scholarly journals A Novel Hybrid Artificial Bee Colony with Monarch Butterfly Optimization for Global Optimization Problems

2017 ◽  
Author(s):  
Waheed Ali H. M. Ghanem ◽  
Aman Jantan
Keyword(s):  
Global Optimization ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Monarch Butterfly ◽  
Monarch Butterfly Optimization ◽  
Bee Colony

scholarly journals A Novel Optimization Algorithm Combing Gbest-Guided Artificial Bee Colony Algorithm with Variable Gradients

2020 ◽  
Vol 10 (10) ◽  
pp. 3352
Author(s):  
Xiaodong Ruan ◽  
Jiaming Wang ◽  
Xu Zhang ◽  
Weiting Liu ◽  
Xin Fu
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Numerical Test ◽  
Stochastic Algorithm ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Novel Algorithm

The artificial bee colony (ABC) algorithm, which has been widely studied for years, is a stochastic algorithm for solving global optimization problems. Taking advantage of the information of a global best solution, the Gbest-guided artificial bee colony (GABC) algorithm goes further by modifying the solution search equation. However, the coefficient in its equation is based only on a numerical test and is not suitable for all problems. Therefore, we propose a novel algorithm named the Gbest-guided ABC algorithm with gradient information (GABCG) to make up for its weakness. Without coefficient factors, a new solution search equation based on variable gradients is established. Besides, the gradients are also applied to differentiate the priority of different variables and enhance the judgment of abandoned solutions. Extensive experiments are conducted on a set of benchmark functions with the GABCG algorithm. The results demonstrate that the GABCG algorithm is more effective than the traditional ABC algorithm and the GABC algorithm, especially in the latter stages of the evolution.

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.

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