Generator Contribution Based Congestion Management using Multiobjective Genetic Algorithm

Sen, Sourav; Roy, Priyanka; Chakrabarti, A.; Sengupta, Samarjit

doi:10.12928/telkomnika.v9i1.635

Cited by 19 publications

(7 citation statements)

References 12 publications

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“…This calculation aims to determine the ability of each individual to survive. In this study, the fitness value to be achieved is the total cost and the rest of the charge on each delivery of frozen foods (Sen et al 2011). It is intended that the cost of distribution to each customer becomes more optimal.…”

Section: Optimization Using Gasmentioning

confidence: 99%

Optimization of Frozen Food Distribution Using Genetic Algorithms

Lesmawati

Rahmi

Mahmudy

2016

JEEST

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Nowadays, 30% of Indonesian consume frozen foods because it is more practical and efficient. The increase of demand requires a good distribution system. The distribution problem can be solved using genetic algorithms. Crossover method used in this study is one-cut point crossover, the mutation method used is reciprocal exchange mutation and the selection method used is elitism selection. The data used in this study is 15 of customers, 11 of product types, 5 vehicles and distance data between regions. From the tests, we found that optimal results are achieved using the population size of 1950, 700 generations, a combination of crossover rate (cr) = 0.5 and mutation rate (mr) = 0.6. The final result is a combination of the customer and vehicle order that distribute all products to all customers with the minimum total distance and cost.

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Section: Optimization Using Gasmentioning

confidence: 99%

Optimization of Frozen Food Distribution Using Genetic Algorithms

Lesmawati

Rahmi

Mahmudy

2016

JEEST

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“…Although GA-crawler does not add new terms like the Gcrawler [16] and the MultiCrawler Agent (MCA) [17] do, it is expected to maintain a good tracking throughout Web links. In different field, a multi-objective GA for generator contribution based congestion management was proposed by Sen et al [18]. The algorithm optimizes both real and reactive losses using optimal power flow model.…”

Section: Proposed Genetic Algorithmmentioning

confidence: 99%

Focused Crawler Optimization Using Genetic Algorithm

2011

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“…The optimization problem can be classified into two categories: the single-objective optimization problem as in [1][2] and the multi-objective optimization problem [3]. Many realworld problems with several conflicting objectives to be optimized at the same time are called the multi-objective optimization problem (MOP).…”

Section: Introductionmentioning

confidence: 99%