scholarly journals Shuffled frog leaping algorithm based on quantum rotation angle

2020 ◽  
Vol 309 ◽  
pp. 03012 ◽  
Author(s):  
Bibo Hu
Keyword(s):  
Artificial Intelligence ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Rotation Angle ◽  
Local Area ◽  
Convergence Speed ◽  
Shuffled Frog Leaping Algorithm ◽  
Bee Colony ◽  
Shuffled Frog Leaping ◽  
Artificial Intelligence Algorithm

In this paper, through the analysis of the artificial intelligence algorithm, shuffled frog leaping algorithm is effectively improved, and the position of the frog is determined by the quantum rotation angle, so as to improve the performance of the algorithm. Compared with the artificial bee colony algorithm and the shuffled frog leaping algorithm, the improved algorithm has a significant improvement in the convergence speed of the algorithm and the ability to jump out of the local area.

scholarly journals Artificial Intelligence Algorithm-Based Analysis of Ultrasonic Imaging Features for Diagnosis of Pregnancy Complicated with Brain Tumor

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lin Wu ◽  
Donghui Wei ◽  
Ning Yang ◽  
Hong Lei ◽  
Yun Wang
Keyword(s):  
Artificial Intelligence ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Diagnostic Accuracy ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Ultrasonic Image ◽  
Bee Colony ◽  
Intelligence Algorithm ◽  
Artificial Intelligence Algorithm

This research was to explore the accuracy of ultrasonic diagnosis based on artificial intelligence algorithm in the diagnosis of pregnancy complicated with brain tumors. In this study, 18 patients with pregnancy complicated with brain tumor confirmed by pathology were selected as the research object. Ultrasound contrast based on artificial bee colony algorithm was performed and diagnosed by experienced clinicians. Ultrasonic image will be reconstructed by artificial bee colony algorithm to improve its image display ability. The pathological diagnosis will be handed over to the physiological pathology laboratory of the hospital for diagnosis. The doctor’s ultrasonic diagnosis results were compared with the pathological diagnosis stage results of patients, and the results were analyzed by statistical analysis to evaluate its diagnostic value. The comparison results showed that the number and classification of benign tumors were the same, while in malignant tumors, the number diagnosis was the same, but there was one patient with diagnostic error in classification. One case of mixed glial neuron tumor was diagnosed as glial neuron tumor, and the diagnostic accuracy was 94.44% and the K value was 0.988. The diagnostic results of the two were in excellent agreement. The results show that, in the ultrasonic image diagnosis of patients with brain tumors during pregnancy based on artificial intelligence algorithm, most of them are benign and have obvious symptoms. Ultrasound has a good diagnostic accuracy and can be popularized in clinical diagnosis. The results can provide experimental data for the clinical application of ultrasonic image feature analysis based on artificial intelligence as the diagnosis of pregnancy complicated with brain tumors.

scholarly journals 15-Puzzle Problem Solving with the Artificial Bee Colony Algorithm Based on Pattern Database

2021 ◽  
Vol 27 (6) ◽  
pp. 635-645
Author(s):  
Adem Tuncer
Keyword(s):  
Artificial Intelligence ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Search Algorithms ◽  
Manhattan Distance ◽  
Small Scale ◽  
Heuristic Function ◽  
New Approach ◽  
Abc Algorithm ◽  
Bee Colony

The N-puzzle problem is one of the most classical problems in mathematics. Since the number of states in the N-puzzle is equal to the factorial of the number of tiles, traditional algorithms can only provide solutions for small-scale ones, such as 8-puzzle. Various uninformed and informed search algorithms have been applied to solve the N-puzzle, and their performances have been evaluated. Apart from traditional methods, artificial intelligence algorithms are also used for solutions. This paper introduces a new approach based on a meta-heuristic algorithm with a solving of the 15-puzzle problem. Generally, only Manhattan distance is used as the heuristic function, while in this study, a linear conflict function is used to increase the effectiveness of the heuristic function. Besides, the puzzle was divided into subsets named pattern database, and solutions were obtained for the subsets separately with the artificial bee colony (ABC) algorithm. The proposed approach reveals that the ABC algorithm is very successful in solving the 15-puzzle problem.

scholarly journals Artificial Bee Colony Algorithm for Fresh Food Distribution without Quality Loss by Delivery Route Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Sapna Katiyar ◽  
Rijwan Khan ◽  
Santosh Kumar
Keyword(s):  
Artificial Intelligence ◽  
Food Quality ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Time Window ◽  
Food Distribution ◽  
Quality Loss ◽  
Bee Colony ◽  
Fresh Food ◽  
Nature Inspired Algorithm

This paper enlightens the use of artificial intelligence (AI) for distribution of fresh foods by searching more viable route to keep intact the food attributes. In recent years, very hard-hitting competition is for food industries because of the individuals living standards and their responsiveness for fresh food products demand within stipulated time period. Food industry deals with the extensive kind of activities such as food processing, food packaging and distribution, and instrumentation and control. To meet market demand, customer satisfaction, and maintaining its own brand and ranking on global scale, artificial intelligence can play a vibrant role in decision-making by providing analytical solutions with adjusting available resources. Therefore, by integrating innovative technologies for fresh food distribution, potential benefits have been increased, and simultaneously risk associated with the food quality is reduced. Time is a major factor upon which food quality depends; hence, time required to complete the task must be minimized, and it is achieved by reducing the distance travelled; so, path optimization is the key for the overall task. Swarm intelligence (SI) is a subfield of artificial intelligence and consists of many algorithms. SI is a branch of nature-inspired algorithm, having a capability of global search, and gives optimized solution for real-time problems adaptive in nature. An artificial bee colony (ABC) optimization and cuckoo search (CS) algorithm also come into the category of SI algorithm. Researchers have implemented ABC algorithm and CS algorithm to optimize the distribution route for fresh food delivery in time window along with considering other factors: fixed number of delivery vehicles and fixed cost and fuel by covering all service locations. Results show that this research provides an efficient approach, i.e., artificial bee colony algorithm for fresh food distribution in time window without penalty and food quality loss.

