Solving artificial ant problem using two artificial bee colony programming versions

Boudardara, Fateh; Görkemli, Beyza

doi:10.1007/s10489-020-01741-0

Cited by 20 publications

(10 citation statements)

References 38 publications

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“…Arti cial bee colony (ABC) algorithm is a bionics adaptive arti cial intelligence technique for solving extremum problem. [24,25] The ABC has natural advantages in solving function optimization problems, and it is also the most applied eld at present, but there are almost no reports on its application in the medical eld. This algorithm can be used to solve the multivariable function problem.…”

Section: Discussionmentioning

confidence: 99%

Decision Support System for Orthgnathic diagnosis and treatment planning based on machine learning

Bi²,

Liu

et al. 2022

Preprint

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Objectives This study aims to establish a machine learn-based decision support system for the treatment of dental and maxillofacial malformations and verify its feasibility. Materials and Methods Between January 2015 to August 2020, 574 patients with spiral CT and have confirmed diagnosis of dento-maxillofacial deformities were used to train the diagnostic model based on five kind of machine learning algorithm. The diagnostic performance of the algorithm was compared with the ground truth and evaluated by accuracy, sensitivity, specifcity, and area under the ROC curve (AUC). The adaptive artificial bee colony (aABC) algorithm was used to calculate the orthognathic surgical plan based on cephalometrics in the normal population. The algorithm was used to automatically generate surgical plans for 50 patients and the plans were rated by three experienced maxillofacial surgeons. Results The binary relevance extreme gradient boosting model delivered the best overall performance. The final diagnosis success rates on six different kinds of maxillofacial deformities were all over 90%, except for maxillary overdevelopment (89.27%). Conclusions The machine learning algorithms show high accuracy and effectiveness in diagnosis and surgical plan design of dento-maxillofacial deformities. The AUC of all diagnostic types was greater than 0.88. The median score for surgical plan was 9. The scheme scores were further improved after human-computer interaction. Clinical Relevance The decision support system based on machine learning could be used for automatic diagnosis and surgical design for patients with dento-maxillofacial deformities, which will help improving diagnostic efciency and provide expertise to areas with lower medical levels.

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Section: Discussionmentioning

confidence: 99%

Decision Support System for Orthgnathic diagnosis and treatment planning based on machine learning

Bi²,

Liu

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…), and the other is the derivative-free approach (e.g., GAs, simulated annealing, evolutionary programming, evolutionary strategies, and swarm intelligence such as ACO and PSO etc. [9][10][11][12][13][14][15]). The derivative-based algorithm is also a gradient-based algorithm that can determine the search direction according to the derivative information of the objective function.…”

Section: Optimization Algorithmsmentioning

confidence: 99%

“…The SI advantages are to explore creatures' working mechanism [1][2][3][4][5]. They are relevant because of their simplicity of inspiration, flexibility, derivative-free mechanism, and local optimum avoidance such as Particle Swarm Optimization (PSO), Ant Colony Optimizer (ACO), and Artificial Bee Colony (ABC) [9][10][11][12][13][14][15]. They may fit better in particular demands [16,17].…”

Section: Introductionmentioning

confidence: 99%

Dove Swarm Optimization Algorithm

Chen

Utami

et al. 2022

IEEE Access

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“…In recent years, many methods have been used to solve path planning problems, including heuristic algorithm (e.g., A* algorithm [2], D* algorithm [3]), evolutionary algorithm (e.g., ant colony algorithm [4], particle swarm algorithm [5], artificial bee colony algorithm [6], genetic algorithm [7]), artificial potential field method [8], etc. Particle swarm optimization (PSO) is a population intelligence algorithm derived from the simulation of bird foraging behaviour.…”

Section: Introductionmentioning

confidence: 99%

An Improved Particle Swarm Optimization Algorithm for Unmanned Aerial Vehicle Route Planning

Wang

Huang

Chen

2022

J. Phys.: Conf. Ser.

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A global path planning method based on improved particle swarm optimization (PSO) algorithm was proposed to find a high quality flight trajectory in three-dimensional complex environment under multiple threats for UAV. The improved path planning algorithm combines the standard PSO with A* method to compensate for the slow convergence rate of PSO. The objective function with multiple constraints of A* method is used to evaluate the quality of the waypoint, and the objective function of PSO algorithm is designed to evaluate the quality of the candidate path. To verify the effectiveness of the improved algorithm, the improved PSO algorithm is used to compare with basic PSO algorithms. The experiment in complex environment shows it has stronger search ability, convergence ability due to the improvement of population diversity and convergence speed.

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Solving artificial ant problem using two artificial bee colony programming versions

Cited by 20 publications

References 38 publications

Decision Support System for Orthgnathic diagnosis and treatment planning based on machine learning

Decision Support System for Orthgnathic diagnosis and treatment planning based on machine learning

Dove Swarm Optimization Algorithm

An Improved Particle Swarm Optimization Algorithm for Unmanned Aerial Vehicle Route Planning

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