Stability analysis of Artificial Bee Colony optimization algorithm

Bansal, Jagdish Chand; Gopal, Anshul; Nagar, Atulya K.

doi:10.1016/j.swevo.2018.01.003

Cited by 63 publications

(26 citation statements)

References 31 publications

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“…Artificial bee colony algorithm is a global optimization algorithm by simulating bee foraging behavior. Since Karaboga and Basturk first proposed this algorithm in 2008, the artificial bee colony algorithm (ABC) has developed rapidly [32][33][34]37]. The artificial bee colony algorithm contains three kinds of bees: employed bees, onlookers, and scouts.…”

Section: Artificial Bee Colony Algorithmmentioning

confidence: 99%

“…Obviously, because traditional methods based on mathematical models are difficult to achieve ideal results, some new methods are incorporating bionic algorithms, such as Dragonfly algorithm [31], Artificial Bee Colony algorithm [32][33][34], Bat Algorithm [35], and Grey Wolf Algorithm [36]. Moreover, Gao et al demonstrated the superiority of the ABC algorithm in finding an optimal value [34].…”

Section: Introductionmentioning

confidence: 99%

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An Improved Multithreshold Segmentation Algorithm Based on Graph Cuts Applicable for Irregular Image

Wang

et al. 2019

Mathematical Problems in Engineering

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In order to realize the multithreshold segmentation of images, an improved segmentation algorithm based on graph cut theory using artificial bee colony is proposed. A new weight function based on gray level and the location of pixels is constructed in this paper to calculate the probability that each pixel belongs to the same region. On this basis, a new cost function is reconstructed that can use both square and nonsquare images. Then the optimal threshold of the image is obtained through searching for the minimum value of the cost function using artificial bee colony algorithm. In this paper, public dataset for segmentation and widely used images were measured separately. Experimental results show that the algorithm proposed in this paper can achieve larger Information Entropy (IE), higher Peak Signal to Noise Ratio (PSNR), higher Structural Similarity Index (SSIM), smaller Root Mean Squared Error (RMSE), and shorter time than other image segmentation algorithms.

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Section: Artificial Bee Colony Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Multithreshold Segmentation Algorithm Based on Graph Cuts Applicable for Irregular Image

Wang

et al. 2019

Mathematical Problems in Engineering

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“…An Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm [26]. ABC is an optimization technique based on a metaheuristic algorithm inspired by cooperative foraging behaviours of honey bees [26,27,28]. The ABC algorithm is applied to solve various continuous and discrete optimization problems in the areas related to neural networks such as in environmental/ economic problems, network topology design, structural engineering, image processing, forecasting and many others [29].…”

Section: B Artificial Bee Colony (Abc)mentioning

confidence: 99%

“…In ABC, food resources denote as an optimal solution and the nectar amount of each resource denote as the quality of each solution. In the ABC algorithm, there are three types of artificial bees groups, namely employed bees, onlookers and scouts [27]. The employed bees comprise the first half of the colony whereas the second half consists of the onlookers.…”

Section: B Artificial Bee Colony (Abc)mentioning

confidence: 99%

“…The employed bees comprise the first half of the colony whereas the second half consists of the onlookers. Furthermore, the ABC algorithm has four phases which are initialized, employed bee, onlooker bee and the scout bee [26,27]. In the initialization phase, ABC starts with a random selection process used by the artificial onlooker bees to discover promising regions which correspond to a potential solution.…”

Section: B Artificial Bee Colony (Abc)mentioning

confidence: 99%

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Methods in Forecasting Water Used and Electricity Production at Hydropower Plants

Joe*,

Yuzainee,

Zaini

et al. 2019

IJRTE

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An energy system in a country has complex impacts on its economy. A retrenchment of energy supply influences economic activity such as distribution and saving of energy, as well as changes in technology to emphasize energy efficiency. Despite the advantages of hydropower developments in power generation over the past decades, highly controversial issues due to various social and environmental concerns are still debatable. A challenge for hydropower developers and operators, as well as government planners and regulators, is to develop tools that promote good practice and sustainable hydropower projects in energy security. In the future, both the scarcity of water and the cost of energy will likely to become limiting factors for economic and population growth, particularly in Malaysia, where the population is projected to grow dramatically. Various climate change models suggested that clean water supplies may decrease significantly. Therefore, integrated planning between the energy and water sectors will be essential to meet the rising demands of both resources. The purpose of this study is to review and identify the method that can deal with historical data and current practices at the hydropower plants to predict future electricity production due to the predicted water used in hydropower plants at the study areas. Various available methods to forecast water used and the electricity generated in hydropower plants have been identified and discussed in this paper.

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Review on parameter estimation techniques of solar photovoltaic systems

Venkateswari

Rajasekar

2021

Int Trans Electr Energ Syst

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Beyond meeting power demand, switching to solar energy especially solar photovoltaic (PV) offers many advantages like modularity, minimal maintenance, pollution free, and zero noise. Yet, its cell modeling is critical in design, simulation analysis, evaluation, and control of solar PV system; most importantly to tap its maximum potential. However, precise PV cell modeling is complicated by PV nonlinearity, presence of large unknown model parameter, and absence of a unique method. Since number of model parameters involved is directly related to model accuracy, and efficiency; determination of its values assume high priority. Besides, application of meta-heuristic algorithms via numerical extraction is popular as it suits for any PV cell/module types and operating conditions. However, existence of many algorithms have drawn attention toward assessment of each method based on its merits, demerits, suitability/ability to parameter estimation problem, and complexity involved. Hence, few authors reviewed the subject of PV model parameter estimation.But existing reviews focused on comparative analysis of analytical and metaheuristic approaches, analysis of models, and application of meta-heuristic methods for model parameter extraction. Thus, lack a comprehensive analysis on methods based on different objective function, assessment on

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Stability analysis of Artificial Bee Colony optimization algorithm

Cited by 63 publications

References 31 publications

An Improved Multithreshold Segmentation Algorithm Based on Graph Cuts Applicable for Irregular Image

An Improved Multithreshold Segmentation Algorithm Based on Graph Cuts Applicable for Irregular Image

Methods in Forecasting Water Used and Electricity Production at Hydropower Plants

Review on parameter estimation techniques of solar photovoltaic systems

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