Scheduling of Cellular Manufacturing With Flexible Routes Intercell Moves for Carbon Reduction in a Network Environment

2019 ◽  
Author(s):  
Jialiang Liu ◽  
Qiong Liu ◽  
Chenxin Xu ◽  
Zhaorui Dong ◽  
Mengbang Zou
Keyword(s):  
Carbon Emissions ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Cellular Manufacturing ◽  
Convergence Speed ◽  
Total Carbon ◽  
Network Environment ◽  
Bee Colony ◽  
Scheduling Model ◽  
Searching Ability

Abstract In order to rapidly share manufacturing resources among enterprises in a network environment, reduce carbon emissions and production costs, scheduling of cellular manufacturing with intercell moves is studied. Previous researches on cellular manufacturing with intercell moves either supposed that a part can only move between two cells at most one time or supposed that intercell moves of parts were on fixed paths. However, there might be several manufacturing cells with the same processing function or several same machines in different cells in a network environment. Intercell moves of parts might have flexible routes. To make the cellular manufacturing with intercell moves in a network environment, a scheduling model aiming at minimizing total carbon emissions, makespan and total costs is proposed for intercell moves with flexible routes and no restrictions on the number of intercell moves. An improved artificial bee colony algorithm (ABC) is proposed to solve the scheduling model. In order to improve searching ability of ABC, neighborhood search with an adaptive stepsize mechanism is proposed in leader bee phase and onlooker phase of the algorithm. A binary tournament selection method is designed to improve convergence speed in the onlooker bee phase. A case study is used to verify the proposed model and algorithm. The results show that improved algorithm has better performance on convergence speed and searching ability than that of original artificial bee colony algorithm.

An Efficient Hybrid Clustering Method Using an Artificial Bee Colony Algorithm and Mantegna Lévy Distribution

2016 ◽  
Vol 25 (02) ◽  
pp. 1550034 ◽  
Author(s):  
Habib Ghafarzadeh ◽  
Asgarali Bouyer
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Convergence Speed ◽  
Functional Evaluation ◽  
Clustering Method ◽  
Abc Algorithm ◽  
Lévy Distribution ◽  
Bee Colony ◽  
Levy Distribution ◽  
Speed Accuracy

Data clustering is a common data mining techniques used in many applications such as data analysis and pattern recognition. K-means algorithm is the common clustering method which has fallen into the trap of local optimization and does not always create the optimized response to the problem, although having more advantages such as high speed. Artificial bee colony (ABC) is a novel biological-inspired optimization algorithm, having the advantage of less control parameters, strong global optimization ability and easy to implement. However, there are still some problems in ABC algorithm, like inability to find the best solution from all possible solutions. Due to the large step of searching equation in ABC, the chance of skipping the true solution is high. Therefore, in this paper, to balance the diversity and convergence ability of the ABC, Mantegna Lévy distribution random walk is proposed and incorporated with ABC. The new algorithm, ABCL, brings the power of the Artificial Bee Colony algorithm to the K-means algorithm. The proposed algorithm benefits from Mantegna Lévy distribution to promote the ABC algorithm in solving the number of functional evaluation and also obtaining better convergence speed and high accuracy in a short time. We empirically evaluate the performance of our proposed method on nine standard datasets taken from the UCI Machine Learning Repository. The experimental results show that the proposed algorithm has ability to obtain better results in terms of convergence speed, accuracy, and reducing the number of functional evaluation.

scholarly journals Improving Artificial Bee Colony Algorithm Using a Dynamic Reduction Strategy for Dimension Perturbation

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Gan Yu ◽  
Hongzhi Zhou ◽  
Hui Wang
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Food Source ◽  
Artificial Bee Colony ◽  
Convergence Speed ◽  
One Dimension ◽  
Exploration And Exploitation ◽  
Abc Algorithm ◽  
Reduction Strategy ◽  
One Dimensional ◽  
Bee Colony

To accelerate the convergence speed of Artificial Bee Colony (ABC) algorithm, this paper proposes a Dynamic Reduction (DR) strategy for dimension perturbation. In the standard ABC, a new solution (food source) is obtained by modifying one dimension of its parent solution. Based on one-dimensional perturbation, both new solutions and their parent solutions have high similarities. This will easily cause slow convergence speed. In our DR strategy, the number of dimension perturbations is assigned a large value at the initial search stage. More dimension perturbations can result in larger differences between offspring and their parent solutions. With the growth of iterations, the number of dimension perturbations dynamically decreases. Less dimension perturbations can reduce the dissimilarities between offspring and their parent solutions. Based on the DR, it can achieve a balance between exploration and exploitation by dynamically changing the number of dimension perturbations. To validate the proposed DR strategy, we embed it into the standard ABC and three well-known ABC variants. Experimental study shows that the proposed DR strategy can efficiently accelerate the convergence and improve the accuracy of solutions.

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