A Novel Artificial Bee Colony Algorithm Based on Attraction Pheromone for the Multidimensional Knapsack Problems

2011 ◽  
pp. 1-10 ◽  
Author(s):  
Hongkai Wei ◽  
Junzhong Ji ◽  
Yufang Qin ◽  
Yamin Wang ◽  
Chunnian Liu
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Knapsack Problems ◽  
Bee Colony ◽  
Multidimensional Knapsack

Quantum Artificial Bee Colony Algorithm for Knapsack Problem

2012 ◽  
Vol 605-607 ◽  
pp. 1722-1728
Author(s):  
Zhen Liu ◽  
Yun An Hu
Keyword(s):  
Evolutionary Algorithm ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Knapsack Problems ◽  
Initial Population ◽  
Local Optima ◽  
Bee Colony ◽  
Long Time ◽  
The Individual ◽  
Better Than

The traditional quantum evolutionary algorithm takes a long time to converge and can be easy trap into local optima. In order to overcome and accelerate the speed of the convergence, a new quantum evolutionary algorithm is proposed in the paper. The proposed new algorithm named discrete quantum bee colony algorithm incorporate the basic idea of the artificial bee colony algorithm. The initial population can be initialized randomly using quantum encoded and the population can be formed by there parts and every subpopulation can evolve cooperatively. In the end, the individual will rated according to the multi-granularity mechanism and also rated according to the evolution condition. Simulation results of knapsack problems show that the proposed algorithm performs better than other algorithm.

scholarly journals A Novel Artificial Bee Colony Algorithm for Function Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Song Zhang ◽  
Sanyang Liu
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Function Optimization ◽  
Experimental Results ◽  
Knapsack Problems ◽  
Search Process ◽  
Challenging Problem ◽  
Exploration And Exploitation ◽  
Bee Colony

It is known that both exploration and exploitation are important in the search equations of ABC algorithms. How to well balance the two abilities in the search process is still a challenging problem in ABC algorithms. In this paper, we propose a novel artificial bee algorithm named as “NABC,” by incorporating the information of the global best solution into the solution search equation of the onlookers stage to improve the exploitation. At the same time, we improve the search equation of the employed bees to keep the exploration. The experimental results of NABC tested on a set of 11 numerical benchmark functions show good performance and fast convergence in solving function optimization problems, compared with variant ABC, DE, and PSO algorithms. The application of NABC on solving five standard knapsack problems shows its effectiveness and practicability.

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

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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