Fitness based gravitational search algorithm

Gupta, Aditi; Sharma, Nirmala; Sharma, Harish

doi:10.1109/ccaa.2016.7813735

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…This feature makes FSADE a more efficient and robust optimization algorithm compared to traditional DE. FSADE has shown promising results on a set of benchmark functions and can be applied to various optimization problems in different fields [8] . Premature convergence is the most common drawback of the population based stochastic algorithm.…”

Section: Overview Of Grey Wolf Optimizationmentioning

confidence: 99%

Advancement on Grey Wolf Optimization with Fitness Based Self Adaptive Differential Evolution

Mohanta¹,

Sharma²

2023

IJSREM

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Hybridization of two or more variants of algorithms is the recent trend of the research field. With the help of a hybridized algorithm we try to find out the better optimal solution and to solve various optimization applications. In this paper, a new approach Advancement on Grey Wolf Optimization with Fitness Based Self Adaptive Differential Evolution (AGWO-FSADE) is proposed. Fitness of populations are calculated using the self adaptive strategy of FSADE and updated by GWO algorithm. FSADE algorithms balance the convergence and diversity capability and due to fine tuning of Crossover Probability CR and Scale Factor F therefore, in large step size very less chance to skip the actual solutions. The performance of AGWO-FSADE is measured by 19 Benchmark functions and compared with classic GWO, ABC and PSOGWO algorithm. The results are more accurate to solve these functions. Keywords - nature inspired algorithm, self adaptive technique, grey wolf optimizer, fitness based self adaptive differential evolution, optimization technique

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Section: Overview Of Grey Wolf Optimizationmentioning

confidence: 99%

Advancement on Grey Wolf Optimization with Fitness Based Self Adaptive Differential Evolution

Mohanta¹,

Sharma²

2023

IJSREM

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“…GSA is a bioinspired algorithm (BIA) following the Newtonian laws of gravitation (3) and motion (4) [24][25][26]. It considers that the gravitational force (F) is directly proportional to the interaction of active mass (m a ) and passive mass (m p ) as affected by gravitational constant G(t) and the inverse square of the distance between the 2 mass agents.…”

Section: Aquaponic Water Macronutrient Prediction Using Machine Learningmentioning

confidence: 99%

Determination of Aquaponic Water Macronutrient Concentrations Based on Lactuca Sativa Leaf Photosynthetic Signatures using Hybrid Gravitational Search and Recurrent Neural Network

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Dadios

Cuello

et al. 2021

Walailak J Sci & Tech

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Crop quality depends dominantly on the nutrients present in its growth media. For precision farming, fertigation is a challenge, especially when dealing with economical and efficiency factors. In this study, the aquaponic pond water macronutrient prediction model (wNPK) was developed based on leaf photosynthetic signature predictors. Aquaphotomics was preliminarily used for correlating physical limnological properties with nitrate, phosphate, potassium concentrations, and the leaf signatures. Using a digital camera, 18 spectro-textural-morphological features were extracted. Neighborhood component analysis (NCA) and ReliefF algorithms selected the spectral components blue, a*, and red minus luma as the most significant as supported by principal component analysis, resulting in low computational cost. A Gravitational Search Algorithm (GSA) was employed to optimize the recurrent neural network (RNN) architecture resulting in higher sensitivity. The hybrid NCA-ReliefF-GSA-RNN (wNPK) predicted NPK with 93.61, 84.03, and 91.39 % accuracy, respectively, besting out other configured feature-based machine learning models. Using wNPK, it was confirmed that potassium helped in accelerating seed germination and nitrogen in promoting chlorophyll intensification, especially on the 6th week after sowing. Phosphate and potassium were the energy and health elements that were consumed in a larger amount at the end of the head development stage. wNPK rules out that macronutrient concentration have a direct resemblance to crop leaf signatures; thus, a leaf is a good indicator of the water quality. The results pointed out that the use of a single camera to measure both water macronutrient concentrations and crop signature at the same time is an innovative, efficient, and economical approach for precision farming.Highlights Aquaponic pond water nutrient estimation based on leaf photosynthetic signatures Macronutrient biomarker extraction through UV-Vis-NIR aquaphotomics Highly accurate macronutrient prediction using hybrid gravitational search and RNN Potassium promotes seed germination and nitrogen in chlorophyll intensification Phosphate and potassium are consumed in greater scale during head development Graphical abstract

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Exploitative Gravitational Search Algorithm

Gupta

Sharma

2017

Advances in Intelligent Systems and Computing

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Fitness based gravitational search algorithm

Cited by 6 publications

References 10 publications

Advancement on Grey Wolf Optimization with Fitness Based Self Adaptive Differential Evolution

Advancement on Grey Wolf Optimization with Fitness Based Self Adaptive Differential Evolution

Determination of Aquaponic Water Macronutrient Concentrations Based on Lactuca Sativa Leaf Photosynthetic Signatures using Hybrid Gravitational Search and Recurrent Neural Network

Exploitative Gravitational Search Algorithm

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