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.

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 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 Metaheuristic algorithm for solving the multi-objective vehicle routing problem with time window and drones

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142092003
Author(s):  
Yun-qi Han ◽  
Jun-qing Li ◽  
Zhengmin Liu ◽  
Chuang Liu ◽  
Jie Tian
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Time Window ◽  
Total Energy Consumption ◽  
Routing Problem ◽  
Bee Colony

In some special rescue scenarios, the needed goods should be transported by drones because of the landform. Therefore, in this study, we investigate a multi-objective vehicle routing problem with time window and drone transportation constraints. The vehicles are used to transport the goods and drones to customer locations, while the drones are used to transport goods vertically and timely to the customer. Three types of objectives are considered simultaneously, including minimization of the total energy consumption of the trucks, total energy consumption of the drones, and the total number of trucks. An improved artificial bee colony algorithm is designed to solve the problem. In the proposed algorithm, each solution is represented by a two-dimensional vector, and the initialization method based on the Push-Forward Insertion Heuristic is embedded. To enhance the exploitation abilities, an improved employed heuristic is developed to perform detailed local search. Meanwhile, a novel scout bee strategy is presented to improve the global search abilities of the proposed algorithm. Several instances extended from the Solomon instances are used to test the performance of the proposed improved artificial bee colony algorithm. Experimental comparisons with the other efficient algorithms in the literature verify the competitive performance of the proposed algorithm.

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.

Path Planning of Mobile Robot Using Improved Artificial Bee Colony Algorithm

2020 ◽  
Vol 38 (9A) ◽  
pp. 1384-1395
Author(s):  
Rakaa T. Kamil ◽  
Mohamed J. Mohamed ◽  
Bashra K. Oleiwi
Keyword(s):  
Mobile Robot ◽  
Comparative Research ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Dynamic Control ◽  
Computational Time ◽  
Bee Colony ◽  
Shortest Distance ◽  
Minimum Number ◽  
Cubic Polynomial

A modified version of the artificial Bee Colony Algorithm (ABC) was suggested namely Adaptive Dimension Limit- Artificial Bee Colony Algorithm (ADL-ABC). To determine the optimum global path for mobile robot that satisfies the chosen criteria for shortest distance and collision–free with circular shaped static obstacles on robot environment. The cubic polynomial connects the start point to the end point through three via points used, so the generated paths are smooth and achievable by the robot. Two case studies (or scenarios) are presented in this task and comparative research (or study) is adopted between two algorithm’s results in order to evaluate the performance of the suggested algorithm. The results of the simulation showed that modified parameter (dynamic control limit) is avoiding static number of limit which excludes unnecessary Iteration, so it can find solution with minimum number of iterations and less computational time. From tables of result if there is an equal distance along the path such as in case A (14.490, 14.459) unit, there will be a reduction in time approximately to halve at percentage 5%.

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