Selection Mechanism in Artificial Bee Colony Algorithm: A Comparative Study on Numerical Benchmark Problems

2017 ◽  
pp. 61-69 ◽  
Author(s):  
Xinyu Zhou ◽  
Hui Wang ◽  
Mingwen Wang ◽  
Jianyi Wan
Keyword(s):  
Comparative Study ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Benchmark Problems ◽  
Selection Mechanism ◽  
Bee Colony

Improving artificial Bee colony algorithm using a new neighborhood selection mechanism

2020 ◽  
Vol 527 ◽  
pp. 227-240 ◽  
Author(s):  
Hui Wang ◽  
Wenjun Wang ◽  
Songyi Xiao ◽  
Zhihua Cui ◽  
Minyang Xu ◽  
...  
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Selection Mechanism ◽  
Bee Colony ◽  
Neighborhood Selection

Differential Operators Embedded Artificial Bee Colony Algorithm

2013 ◽  
pp. 149-163
Author(s):  
Tarun Kumar Sharma ◽  
Millie Pant
Keyword(s):  
Global Optimization ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Differential Operators ◽  
Optimization Problems ◽  
Numerical Results ◽  
Benchmark Problems ◽  
Continuous Space ◽  
Abc Algorithm ◽  
Bee Colony

Artificial Bee Colony (ABC) is one of the most recent nature inspired (NIA) algorithms based on swarming metaphor. Proposed by Karaboga in 2005, ABC has proven to be a robust and efficient algorithm for solving global optimization problems over continuous space. However, it has been observed that the structure of ABC is such that it supports exploration more in comparison to exploitation. In order to maintain a balance between these two antagonist factors, this paper suggests incorporation of differential evolution (DE) operators in the structure of basic ABC algorithm. The proposed algorithm called DE-ABC is validated on a set of 10 benchmark problems and the numerical results are compared with basic DE and basic ABC algorithm. The numerical results indicate that the presence of DE operators help in a significant improvement in the performance of ABC algorithm.

Differential Operators Embedded Artificial Bee Colony Algorithm

2011 ◽  
Vol 2 (3) ◽  
pp. 1-14 ◽  
Author(s):  
Tarun Kumar Sharma ◽  
Millie Pant
Keyword(s):  
Global Optimization ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Differential Operators ◽  
Optimization Problems ◽  
Numerical Results ◽  
Benchmark Problems ◽  
Continuous Space ◽  
Abc Algorithm ◽  
Bee Colony

Artificial Bee Colony (ABC) is one of the most recent nature inspired (NIA) algorithms based on swarming metaphor. Proposed by Karaboga in 2005, ABC has proven to be a robust and efficient algorithm for solving global optimization problems over continuous space. However, it has been observed that the structure of ABC is such that it supports exploration more in comparison to exploitation. In order to maintain a balance between these two antagonist factors, this paper suggests incorporation of differential evolution (DE) operators in the structure of basic ABC algorithm. The proposed algorithm called DE-ABC is validated on a set of 10 benchmark problems and the numerical results are compared with basic DE and basic ABC algorithm. The numerical results indicate that the presence of DE operators help in a significant improvement in the performance of ABC algorithm.

An Improved Artificial Bee Colony Algorithm for Job Shop Problem

2010 ◽  
Vol 26-28 ◽  
pp. 657-660 ◽  
Author(s):  
Bao Zhen Yao ◽  
Cheng Yong Yang ◽  
Juan Juan Hu ◽  
Guo Dong Yin ◽  
Bo Yu
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Production Management ◽  
Artificial Bee Colony ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Benchmark Problems ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Bee Colony

Job shop scheduling problem (JSP) plays a significant role for production management and combinatorial optimization. An improved artificial bee colony (IABC) algorithm with mutation operation is presented to solve JSP in this paper. The results for some benchmark problems reveal that IABC is effective and efficient compared to those of other approaches. IABC seems to be a powerful tool for optimizing job shop scheduling problem.

scholarly journals A modified artificial bee colony algorithm to optimise integrated assembly sequence planning and assembly line balancing

2019 ◽  
Vol 13 (4) ◽  
pp. 5905-5921
Author(s):  
M. F. F. Ab. Rashid ◽  
N. M. Z. Nik Mohamed ◽  
A. N. Mohd Rose
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Assembly Line ◽  
Assembly Line Balancing ◽  
Assembly Sequence Planning ◽  
Line Balancing ◽  
Benchmark Problems ◽  
Assembly Sequence ◽  
Sequence Planning ◽  
Bee Colony

Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) are traditionally optimised independently. However recently, integrated ASP and ALB optimisation has become more relevant to obtain better quality solution and to reduce time to market. Despite many optimisation algorithms that were proposed to optimise this problem, the existing researches on this problem were limited to Evolutionary Algorithm (EA), Ant Colony Optimisation (ACO), and Particle Swarm Optimisation (PSO). This paper proposed a modified Artificial Bee Colony algorithm (MABC) to optimise the integrated ASP and ALB problem. The proposed algorithm adopts beewolves predatory concept from Grey Wolf Optimiser to improve the exploitation ability in Artificial Bee Colony (ABC) algorithm. The proposed MABC was tested with a set of benchmark problems. The results indicated that the MABC outperformed the comparison algorithms in 91% of the benchmark problems. Furthermore, a statistical test reported that the MABC had significant performances in 80% of the cases.

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