Unsupervised Learning-based Artificial Bee Colony for minimizing non-value-adding operations

Cheng, Chen-Yang; Pourhejazy, Pourya; Ying, Kuo‐Ching; Lin, Chia‐Chi

doi:10.1016/j.asoc.2021.107280

Cited by 23 publications

(9 citation statements)

References 44 publications

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“…Looking at figure (16) it is evident that the proposed method has received the least percentage of errors in the signal and quality of the resulting image compared to the results of the shown methods. When implementing color quantization for RGB space, Table (14) reveals that the CQ-ABC + K-Means method obtains the best values in terms of obtaining the lowest values from MSE and the largest values in terms of PQNR in most application cases as shown in tables (2,4,6,8,10,12). Whereas, CQ-ABC + K-Means can only get better values for Lena in terms of dividing into 32, 64, and 128 colors for results related MSE.…”

Section: 22mentioning

confidence: 99%

“…Since the last decade Optimization algorithms was used in many research using different techniques and studying their performance in color quantization [14].…”

Section: Introductionmentioning

confidence: 99%

“…Figure 14. S.D1,2 For images (MY PIC) onlyFigure(14) shows the comparison between the standard deviation of the original image and the resulting image, respectively, as it shows the difference and the amount of deviation between the two images and shows through the results the emergence of a small amount of change between the two deviations.…”

mentioning

confidence: 99%

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Color Quantization Based Artificial Bee Colony and Training Tools

Reda¹,

Ibrahim²

2022

IJCDS

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Color quantization addressed as a useful method to help with limited disk capacity, network bandwidth, monitors true resolution. It works in reducing the true colors to the colors that the human eyes can detect and don't change the scene. In this paper a new method for color image quantization is proposed that depends on hybrid artificial tools. It consists of three main steps, the first is creating K colors to depend on them in quantizing the required colors in this step k-means algorithm is used starting from N colors. Then Artificial Bee Colony algorithm is used to improve the center of each cluster (color) which is combined to k-means algorithm as part of its optimization. Finally, the proposed color palette is used to quantize the image depending on the result of hybrid approach and apply to k-means. Two metrics are used to optimized the method (MSE, PSNR), both of them show good results for the quality of quantized images, as well as good human perception and good running time.

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Section: 22mentioning

confidence: 99%

“…Since the last decade Optimization algorithms was used in many research using different techniques and studying their performance in color quantization [14].…”

Section: Introductionmentioning

confidence: 99%

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Color Quantization Based Artificial Bee Colony and Training Tools

Reda¹,

Ibrahim²

2022

IJCDS

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“…The main difference between these methods is that in FSS some elements are fixed and it is expected that the final solution contains such elements. In [263] the authors focus on the same problem, but apply an unsupervised learning based ABC algorithm. This method applies k-means clustering to group jobs so that the setup times between the consecutively executed jobs is decreased.…”

Section: B Metaheuristicsmentioning

confidence: 99%

Heuristic and Metaheuristic Methods for the Unrelated Machines Scheduling Problem: A Survey

Đurasević,

Jakobović

2021

Preprint

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Today scheduling problems have an immense effect on various areas of human lives, be it from their application in manufacturing and production industry, transportation, or workforce allocation. The unrelated parallel machines scheduling problem (UPMSP), which is only one of the many different problem types that exist, found its application in many areas like production industries or distributed computing. Due to the complexity of the problem, heuristic and metaheuristic methods are gaining more attention for solving it. Although this problem variant did not receive much attention as other models, recent years saw the increase of research dealing with this problem. During that time, many different problem variants, solution methods, or other interesting research directions were considered. However, no study has until now tried to systematise the research in which heuristic methods are applied for the UPMSP. The goal of this study is to provide an extensive literature review on the application of heuristic and metaheuristic methods for solving the UPMSP. The research was systematised and classified into several categories to enable an easy overview of the different problem and solution variants. Additionally, current trends and possible future research directions are also shortly outlined.

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“…These characteristics ensure that meta-heuristic algorithms can be developed freely for a variety of classifications. Several optimization algorithms have been created based on biological populations or population mechanisms, e.g., the differential evolution (DE) algorithm [7][8][9], particle swarm optimization (PSO) algorithm [10][11][12], gravitational search algorithm (GSA) [13][14][15], whale optimization algorithm (WOA) [16][17][18] and artificial bee colony (ABC) algorithm [19,20]. All these variants have achieved great success in solving practical problems in many fields such as artificial neural network learning [21][22][23][24], protein structure prediction [25][26][27], time series prediction [28][29][30], and dendritic neuron learning [31,32].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Self-Scaling Brain-Storm Optimization via a Chaotic Search Mechanism

et al. 2021

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Brain-storm optimization (BSO), which is a population-based optimization algorithm, exhibits a poor search performance, premature convergence, and a high probability of falling into local optima. To address these problems, we developed the adaptive mechanism-based BSO (ABSO) algorithm based on the chaotic local search in this study. The adjustment of the search space using the local search method based on an adaptive self-scaling mechanism balances the global search and local development performance of the ABSO algorithm, effectively preventing the algorithm from falling into local optima and improving its convergence accuracy. To verify the stability and effectiveness of the proposed ABSO algorithm, the performance was tested using 29 benchmark test functions, and the mean and standard deviation were compared with those of five other optimization algorithms. The results showed that ABSO outperforms the other algorithms in terms of stability and convergence accuracy. In addition, the performance of ABSO was further verified through a nonparametric statistical test.

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Unsupervised Learning-based Artificial Bee Colony for minimizing non-value-adding operations

Cited by 23 publications

References 44 publications

Color Quantization Based Artificial Bee Colony and Training Tools

Color Quantization Based Artificial Bee Colony and Training Tools

Heuristic and Metaheuristic Methods for the Unrelated Machines Scheduling Problem: A Survey

Adaptive Self-Scaling Brain-Storm Optimization via a Chaotic Search Mechanism

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