A Fuzzy-PD Controller Optimized by Artificial Bee Colony Algorithm Applied to a Small-Scale Pasteurization Plant

2020 ◽  
Author(s):  
Andres Arostegui ◽  
Diego S. Benitez ◽  
Luis Caiza
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Small Scale ◽  
Pd Controller ◽  
Bee Colony ◽  
Fuzzy Pd

scholarly journals Chaotic Artificial Bee Colony Algorithm for System Identification of a Small-Scale Unmanned Helicopter

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Li Ding ◽  
Hongtao Wu ◽  
Yu Yao
Keyword(s):  
System Identification ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
State Space Model ◽  
Global Optimum ◽  
Small Scale ◽  
Local Optimum ◽  
Unmanned Helicopter ◽  
Bee Colony ◽  
Chaotic Operator

The purpose of this paper is devoted to developing a chaotic artificial bee colony algorithm (CABC) for the system identification of a small-scale unmanned helicopter state-space model in hover condition. In order to avoid the premature of traditional artificial bee colony algorithm (ABC), which is stuck in local optimum and can not reach the global optimum, a novel chaotic operator with the characteristics of ergodicity and irregularity was introduced to enhance its performance. With input-output data collected from actual flight experiments, the identification results showed the superiority of CABC over the ABC and the genetic algorithm (GA). Simulations are presented to demonstrate the effectiveness of our proposed algorithm and the accuracy of the identified helicopter model.

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 A topographic-awareness and situational-perception based mobility model with artificial bee colony algorithm for tactical MANET

2013 ◽  
Vol 10 (2) ◽  
pp. 725-746 ◽  
Author(s):  
Jinhai Huo ◽  
Bowen Deng ◽  
Shuhang Wu ◽  
Jian Yuan ◽  
Ilsun You
Keyword(s):  
Dynamic Characteristics ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Method ◽  
Mobility Model ◽  
Path Optimization ◽  
Small Scale ◽  
Topographic Factors ◽  
Bee Colony ◽  
Random Acceleration

A topographic-awareness and situational-perception based mobility model with path optimization for tactical MANET is proposed in this paper. Firstly, a formalized process is constructed to generate a random acceleration on nodes as the disturbance caused by small-scale topographic factors in the battlefield. Secondly, a path optimization method with the artificial bee colony algorithm is introduced to mimic the trace planning when the nodes possess the terrain information of battlefield. Thirdly, a topographic-awareness based bypass strategy is proposed to simulate the action of nodes facing largescale terrain factors in the case when the terrain information is lacking. Finally, a situational-perception based avoidance strategy is built to simulate the process of cognition and decision when there is an encounter with the enemies on the march. The mobility model consists of the four parts above and imitates the dynamic characteristics of tactical nodes in military environment.

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%.

Artificial bee colony algorithm with modified search strategy

2013 ◽  
Vol 32 (12) ◽  
pp. 3326-3330
Author(s):  
Yin-xue ZHANG ◽  
Xue-min TIAN ◽  
Yu-ping CAO
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Search Strategy ◽  
Bee Colony
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