Optimization of Rubber Compound Design Process Using Artificial Neural Network and Genetic Algorithm

Ghaffarian, Najmeh; Hamedi, Maryam

doi:10.5829/ije.2020.33.11b.22

Cited by 2 publications

(3 citation statements)

References 21 publications

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“…This step is a critical part of the genetic algorithm, where genetic recombination is performed to create potential variations and enhance the quality of solutions. By obtaining the best composition of values through the crossover process, the genetic algorithm can generate more unique and optimal Karawo motif patterns [31].…”

Section: Crossovermentioning

confidence: 99%

Artificial intelligence-based Karawo motif formation using genetic algorithm

Latief,

Syahrul,

Mappalotteng

2024

IJEECS

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This research explores the application of artificial intelligence in generating Karawo motifs, a traditional Indonesian pattern. The research involves collecting a dataset of existing Karawo motifs and utilizing genetic algorithms to evolve and create novel pattern variations. The generated motifs are evaluated based on their adherence to traditional design principles and aesthetic appeal. The formation of Karawo motifs begins with randomly selecting image data from a database. Then, the selection of transformation treatments is performed by optimizing the fitness function within the genetic algorithm. The applied types of transformations include geometric transformations, Boolean transformations, and arithmetic transformations. The outlined genetic algorithm steps include determining the fitness function, performing its evaluation, selecting fitness values, applying crossover, implementing mutation, managing survivor selection, and terminating iterations. The results indicate that the developed system is capable of creating diverse and appealing Karawo motif patterns, showcasing the potential of combining traditional artistry with artificial intelligence. This study has the potential to expand the possibilities of Karawo motif design and contribute to the preservation and promotion of Indonesian cultural heritage.

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Section: Crossovermentioning

confidence: 99%

Artificial intelligence-based Karawo motif formation using genetic algorithm

Latief,

Syahrul,

Mappalotteng

2024

IJEECS

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“…The second objective function minimizes each unit's emission function in grams per hour [20]. The emission function is shown in Equation (2).…”

Section: Emission Functionmentioning

confidence: 99%

“…Optimizing the placement of Bank Voltage Regulators and Capacitors based on FSM and MMOPSO [19]. Optimization of the Rubber Compound Design Process was conducted Using Artificial Neural Networks and Genetic Algorithms [20].…”

Section: Introductionmentioning

confidence: 99%

Generator Scheduling Optimization Involving Emission to Determine Emission Reduction Costs

Puspitasari

Raharjo

Sastrosubroto

et al. 2022

IJE

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Climate change, greenhouse gases, and global warming are global issues today. Of course, this global issue cannot be separated from the issue of emissions. Various methods to solve generator scheduling problems by considering emissions or Economic Emission Dispatch (EED) have been published, but not to the extent of calculating the cost to reduce emissions. The main objective of this research is to determine the cost of reducing the emission of electricity generation in Indonesia through solving the EED problem. The method proposed to solve the EED problem is an annealing simulation algorithm and tested using an electrical system of eight generators, four different loads, and five combinations of cost and emission weights. This method is tested with various loads (conditions), and each condition is tested with various combinations of cost weights and emission weights. The obtained results were compared with the results of the calculation of the Cuckoo algorithm, and the whale optimization algorithm. The simulation results show that it costs US$258.81 to reduce 1 ton of emissions. This paper can be used as a material for further consideration for the government and generator providers in making policies related to the operation of power plants by considering emissions.

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Optimization of Rubber Compound Design Process Using Artificial Neural Network and Genetic Algorithm

Cited by 2 publications

References 21 publications

Artificial intelligence-based Karawo motif formation using genetic algorithm

Artificial intelligence-based Karawo motif formation using genetic algorithm

Generator Scheduling Optimization Involving Emission to Determine Emission Reduction Costs

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