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.

scholarly journals Artificial bee colony algorithm variants on constrained optimization

2017 ◽  
Vol 7 (1) ◽  
pp. 98-111 ◽  
Author(s):  
Bahriye Basturk Akay ◽  
Dervis Karaboga
Keyword(s):  
Constrained Optimization ◽  
Optimization Algorithm ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Abc Algorithm ◽  
Constrained Problems ◽  
Bee Colony ◽  
The Stability ◽  
Feasible Space

Optimization problems are generally classified into two main groups:unconstrained and constrained. In the case of constrainedoptimization, special techniques are required to handle withconstraints and produce solutions in the feasible space. Intelligentoptimization techniques that do not make assumptions on the problemcharacteristics are preferred to produce acceptable solutions to theconstrained optimization problems. In this study, the performance ofartificial bee colony algorithm (ABC), one of the intelligentoptimization techniques, is investigated on constrained problems andthe effect of some modifications on the performance of the algorithmis examined. Different variants of the algorithm have been proposedand compared in terms of efficiency and stability. Depending on theresults, when DE operators were integrated into ABC algorithm'sonlooker phase while the employed bee phase is retained as in ABCalgorithm, an improvement in the performance was gained in terms ofthe best solution in addition to preserving the stability of thebasic ABC. The ABC algorithm is a simple optimization algorithm thatcan be used for constrained optimization without requiring a prioriknowledge.

Hybrid Artificial Bee Colony Algorithm with Differential Evolution and Free Search for Numerical Function Optimization

2016 ◽  
Vol 25 (04) ◽  
pp. 1650020 ◽  
Author(s):  
Lian Lian ◽  
Fu Zaifeng ◽  
Yang Guangfei ◽  
Huang Yi
Keyword(s):  
Differential Evolution ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Robustness Analysis ◽  
Selection Strategy ◽  
Local Optimum ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Free Search

Artificial bee colony (ABC) algorithm invented by Karaboga has been proved to be an efficient technique compared with other biological-inspired algorithms for solving numerical optimization problems. Unfortunately, convergence speed of ABC is slow when working with certain optimization problems and some complex multimodal problems. Aiming at the shortcomings, a hybrid artificial bee colony algorithm is proposed in this paper. In the hybrid ABC, an improved search operator learned from Differential Evolution (DE) is applied to enhance search process, and a not-so-good solutions selection strategy inspired by free search algorithm (FS) is introduced to avoid local optimum. Especially, a reverse selection strategy is also employed to do improvement in onlooker bee phase. In addition, chaotic systems based on the tent map are executed in population initialization and scout bee's phase. The proposed algorithm is conducted on a set of 40 optimization test functions with different mathematical characteristics. The numerical results of the data analysis, statistical analysis, robustness analysis and the comparisons with other state-of-the-art-algorithms demonstrate that the proposed hybrid ABC algorithm provides excellent convergence and global search ability.

Load Distribution Calculation of Pile Group Using Artificial Bee Colony Algorithm

2012 ◽  
Vol 204-208 ◽  
pp. 4878-4883
Author(s):  
Guo Shao Su ◽  
Kun Qian ◽  
Yan Zhang
Keyword(s):  
Optimization Algorithm ◽  
Load Distribution ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Fitness Function ◽  
Pile Group ◽  
Bee Colony ◽  
Distribution Calculation ◽  
Highly Nonlinear

Artificial bee colony algorithm (ABC) is a newly swarm intelligence optimization algorithm. It has become a powerful tool for solving highly nonlinear multi-peak optimization problems. The results of performances testing using three benchmark functions show that the numbers of evaluation for fitness function of ABC are obviously less than that using particle swarm optimization algorithm. Thus, ABC has better suitability for solving multi-modal optimization problems. Finally, ABC algorithm is applied to the load distribution calculation of pile group. The result shows that the ABC is feasible and has the advantages of high efficiency and easy implementation

An artificial bee colony algorithm with adaptive heterogeneous competition for global optimization problems

2020 ◽  
Vol 93 ◽  
pp. 106391
Author(s):  
Xianghua Chu ◽  
Fulin Cai ◽  
Da Gao ◽  
Li Li ◽  
Jianshuang Cui ◽  
...  
Keyword(s):  
Global Optimization ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Bee Colony

scholarly journals A Simple and Efficient Artificial Bee Colony Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Yunfeng Xu ◽  
Ping Fan ◽  
Ling Yuan
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Population Based ◽  
Search Pattern ◽  
Stochastic Algorithms ◽  
Search Process ◽  
Stochastic Algorithm ◽  
Bee Colony ◽  
Simulation Results

Artificial bee colony (ABC) is a new population-based stochastic algorithm which has shown good search abilities on many optimization problems. However, the original ABC shows slow convergence speed during the search process. In order to enhance the performance of ABC, this paper proposes a new artificial bee colony (NABC) algorithm, which modifies the search pattern of both employed and onlooker bees. A solution pool is constructed by storing some best solutions of the current swarm. New candidate solutions are generated by searching the neighborhood of solutions randomly chosen from the solution pool. Experiments are conducted on a set of twelve benchmark functions. Simulation results show that our approach is significantly better or at least comparable to the original ABC and seven other stochastic algorithms.

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