An improved artificial bee colony algorithm based on Bayesian estimation

Wang, Chunfeng; Shang, Peng‐Peng; Shen, Peiping

doi:10.1007/s40747-022-00746-1

Cited by 25 publications

(10 citation statements)

References 44 publications

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“…Onlooker bees choose food sources based on nectar abundance collected from recruited bees. This is done by calculating the following probability [29]:…”

Section: Artificial Bee Colonymentioning

confidence: 99%

Enhancement of a Path-Finding Algorithm for the Hovercraft System Based on Intelligent Hybrid Stochastic Methods

2024

IJIES

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As a result of the widespread adoption of hovercraft systems, the path finding of such systems has become an essential rule for locating the target with the shortest distance and avoiding collision between the starting point and the target locations. The purpose of this paper is to develop a method for path finding by proposing a hybrid method of intelligent optimization techniques, including two stochastic approaches. The first one is called the Artificial Bee Colony (ABC) algorithm, and it aims to direct the hovercraft toward the target utilizing the behaviour of honey bees to reduce the path length required to reach the target. The second approach makes use of the Self Perception Particle Swarm Optimization (SP-PSO) algorithm, which is designed to improve particle swarm optimization in order to obtain a path that is more effective, smoother, and shorter for the hovercraft to travel in a complicated environment. The developed hybrid method is called the Artificial Bee Colony Self Perception Particle Swarm Optimization (ABC-SPPSO), which enhances the convergence speed and achieves a trade-off between exploration and exploitation in order to generate the shortest path while simultaneously avoiding collisions. The simulation results indicate that the performance of the proposed hybrid algorithm surpasses those of the original techniques. Moreover, the results demonstrate that the proposed hybrid algorithm outperforms the hybrid Firefly Algorithm Modified Chaotic Particle Swarm (HFAMCPSO) by (11.90%) in terms of distance and (56%) in terms of iterations. In addition, it outperforms the Quarter Orbit combined with the Particle Swarm Optimization (QOPSO) algorithm by (1.89%) in terms of distance and (16.66%) in terms of iterations. Furthermore, compared to the Rapid Random Tree Star Particle Swarm Optimization (RRT*PSO), the proposed method achieved enhancement of (0.82%) in terms of distance and (33.33%) in terms of iterations in providing the shortest path while avoiding collisions and reducing the trajectory tracking error to approximately zero. All the considered approaches were simulated in a global environment, utilizing the MATLAB 2022 package.

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“…Onlooker bees choose food sources based on nectar abundance collected from recruited bees. This is done by calculating the following probability [29]:…”

Section: Artificial Bee Colonymentioning

confidence: 99%

Enhancement of a Path-Finding Algorithm for the Hovercraft System Based on Intelligent Hybrid Stochastic Methods

2024

IJIES

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“…Moreover, among those linear regression models, the Bayesian regression model had higher accuracy than the other two proposed linear models. Te reason may lie in that, through establishment of a payof function, the Bayesian model is able to generate an optimal iteration algorithm to obtain desired predicted values [47].…”

Section: Forecast Performance Of Single Modelsmentioning

confidence: 99%

Information Entropy-Based Hybrid Models Improve the Accuracy of Reference Evapotranspiration Forecast

Qin,

Fan,

Zhang

2024

Advances in Meteorology

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Accurate forecasting of reference crop evapotranspiration (ET0) is vital for sustainable water resource management. In this study, four popularly used single models were selected to forecast ET0 values, including support vector regression, Bayesian linear regression, ridge regression, and lasso regression models, respectively. They all had advantages of low requirement of data input and good capability of data fitting. However, forecast errors inevitably existed in those forecasting models due to data noise or overfitting. In order to improve the forecast accuracy of models, hybrid models were proposed to integrate the advantages of the single models. Before the construction of hybrid models, each single model’s weight was determined based on two weight determination methods, namely, the variance reciprocal and information entropy weighting methods. To validate the accuracy of the proposed hybrid models, 1–30 d forecast data from January 2 to February 1, 2022, were used as a test set in Xinxiang, North China Plain. The results confirmed the feasibility of the information entropy-based hybrid model. In detail, the information entropy model generated the mean absolute percentage errors of 11.9% or a decrease by 48.9% compared to the single and variance reciprocal hybrid models. Moreover, the model generated a correlation coefficient of 0.90 for 1–30 d ET0 forecasting or an increase by 13.6% compared to other models. The standard deviation and the root mean square error of the information entropy model were 1.65 mm·d−1 and 0.61 mm·d−1 or had a decrease by 16.4% and 23.7%. The maximum precision and the F1 score were 0.9618 and 0.9742 for the information entropy model. It was concluded that the information entropy-based hybrid model had the best midterm (1–30 d) ET0 forecasting performance in the North China Plain.

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“…If the current ant is equal to local best, then formula ( 11) is used to calculate X t+1,i , otherwise if the current solution is equal to the previous solution, then formula ( 12) is used to calculate X t+1,i . Where dw is deposition weight can be calculated using formula (13).…”

Section: Anamentioning

confidence: 99%

“…All the above algorithms work depend on creatures' changing position. Many metaheuristic algorithms suffer from low exploration and many of them improved by applying levy flight to add more randomness within the algorithm like ACO improved with levy flight in [8,9], BA in [10,11], ABC for multi-objective improved by levy flight in [12] and also ABC improved by Bayesian estimation in [13], exploration of PSO improved by levy flight also [14,15], and in recent researches, MFO also improved by adding more randomness, for example, add levy flight in [16,17]. However, some algorithms had fast convergence to the best solutions while leading to local optima.…”

Section: Introductionmentioning

confidence: 99%

Moth-Flame Optimization and Ant Nesting Algorithm: A Systematic Evaluation

AbdulKarim¹,

Rashid²

2023

Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)

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In this paper, some swarm metaheuristic algorithms are analyzed to show the performance of algorithms. Swarm Intelligence algorithms are more trapping in local optima because of low exploration. The Moth-Flame Optimization algorithm is a widely applied metaheuristic algorithm. Nevertheless, the Ant Nesting Algorithm is another recent powerful algorithm. Both algorithms are theoretically and practically studied and applied to a simple optimization problem with a simple objective function. All steps of the algorithms are implemented and discussed providing results to show and compare the exploration and exploitation performance between both algorithms. A simple comparison between both algorithms is conducted using the same sample of data, and it is concluded that convergence within cycles of implementation shows that Ant Nesting Algorithm is fast converged but it might get stuck in local optima because of low exploration. Additionally, the merits of the Ant Nesting Algorithm show the algorithm will stuck in local optima when solving highly complex problems or if the initial population can't be explored efficiently. Moth-Flame Optimization also may have exploration problems when the size of flames during cycles is decreased.

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An improved artificial bee colony algorithm based on Bayesian estimation

Cited by 25 publications

References 44 publications

Enhancement of a Path-Finding Algorithm for the Hovercraft System Based on Intelligent Hybrid Stochastic Methods

Enhancement of a Path-Finding Algorithm for the Hovercraft System Based on Intelligent Hybrid Stochastic Methods

Information Entropy-Based Hybrid Models Improve the Accuracy of Reference Evapotranspiration Forecast

Moth-Flame Optimization and Ant Nesting Algorithm: A Systematic Evaluation

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