A new approach to solve Economic Dispatch problem using a Hybrid ACO–ABC–HS optimization algorithm

Sen, Tanuj; Mathur, H. D.

doi:10.1016/j.ijepes.2015.11.121

Cited by 91 publications

(38 citation statements)

References 28 publications

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“…Other algorithms together with ABC have been used, such as the hybrid algorithm ACO-ABC-HS, which combines the framework of ant colony optimization (ACO), artificial bee colony (ABC) and harmonic systems (HS) for solving the problem of economic dispatch for a multi-generator system in [6]. In this sense, Gaidhane et al [7] developed the grey wolf optimizer and ABC algorithm to inherit their advantages and overcome their drawbacks.…”

Section: Related Workmentioning

confidence: 99%

“…A modified ABC algorithm for constrained optimization problems was presented by Karaboga et al [3] for a set of constrained test problems. Other evolutionary algorithms, such as TLBO, were studied for the optimization of the power factor (PF) in [4].The ABC algorithm has been used for the determination of optimal location and sizing of DG units using the multi objective performance index for enhancing the voltage stability of the radial distribution system in [5].Other algorithms together with ABC have been used, such as the hybrid algorithm ACO-ABC-HS, which combines the framework of ant colony optimization (ACO), artificial bee colony (ABC) and harmonic systems (HS) for solving the problem of economic dispatch for a multi-generator system in [6]. In this sense, Gaidhane et al [7] developed the grey wolf optimizer and ABC algorithm to inherit their advantages and overcome their drawbacks.Also, the ABC algorithm is used to find the optimum PID controller parameters.…”

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confidence: 99%

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Performance Optimization LoRa Network by Artificial Bee Colony Algorithm to Determination of the Load Profiles in Dwellings

Caño

Sánchez-Sutil

2020

Energies

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This paper presents a system to improve the performance of the Long Range (LoRa) network using an algorithm derived from the artificial bee colony (ABC), which obtains a minimum packet lost rate (PLR) in the LoRa network and allows to more accurately determine load profiles of dwellings, with smaller a time measurement and less data transmission. The developed algorithm calculates the configuration parameters of the LoRa network, monitoring in real time the data traffic, and is implemented in gateway LoRa network monitor (GLNM). Intelligent measurement equipment has been developed to determine the dwelling load profiles. This energy measurement device for dwelling (EMDD) measures the variables and consumption of electricity in each home with measurement times that can be configured. This research also develops the GLNM gateway, which monitors and receives data from the EMDDs installed and uploads them to the cloud using Firebase. This developed system allows to perform demand forecasting studies, analysis of home consumption, optimization of electricity tariffs, etc., applied to smart grids.

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Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

Performance Optimization LoRa Network by Artificial Bee Colony Algorithm to Determination of the Load Profiles in Dwellings

Caño

Sánchez-Sutil

2020

Energies

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“…To optimize multilayer perceptron (MLP) artificial neural networks, five parameters, namely the number of neurons, the number of layers, transfer function, back-propagation network training function, and back-propagation weight/bias learning function must be optimized, which was done using ACO in this study (Sen & Mathur 2016). Table 3 also demonstrates the optimal values of MLP neural network.…”

Section: Classificationmentioning

confidence: 99%

Developing a Machine Vision System to Detect Weeds from Potato Plant

Sabzi

Abbaspour‐Gilandeh

Javadikia

2018

Tarım Bilimleri Dergisi

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Potato is one of the widely used products all over the world that has numerous nutritional properties. Similar to other crops, different weeds grow along with potatoes in agricultural fields. These weeds reduce the performance of crops due to competing with them to absorb water, light, and nutrients from soil. Accordingly, in this study, a machine vision system with the hybrid artificial neural network-ant colony algorithm (ANN-ACO) classifier was developed for a site-specific spraying considering the weed type. Potato plant and three weed types including Chenopodium album, Polygonum aviculare L., and Secale cereale L. were used in this study. A digital camera (SAMSUNG WB151F (CCD, 14.2 MP, 30f/s) was placed in the center of the video acquisition system. The distance between plants and the digital camera was fixed at 40 cm. For video acquisition, only lamps of white LED with a light intensity of 327 lux were selected. For filming in order to evaluate the proposed system, a 4-hectare area of Agria potato fields in Kermanshah-Iran (longitude: 7.03°E; latitude: 4.22°N) was selected. Employing the Gamma test, among 31 features, 5 features (Luminance and Hue corresponding to YIQ color space, Autocorrelation, Contrast, and Correlation) were selected. The correct classification accuracy for testing and training data using three classifiers of the hybrid ANN-ACO, radial basis function (RBF) artificial neural network, and Discriminant analysis (DA) was 99.6% and 98.13%, 97.24% and 91.23%, and 69.8% and 70.8%, respectively. The results show that the accuracy of DA statistical method is much lower than that of the hybrid ANN-ACO classifier. Consequently, the results of the present study can be used in machine vision system for the optimum spraying of herbicides.

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“…PS_ABC is hybrid algorithms do it [11]. ACO-ABC-HS is a hybrid algorithm from ACO, ABC and HS introduced in [12] that used for solving the problem of Economic Dispatch (ED) for a multi-generator system. ABC is powerful in exploration but week in exploitation for this reason tried to improve searching method for global search.…”

Section: Related Workmentioning

confidence: 99%