Differential Artificial Bee Colony for Dynamic Environment

2011 ◽  
pp. 59-69 ◽  
Author(s):  
Syed Raziuddin ◽  
Syed Abdul Sattar ◽  
Rajya Lakshmi ◽  
Moin Parvez
Keyword(s):  
Artificial Bee Colony ◽  
Dynamic Environment ◽  
Bee Colony

Dynamic Function Optimization by Improved Artificial Bee Colony Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 3562-3566 ◽  
Author(s):  
Shuo Jiang
Keyword(s):  
Particle Swarm Optimization ◽  
Greedy Algorithm ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Dynamic Environment ◽  
Particle Swarm ◽  
Function Optimization ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Dynamic Function

In this paper, an improved artificial bee colony algorithm (IABC) for dynamic environment optimization has been proposed. As we compared the IABC with greedy algorithm (GA), Particle swarm optimization (PSO) and original artificial bee colony algorithm (ABC), the result of dynamic function optimization shows that the IABC can obtain satisfactory solutions and good tracing performance for dynamic function in time.

scholarly journals Drug Delivery Based on Swarm Microrobots

2016 ◽  
Vol 15 (02) ◽  
pp. 1650006 ◽  
Author(s):  
Anan Banharnsakun ◽  
Tiranee Achalakul ◽  
Romesh C Batra
Keyword(s):  
Drug Delivery ◽  
Collision Detection ◽  
Energy Supply ◽  
Numerical Experiments ◽  
Artificial Bee Colony ◽  
Dynamic Environment ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Controlled Motion ◽  
Boundary Distance

Advances in the development of technology have led to microrobots applications in medical fields. Drug delivery is one of these applications in which microrobots deliver a pharmaceutical compound to targeted cells. Chemotherapy and its side effects can then be minimized by this method. Two major constraints, however, must be considered: the robot’s onboard energy supply and the time needed for drug delivery. Furthermore, a microrobot must avoid biological restricted areas which we treat as obstacles in the path. The main objectives of this work were to find optimal paths to targeted cells and avoid collision with obstacles in the paths under a dynamic environment. In this study, we controlled motion of microrobots based on the concept of swarm intelligence. Artificial Bee Colony (ABC), the Best-so-far ABC, and the Particle Swarm Optimization (PSO) methods were employed to implement the collision detection and the boundary distance detection modules. Forces that drove or resisted blood flow as well as pressure in blood vessels were considered to approximate the effects of the environment on the microrobots. Numerical experiments were conducted using various obstacle environments. The results confirm that the proposed approaches were successful in avoiding obstacles and optimizing the energy consumption used to reach the target.

Self-Adaptive and Adaptive Parameter Control in Improved Artificial Bee Colony Algorithm

Informatica ◽  
2017 ◽  
Vol 28 (3) ◽  
pp. 415-438 ◽  
Author(s):  
Bekir Afşar ◽  
Doğan Aydin ◽  
Aybars Uğur ◽  
Serdar Korukoğlu
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Parameter Control ◽  
Adaptive Parameter ◽  
Bee Colony ◽  
Adaptive Parameter Control ◽  
Self Adaptive

PERFORMANCE EVALUATION OF CULTURAL ARTIFICIAL BEE COLONY AND CULTURAL ARTIFICIAL FISH SWARM ALGORITHM

2019 ◽  
Vol 6 (4) ◽  
pp. 43
Author(s):  
HADIR ADEBIYI BUSAYO ◽  
TIJANI SALAWUDEEN AHMED ◽  
FOLASHADE O. ADEBIYI RISIKAT ◽  
◽  
◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Artificial Bee Colony ◽  
Artificial Fish Swarm Algorithm ◽  
Bee Colony ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

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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