FPGA Implementation of Crossover Module of Genetic Algorithm

Attarmoghaddam, Narges; Li, Kin Fun; Kanan, Awos

doi:10.3390/info10060184

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…Nevertheless, this algorithm requires a huge amount of computational resources due to the iterative and constrained calculation [98]. This can also be mirrored at hardware implementation as in Attarmoghaddam et al [99] where they only implemented one GA module.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Maximum Power Point Tracking Techniques for Photovoltaic Panel: A Review and Experimental Applications

et al. 2021

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This article contains a review of essential control techniques for maximum power point tracking (MPPT) to be applied in photovoltaic (PV) panel systems. These devices are distinguished by their capability to transform solar energy into electricity without emissions. Nevertheless, the efficiency can be enhanced provided that a suitable MPPT algorithm is well designed to obtain the maximum performance. From the analyzed MPPT algorithms, four different types were chosen for an experimental evaluation over a commercial PV system linked to a boost converter. As the reference that corresponds to the maximum power is depended on the irradiation and temperature, an artificial neural network (ANN) was used as a reference generator where a high accuracy was achieved based on real data. This was used as a tool for the implementation of sliding mode controller (SMC), fuzzy logic controller (FLC) and model predictive control (MPC). The outcomes allowed different conclusions where each controller has different advantages and disadvantages depending on the various factors related to hardware and software.

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Section: Genetic Algorithmmentioning

confidence: 99%

Maximum Power Point Tracking Techniques for Photovoltaic Panel: A Review and Experimental Applications

et al. 2021

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“…This is a very simple approach with less difficulty but it is hard to achieve the optimum minimum cost of travel. Partially mapped crossover (PMX) was suggested in [15], [16]. This procedure selects a two-point crossover operator that samples the parent chromosome into three substrings and swaps the middle substring.…”

Section: Introductionmentioning

confidence: 99%

A self adaptive new crossover operator to improve the efficiency of the genetic algorithm to find the shortest path

Chattoraj

Vinayakamurthy

2021

IJEECS

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<p>Route planning is an important part of road network. To select an optimized route several factors such as flow of traffic, speed limits of road. are concerned. Total cost of such a network depends on the number of junctions between the source and the destination. Due to the growth of the nodes in the network it becomes a tough job to determine the exact path using deterministic algorithms so in such cases genetic algorithms (GA) plays a vital role to find the optimized route. Crossover is an important operator ingenetic algorithm. The efficiency of thegenetic algorithmis directlyinfluenced by the time of a crossover operation. In this paper a new crossoveroperator closest-node pairing crossover (CNPC) is recommended which is explicitly designed to improve the performance of the genetic algorithm compared to other well-known crossover operators such as point based crossover and order crossover. The distance aspect of the network problem has been exploited in this crossover operator. This proposed technique gives a better result compared to the other crossover operator with the fitness value of 0.0048. The CNPC operator gives better rate of convergence compared to the other crossover operators.</p>

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