support System Based on a Genetic Algorithm for the Utilization of Leftovers

Chang

2016

IJCIS

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There are usually some leftovers (usable pieces of raw material) and scraps (unusable pieces of raw material).generated after the completion of a manufacturing process. These leftovers consist of many different types, materials, styles and sizes so the use of such materials is difficult to manage, resulting in a significant material management problem for manufacturers. This study examines ways to use these leftovers and proposes the application of a genetic algorithm to handle the problem of matching usable leftovers to incoming orders. In order to verify the feasibility of the solution, a decision support system was implemented, based on a genetic algorithm. With lots of experimental results, we conclude this method is thus of considerable value to the managers of manufacturing factories.

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Section: Genetic Algorithmmentioning

confidence: 99%

“…3. The pseudocode of SGA (Source: Vidyarthi et al 37) Our study used the general structure of the SGA presented above to estimate the fitness function, using the following steps:…”

Section: Genetic Algorithmmentioning

confidence: 99%

A Decision Support System Based on a Genetic Algorithm for the Utilization of Leftovers

Chang

2016

IJCIS

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“…Traditionally, channel assignment is made according to the cochannel interference level determined by a fixed reuse distance which is decided during network planning. Many approaches proposed in the literature to solve channel allocation problem are based on such concept [3,6,12,13,[15][16][17].…”

Section: Reviewmentioning

confidence: 99%

“…HCA [5,6,17,33,34] combines the features of both FCA and DCA techniques to overcome the drawbacks of FCA and DCA. In HCA, the set of channels is divided into two subsets [6], the Fixed Channels set (or FC set: a set of channels permanently allocated to given cells) and the Dynamic Channels set (or DC set: a set of channels available to all cells).…”

Section: Reviewmentioning

confidence: 99%

Optimized Hybrid Resource Allocation in Wireless Cellular Networks with and without Channel Reassignment

Jaekel

Bari

et al. 2010

Journal of Computer Systems, Networks, and Communications

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In cellular networks, it is important to determine an optimal channel assignment scheme so that the available channels, which are considered as “limited” resources in cellular networks, are used as efficiently as possible. The objective of the channel assignment scheme is to minimize thecall-blockingand thecall-droppingprobabilities. In this paper, we present two efficient integer linear programming (ILP) formulations, foroptimallyallocating a channel (from a pool of available channels) to an incoming call such that both “hard” and “soft” constraints are satisfied. Our first formulation, ILP1, does not allow channel reassignment of the existing calls, while our second formulation, ILP2, allows such reassignment. Both formulations can handle hard constraints, which includesco-siteandadjacent channelconstraints, in addition to the standardco-channelconstraints. The simplified problem (with only co-channel constraints) can be treated as a special case of our formulation. In addition to the hard constraints, we also consider soft constraints, such as, thepacking condition, resonance condition,andlimiting rearrangements, to further improve the network performance. We present the simulation results on a benchmark 49 cell environment with 70 channels that validate the performance of our approach.

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Optimized Hybrid Resource Allocation in Wireless Cellular Networks with and without Channel Reassignment

Jaekel

Bari

et al. 2009

2009 Sixth International Conference on Information Technology: New Generations

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Combinatorial Evolutionary Methods in Wireless Mobile Computing

Cited by 3 publications

References 62 publications

A Decision Support System Based on a Genetic Algorithm for the Utilization of Leftovers

A Decision Support System Based on a Genetic Algorithm for the Utilization of Leftovers

Optimized Hybrid Resource Allocation in Wireless Cellular Networks with and without Channel Reassignment

Optimized Hybrid Resource Allocation in Wireless Cellular Networks with and without Channel Reassignment

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