Luminance Optimization in Closed Environments by Simulated Annealing

Filho, Cícero Ferreira Fernandes Costa; Albuquerque, A T de; Costa, Marly Guimarães Fernandes

doi:10.1109/icnc.2008.853

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…However, metaheuristic strategies are used in artificial intelligence, such as the simulated annealing algorithm (Costa Filho et al, 2010;Guedes et al, 2011) and the genetic algorithm (Chakrapani and Rajan, 2008;Ruiz-Cárdenas, 2010, Guedes et al, 2011, which use an iterative search method to determine the optimal solution.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Sample Design Sizes and Shapes for Regionalized Variables Using Simulated Annealing

2014

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The spatial variability of structures in regionalized variables are defined with the aid of geostatistical techniques, which facilitate the estimation of values for these variables in unsampled localizations and generate thematic maps to be used in decision making for localized treatments in the area under study. The quality of these maps depends on the trustworthiness of these estimates that can be modified with the choice for the sample design. The objective of this work was to establish an optimal size and shape of the sample designs in order to enhance the efficiency of sampling plans for the prediction of space dependent variables. These designs were obtained with the use of a stochastic search method called Simulated Annealing. This method is based on a sampling grid with a large number of points. Here, it is initially used to consider simulated data sets with distinct spatial dependence structures and is then used to consider real data on soy productivity. The simulated results are used as reference for the achievement of the best sample design with the lowest number of sample points that can efficiently represent the spatial dependence structure of soy productivity in a commercial area harvested by the harvester monitor. The results reported for the simulations and soy productivity data show that the optimization process was efficient in determining sample designs with reduced size, especially when using the Global Accuracy as the measurement to be maximized.

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Section: Introductionmentioning

confidence: 99%

Optimization of Sample Design Sizes and Shapes for Regionalized Variables Using Simulated Annealing

2014

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Metaheuristic Optimization of Ammonia Factor as a Eutrophication Pollution Emission Descriptor for Trophic State Stability

Concepcion¹,

Lauguico²,

Alejandrino³

et al. 2020

IJESD

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Aquaponic toxicity relies on the combinations of its pollution parameters that are dissolved in water and emitted in air. Ammonia is considered as an important indicator affecting aquaculture species, water nutrient imbalance and air pollution. Trophic state of aquatic body is measured by ammonia. In this study, the suitability of metaheuristic models, namely, genetic algorithm, simulated annealing, water cycle algorithm, enhanced vibrating particles system and particle swarm optimization, in determining the optimum condition of ammonia factor for providing minimal toxicity and oligotrophication was determined by varying its corresponding hyperparameters. The parameters that were optimized are water temperature and pH level. These parameters significantly affect ammonia factor that is an essential contributor to eutrophication. The optimized genetic algorithm yielded the practical-ideal fitness function value for ammonia factor as to compare with other optimized metaheuristics based on optimizing time. It selected the 50 fittest individuals based on their fitness score with the rate of 0.2 and proceeds to recombination process to extract characteristics from parent chromosomes with crossover rate of 0.8. The mutation rate of 0.01 was injected to form diversity and to test if the global solution was attained. The tournament size is 4 and the reproduction elite count is 2.5. The best condition of the ammonia factor was extracted when the number of generations has been reached. The GA results showed that the optimum condition for ammonia factor that will prevent eutrophication and provide ecological balance in aquaponic system needs a temperature of 29.254 °C and pH of 7.614.

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Luminance Optimization in Closed Environments by Simulated Annealing

Cited by 2 publications

References 3 publications

Optimization of Sample Design Sizes and Shapes for Regionalized Variables Using Simulated Annealing

Optimization of Sample Design Sizes and Shapes for Regionalized Variables Using Simulated Annealing

Metaheuristic Optimization of Ammonia Factor as a Eutrophication Pollution Emission Descriptor for Trophic State Stability

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